Determinants of Infant Mortality in Gezira State, Sudan: A Survival Analysis Using Cox Proportional Hazards Model

Determinants of Infant Mortality in Gezira State, Sudan: A Survival Analysis Using Cox Proportional Hazards Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Determinants of Infant Mortality in Gezira State, Sudan: A Survival Analysis Using Cox Proportional Hazards Model Mohammed Omar Musa Mohammed, Dawit G. Ayele, Ahmed Saied, Aziza Ahmed Seneen Mohammed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6902631/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background: Infant mortality is a critical indicator of population health, with the highest rates observed in sub-Saharan Africa. This study aims to identify factors associated with infant mortality in Gezira State, Sudan. Methods: A cross-sectional survey was conducted from July to December 2021, involving 332 participants selected using simple random sampling. Data was collected through a structured questionnaire, and the Cox proportional hazards regression model was used to identify significant predictors of infant mortality. Results: Significant predictors of infant mortality included parental education, father's occupation, family income, sex of the child, dead siblings, stillbirth, delivery method, birth size, breastfeeding ability, and maternal age-related variables. Several interaction effects were also significant. Conclusion: Efforts to reduce infant mortality in Sudan should prioritize maternal education, healthcare access, and targeted interventions for high-risk groups identified in this study. Infant Mortality Rate survival analysis Cox regression Hazard Rate Gezira State Introduction Reducing neonatal and under-five mortality to 25 or fewer deaths per 1,000 live births by 2030 is a key target of the United Nations Sustainable Development Goals (SDGs). In Sudan, this target aligns with national child survival priorities. Studies have shown that under-five mortality rates in conflict-affected regions can be up to 80 times higher than in stable regions [ 1 ]. Infant mortality is widely recognized as a sensitive indicator of population health and a proxy for a country’s level of socioeconomic development [ 2 ]. Infant mortality refers to the death of a child before reaching their first birthday. The infant mortality rate (IMR) is defined as the number of deaths of infants under one year of age per 1,000 live births [ 3 ]. Globally, the average IMR is approximately 28 per 1,000 live births. However, this rate varies widely across regions due to differences in socioeconomic conditions, public health systems, and access to quality maternal and child healthcare services [ 4 ]. Sub-Saharan Africa has the highest infant mortality rate (IMR) in the world, with the World Bank reporting an average of 47 deaths per 1,000 live births in 2020. Some countries within the region report rates as high as 100 deaths per 1,000 live births [ 5 ]. In Sudan, the IMR was reported at 49 deaths per 1,000 live births in 2020, slightly higher than the sub-Saharan average. Although this represents progress from previous years (e.g., 66 deaths per 1,000 in earlier estimates), infant mortality remains a major public health concern. Numerous studies have identified a range of factors associated with infant mortality. These include maternal age, education level, household size, breastfeeding practices, birth weight, birth order, contraceptive use, father’s education, and spacing between births [ 6 ] [ 7 ] [ 8 ]. Additional studies from Ethiopia and other African countries highlight the influence of rural residence, prematurity, multiple births, and child sex on mortality risk [ 9 ] [ 10 ]. A lack of antenatal care (ANC), low maternal education, and low household income have also been shown to increase the risk of infant death significantly [ 11 ] [ 12 ]. Furthermore, cultural practices, early maternal age at first birth, and the location and quality of delivery services have been linked to infant survival outcomes [ 13 ] [ 14 ] [ 15 ]. Socioeconomic status is consistently reported as a key determinant of infant mortality. Maternal education has a significant inverse relationship with infant deaths—educated mothers are more likely to access prenatal care, adopt healthy behaviors, and seek timely medical attention. Similarly, household wealth and urban residence are associated with better child health outcomes due to improved access to healthcare and sanitation. Conversely, poverty and rural living conditions are linked to higher mortality, largely due to limited access to skilled birth attendants, vaccines, and safe drinking water [ 16 ] [ 17 ] [ 18 ]. Understanding the determinants of infant mortality is crucial for informing public health interventions. By identifying high-risk groups and contributing factors, health authorities and policymakers can design targeted strategies to reduce infant deaths and improve maternal and child health. In this context, survival analysis provides a valuable tool for examining time-to-event data and evaluating the impact of multiple covariates on infant survival. Therefore, this study aims to identify the socioeconomic and demographic determinants of infant mortality in Gezira State, Sudan, using the Cox proportional hazards model. Materials and Methods Study Design Data for the study period, which spanned from July 2021 to December 2021, was collected through a structured survey using a standardized questionnaire. The study population consisted of residents from various localities within Sudan's Gezira State. A sample of 332 participants was selected using a simple random sampling technique, appropriate due to the assumed homogeneity of the population [ 19 ]. The dependent variable was estimated based on responses obtained through this method. All experimental procedures adhered to relevant ethical guidelines and regulations. Informed consent was obtained from all participants or their legal guardians prior to participation, ensuring they were fully informed about the study’s purpose, procedures, potential risks, and benefits. Study Variables The variables were classified to align with the study's objectives and the chosen method of analysis. The dependent variable was the child's survival status, categorized as either alive or deceased. The explanatory variables were grouped into five main categories: health-related, maternal, demographic, socioeconomic, and environmental factors. This classification approach is consistent with the framework adopted in several previous studies that have examined similar determinants of child mortality [ 1 – 10 ]. Statistical Model Infant death before the age of one is the study's target outcome. One Millennium Development Goal (MDG) objective is to lower infant mortality. Examining diverse circumstances and changes in infant mortality between the wealthiest and poorest as progress is made toward the MDGs is crucial to pinpoint the socioeconomic, demographic, and geographic determinants. As a result, there is more attention on the connection between child health and socioeconomic and geographic status [ 22 – 24 ]. The study includes the following variables, as poverty is correlated with socioeconomic and geographic factors. The socioeconomic, demographic, and geographic factors, including place of residence, mother's and father's educational levels, parents' occupations, family income, and child type, were the explanatory variables. Infant children served as the unit of analysis for the study. It is possible to evaluate the impacts of the contributing factors on the hazard function using the Cox proportional hazard regression models. The Cox proportional hazard models are necessary when analyzing lifetime data. For the proportional hazard hypothesis that X gives, the continuous random predictor in the model represents an individual's lifetime (t) and the vector of explanatory variables related to (X). Thus, let x 1 , x 2 ,..., x p represent the p covariates X 1 , X 2 ,..., X p values. The following is the hazard function as per the Cox regression model: $$\:h\left(t,X\right)={h}_{0}\left(t\right){\Psi\:}\left(\text{X}\right)$$ 1 where \(\:{\Psi\:}\left(\text{X}\right)=\text{exp}\left(\sum\:_{i=1}^{p}{\beta\:}_{i}{x}_{i}\right),\beta\:=({\beta\:}_{1},{\beta\:}_{2},\:.\:.\:.\:,{\beta\:}_{p},)\) is \(\:1\:X\:P\) vector of regression parameters and \(\:{h}_{0}\left(t\right)\) is the baseline function at that time. Documentation for the Cox proportional hazard regression model can be found in various sources. The detailed explanation can be found in multiple books and articles [18–21, 25, 26 [ 11 ] [ 12 ] [ 13 ] [ 14 ] [ 15 ]]. The model can be evaluated using various model diagnosis techniques. Most diagnostic examinations rely on various residual outcomes. The application of the martingale residual is the first model diagnosis technique. This approach is useful for evaluating the quality of predictor variables and is the default method for model diagnostics. The deviation residual serves as the foundation for the other model diagnosis method. The normalized transform from the martingale residual is used in this procedure. The deviation residual diagnosis technique is employed to identify individuals who were poorly anticipated. The Cox-Snell residual is the alternative diagnostic technique. The same approach as in parametric models is used in this one. Plots of the Cox-Snell residuals' cumulative hazard function provide a useful method for determining the model's fit quality. When multiple risk factors are considered, the SAS procedure known as PROC PHREG can be used to investigate the determinants of infant mortality. The model's goodness of fit to the data must be assessed before any conclusions can be drawn. Among the methods for diagnosing a model are residuals, influence diagnostics, assessment of distributional assumptions, and outlier diagnostics [ 26 – 28 ]. It is essential to evaluate the suitability of Cox's regression model with martingale residuals. In data analysis, selecting a suitable model from among various options is crucial. Selecting the most appropriate model aids in managing confounding effects. Two phases of model building were therefore employed. One by one, each predictor variable was fitted. Significant covariates have not been removed from the model. Furthermore, a potential interaction was discovered. The primary outcome of this study was infant death before the age of one year, analyzed as a time-to-event variable. Given the relevance of infant mortality as a public health indicator and its alignment with international targets such as the Millennium Development Goals (MDGs) and Sustainable Development Goals (SDGs), it is essential to investigate the socioeconomic and demographic disparities associated with infant survival, especially in low-resource settings like Gezira State, Sudan [ 20 ] [ 21 ] [ 22 ] [ 23 ]. The study incorporated several explanatory variables, including socioeconomic, demographic, and geographic factors. These included residence (urban/rural), parental education levels, parental occupations, family income, and type of child (single/twin). These variables were selected based on their theoretical and empirical relevance to poverty and health outcomes. The analysis was conducted using the Cox proportional hazards regression model, an appropriate method for evaluating survival data with censoring. The Cox model estimates the hazard of an event (infant death) at time t , conditional on a set of covariates. For the proportional hazard hypothesis that X gives, the continuous random predictor in the model represents an individual's lifetime (t) and the vector of explanatory variables related to (X). Thus, let x 1 , x 2 ,..., x p represent the p covariates X 1 , X 2 ,..., X p values. The following is the hazard function as per the Cox regression model: $$\:h\left(t,X\right)={h}_{0}\left(t\right){\Psi\:}\left(\text{X}\right)$$ 1 where \(\:{\Psi\:}\left(\text{X}\right)=\text{exp}\left(\sum\:_{i=1}^{p}{\beta\:}_{i}{x}_{i}\right),\beta\:=({\beta\:}_{1},{\beta\:}_{2},\:.\:.\:.\:,{\beta\:}_{p},)\) is \(\:1\:X\:P\) vector of regression parameters and \(\:{h}_{0}\left(t\right)\) is the baseline function at that time. Documentation for the Cox proportional hazard regression model can be found in various sources. The detailed explanation can be found in multiple books and articles [ 11 – 26 ]. The model was evaluated using a range of diagnostic techniques, most of which are based on different types of residuals. The martingale residual was the primary diagnostic tool applied and is particularly useful for assessing the adequacy of covariates in the model. It serves as the default method for evaluating the quality of predictors. The deviance residual, derived as a normalized transformation of the martingale residual, was also used to identify individuals whose outcomes were poorly predicted by the model. Another key diagnostic tool employed was the Cox-Snell residual, which follows the same principles as in parametric survival models. By plotting the cumulative hazard function of the Cox-Snell residuals, the model's overall goodness of fit can be visually assessed. These diagnostic approaches help ensure the robustness and reliability of the fitted model. To investigate the determinants of infant mortality while accounting for multiple risk factors, the SAS procedure PROC PHREG was used to fit a Cox proportional hazards model. Prior to interpreting the results, the model’s adequacy was examined through diagnostics such as residual analysis, influence diagnostics, assessments of distributional assumptions, and detection of outliers [ 24 ] [ 25 ] [ 26 ]. Evaluating the appropriateness of the Cox regression model using martingale residuals is critical for validating the model's assumptions and performance. Model building was carried out in two stages. In the first stage, each predictor was assessed individually. All significant covariates were retained in the model to preserve explanatory power. Additionally, possible interaction effects were explored and identified, further enhancing the model’s explanatory capacity. Proper model selection is essential in minimizing confounding and improving the accuracy of inference. Results The data reveals important insights into factors influencing child survival, based on statistical tests comparing dead and alive children across different groups. First, several factors strongly affect child survival (Table 1). Higher parental education (both mother’s and father’s), father’s occupation, mother’s occupation, and the ability of the child to breast or bottle feed are all significantly associated with better child survival rates. For example, children of university-educated parents or mothers who are employed show higher survival rates. Additionally, children without genetic diseases, males, those without deceased siblings, and those with a history of no stillbirths generally survive better. Notably, all children with genetic diseases in this sample died, highlighting the severity of genetic conditions on survival. Second, some variables, such as place of delivery, family income, type of delivery, and whether the child was a single or twin, do not show statistically significant associations with survival in this dataset. Although the family income’s p-value is close to significance, it does not conclusively predict survival here. This suggests these factors may have a less direct impact, or that other unmeasured variables could play a modifying role. Table 1: Association between infant mortality and demographic variables Variables Child Survival status Dead Alive Total Chi-square Number % Number % Residence Urban 85 58.2% 61 41.8% 146 0.046 Rural 128 68.8% 58 31.2% 186 Total 213 64.2% 119 35.8% 332 Mother's educational level Illiterate/Primary 49 65.3% 26 34.7% 75 0.000 Secondary 92 76.7% 28 23.3% 120 University and above 72 52.6% 65 47.4% 137 Total 213 64.2% 119 35.8% 332 Father's educational level Illiterate/Primary 63 75.9% 20 24.1% 83 0.000 Secondary 96 70.6% 40 29.4% 136 University and above 54 47.8% 59 52.2% 113 Total 213 64.2% 119 35.8% 332 Father's Occupation Farmer 16 84.2% 3 15.8% 19 0.006 Employee 88 56.4% 68 43.6% 156 Driver 11 52.4% 10 47.6% 21 Free Businesses 98 72.1% 38 27.9% 136 Total 213 64.2% 119 35.8% 332 Place of child delivery Hospital/Health care 145 61.4% 91 38.6% 236 0.106 Home 68 70.8% 28 29.2% 96 Total 213 64.2% 119 35.8% 332 Mother Occupation Housewife 191 67.3% 93 32.7% 284 0.000 Teacher 10 76.9% 3 23.1% 13 Employee 12 34.3% 23 65.7% 35 Total 213 64.2% 119 35.8% 332 Family Income Low Income 71 74.0% 25 26.0% 96 0.053 Average Income 92 61.3% 58 38.7% 150 Highe Income 50 58.1% 36 41.9% 86 Total 213 64.2% 119 35.8% 332 Nature of the chid Single 188 62.9% 111 37.1% 299 0.143 Twins 25 75.8% 8 24.2% 33 Total 213 64.2% 119 35.8% 332 Genetic diseases Yes 26 100.0% 0 0.0% 26 0.000 No 187 61.1% 119 38.9% 306 Total 213 64.2% 119 35.8% 332 Sex of the child Male 126 72.4% 48 27.6% 174 0.001 Female 87 55.1% 71 44.9% 158 Total 213 64.2% 119 35.8% 332 Dead siblings Yes 68 87.2% 10 12.8% 78 0.000 No 145 57.1% 109 42.9% 254 Total 213 64.2% 119 35.8% 332 Stillbirth Yes 58 75.3% 19 24.7% 77 0.020 No 155 60.8% 100 39.2% 255 Total 213 64.2% 119 35.8% 332 Kind of delivery Normal/ vaginal 150 62.8% 89 37.2% 239 0.279 Elective Caesarean delivery 22 59.5% 15 40.5% 37 Emergency cesarean delivery 41 73.2% 15 26.8% 56 Total 213 64.2% 119 35.8% 332 Child Size Low than natural 25 75.8% 8 24.2% 33 0.002 Natural 171 60.6% 111 39.4% 282 Biger than natural 17 100.0% 0 0.0% 17 Total 213 64.2% 119 35.8% 332 The ability of the child to breast or bottle Yes 133 53.6% 115 46.4% 248 0.000 No 80 95.2% 4 4.8% 84 Total 213 64.2% 119 35.8% 332 To identify the factors associated with infant mortality, data from the infant mortality survey conducted in Gezira State, Sudan, between July 2021 and December 2021 were analyzed using the Cox proportional hazards regression model . The analysis was performed in SAS 9.4 using the PROC PHREG procedure, which also incorporated additional options for model diagnostics. The response variables for this analysis were age at death and infant survival status . A comprehensive set of covariates was included in the model to assess their influence on infant mortality. These predictors comprised: the infant’s ability to breastfeed or bottle-feed, size at birth, number of deceased siblings, family income, father's educational level, father's occupation, presence of genetic diseases, type of delivery, mother's occupation, mother's educational level, nature of the child at birth, place of delivery, sex of the child, history of stillbirth, mother’s age at first marriage, current age of the mother, birth order (child’s rank), and the total number of deliveries. These variables were selected based on theoretical relevance and evidence from prior studies on determinants of child survival. Figure 1 presents the residual assessment plot based on Martingale and Deviance residuals. The Martingale residuals (Y-axis) evaluate the goodness of fit for individual observations and are typically skewed and non-symmetric. In contrast, the Deviance residuals (X-axis) are symmetrized transformations of the Martingale residuals, making it easier to detect outliers. The curved relationship observed in the plot is expected, as Martingale residuals are nonlinear functions of the Deviance residuals. Most data points lie close to the fitted curve (solid line), indicating a satisfactory model fit. Notably, there are no pronounced outliers or irregular patterns suggesting model inadequacy. Overall, the residuals exhibit behavior consistent with the assumptions of a well-fitting Cox proportional hazards model. Table 2 shows the Supremum Test for Functional Form. To evaluate the adequacy of the functional form for the covariate age of mother at first marriage (Q21) in the Cox proportional hazards model, a Supremum Test based on cumulative martingale residuals was performed for each spline basis term. The test results showed that four of the six spline components—splineQ212 (p = 0.1440), splineQ213 (p = 0.1390), splineQ214 (p = 0.2000), and splineQ215 (p = 0.0980)—did not exhibit significant departures from the expected residual behavior, indicating a good fit. However, splineQ211 (p = 0.0470) and splineQ216 (p = 0.0220) were statistically significant at the 5% level, suggesting minor deviations from the assumed functional form. Despite these two components being marginally significant, the overall pattern of the residuals and the improved fit relative to the linear specification support the conclusion that the spline transformation of Q21 provides a substantially better approximation of the underlying relationship with the outcome variable. Table 2: Supremum Test for Functional Form Variable Maximum Absolute Value Replications Seed Pr > MaxAbsVal splineQ211 2.6861 1000 1275827554 0.0470 splineQ212 2.9379 1000 1275827554 0.1440 splineQ213 3.7371 1000 1275827554 0.1390 splineQ214 2.9847 1000 1275827554 0.2000 splineQ215 3.2364 1000 1275827554 0.0980 splineQ216 2.9379 1000 1275827554 0.0220 The coefficient estimates and related statistics for the covariates are shown in Table 3. The table shows that a few factors influence the risk of infant death. The findings showed that the following factors were significant at the 5% significant level: mother and father's educational level, father's occupation, family income, child sex, dead siblings, stillbirth, place of delivery, type of delivery, child size at birth, ability to breastfeed or bottle-feed, mother's age at first marriage, mother's current age, mother's number of deliveries, and length of the child's pregnancy. In Gezira State, Sudan, interaction effects have also impacted infant mortality in addition to the main effect. These consequences include the child's residence and sex, the mother's occupation, and the Table 3: Type III tests for covariates and interaction effects Effect DF Wald Chi-Square P-value Residence 1 0.474 0.4910 Mother's educational level 2 38.722 <.0001 Father's educational level 2 7.922 0.0190 Father's Occupation 3 21.485 <.0001 Mother Occupation 2 5.473 0.0648 Family Income 2 16.164 0.0003 Nature of the child 1 0.176 0.6745 Genetic diseases 1 0.000 0.9945 Sex of the child 1 17.078 <.0001 Dead siblings 1 4.903 0.0268 Still Birth 1 8.784 0.0030 Place of child delivery 1 11.717 0.0006 Kind of delivery 2 40.019 <.0001 Child Size 1 32.417 <.0001 The ability of the child to breast or bottle 1 5.355 0.0207 Age of mother at first marriage 1 28.160 <.0001 Mother current age 1 4.968 0.0258 The rank of the child 1 3.729 0.0535 Number of deliveries for mother 1 6.914 0.0086 Duration of pregnancy of the child 2 10.644 0.0049 Residence & sex of the child 1 6.6189 0.0101 Mother Occupation & sex of the child 2 9.4311 0.0090 The rank of the child & sex of the child 1 20.2717 <.0001 Residence & stillbirth 1 18.8877 <.0001 Residence & family income 2 6.6159 0.0366 Mother's Occupation & Family Income 2 16.4870 0.0003 Rank of the child & family income 2 45.7173 <.0001 The Cox regression analysis reveals several sociodemographic factors that are significantly associated with child mortality, as indicated by hazard ratios (HRs) that reflect the strength and direction of the associations. Children of mothers with illiterate or primary education had a markedly higher risk of mortality (HR = 25.17; 95% CI: 3.50–181.30), while those with secondary-educated mothers had a significantly lower risk (HR = 0.08; 95% CI: 0.02–0.27), compared to children of mothers with university-level education. Low and average family income also substantially increased the risk (HR = 5.53; 95% CI: 1.57–19.45 and HR = 6.95; 95% CI: 2.61–18.49, respectively) compared to high-income families. Father's education and occupation showed mixed effects, with illiterate/primary education associated with reduced risk (HR = 0.25; 95% CI: 0.07–0.93) and farming as an occupation having an extremely low hazard ratio (HR = 0.00; 95% CI: 0.00–0.002), which may reflect small sample size or model instability (Table 4). Birth and delivery-related characteristics showed even stronger associations. Children born at home had a much higher risk of mortality (HR = 7.33; 95% CI: 2.34–22.91) compared to those delivered in health facilities. Stillbirth history was also a strong risk factor (HR = 12.66; 95% CI: 2.36–67.85). Delivery mode significantly influenced outcomes: vaginal delivery (HR = 43.95; 95% CI: 11.60–166.45) and elective cesarean section (HR = 222.76; 95% CI: 40.54–1223.92) were both associated with much higher risks than emergency cesarean delivery. Extremely high risk was also found for children smaller than average at birth (HR = 70400.81; 95% CI: 1509.54–3,283,308), indicating a critical vulnerability, though the magnitude suggests possible data quality or modeling concerns. Other important predictors included the child’s sex, with males having lower risk (HR = 0.24; 95% CI: 0.12–0.48) compared to females, and the presence of previous sibling death, which surprisingly showed a protective association (HR = 0.06; 95% CI: 0.004–0.72). The ability to breastfeed or bottle-feed was associated with increased risk (HR = 17.94; 95% CI: 1.56–206.93), which may reflect reverse causality or measurement issues. A gestational age of eight months was linked to extremely low survival (HR = 0.00; 95% CI: 0.00–0.01). Maternal age at first marriage was protective (HR = 1.71; 95% CI: 1.40–2.08), while older current maternal age slightly reduced risk (HR = 0.89; 95% CI: 0.81–0.99). These findings emphasize that child mortality is shaped by a complex interplay of socioeconomic, biological, and healthcare factors, underscoring the need for integrated maternal and child health interventions. Table 4: The main effects of covariates for infant mortality Factors Estimate P-Value H.R. 95% Hazard Ratio Confidence Limits Place of residence(Ref. Rural) Urban 0.253 0.4910 1.287 0.627 2.643 Mother's educational level (Ref. University and above) Illiterate/Primary 3.226 0.0014 25.173 3.495 181.295 Secondary -2.520 <.0001 0.080 0.024 0.269 Father's educational level (Ref.University and above) Illiterate/Primary -1.383 0.0384 0.251 0.068 0.929 Secondary 0.162 0.6504 1.176 0.584 2.369 Father's Occupation (Ref. Free Businesses) Farmer -10.742 <.0001 0.000 0.000 0.002 Employee -0.509 0.1439 0.601 0.304 1.190 Driver -0.754 0.2519 0.471 0.130 1.709 Mother Occupation (Ref. Employee) Housewife 1.129 0.0544 3.093 0.979 9.771 Teacher -0.488 0.6058 0.614 0.096 3.917 Family Income (Ref. Higher Income) Low Income 1.709 0.0078 5.526 1.570 19.446 Average Income 1.939 0.0001 6.950 2.613 18.486 Nature of the child (Ref. Twins) Single 0.298 0.6745 1.347 0.336 5.405 Genetic diseases (Ref. No) Yes -34.737 0.9945 0.000 0.000 . Sex of the child (Ref. Female) Male -1.417 <.0001 0.242 0.124 0.475 Dead siblings (Ref. No) Yes -2.892 0.0268 0.055 0.004 0.717 Still Birth (Ref. No) Yes 2.539 0.0030 12.661 2.363 67.850 Place of child delivery (Ref. Hospital/Health care) Home 1.991 0.0006 7.326 2.342 22.911 Kind of delivery (Ref. Emergency cesarean delivery) normal/ vaginal 3.783 <.0001 43.946 11.603 166.445 Elective cesarean delivery 5.406 <.0001 222.761 40.544 1223.916 Child Size (Ref. Bigger than natural) Low than natural 11.162 <.0001 70400.81 1509.537 3283308 Ability of child to breast or bottle (Ref. No) Yes 2.887 0.0207 17.942 1.556 206.929 Duration of pregnancy of the child (Ref. 9 months) 7 months -18.950 0.9699 0.000 0.000 . 8 months -11.587 0.0011 0.000 0.000 0.010 Age of mother at first marriage 0.535 <.0001 1.708 1.402 2.082 Mother current age -0.112 0.0258 0.894 0.810 0.987 Rank of the child -1.006 0.0535 0.366 0.132 1.015 Number of deliveries for mother 1.625 0.0086 5.076 1.512 17.038 Several individual-level socioeconomic and demographic factors significantly influenced the survival status of children. Children born to mothers with no education or only primary education had significantly higher odds of mortality compared to those born to mothers with university-level education (OR = 25.17, 95% CI: 3.41–185.9). Similarly, children from households with low income (OR = 5.53, 95% CI: 1.59–19.24) or average income (OR = 6.95, 95% CI: 2.30–20.98) were at increased risk of death compared to those from high-income households. Births that occurred at home (OR = 7.33, 95% CI: 2.18–24.62), deliveries by vaginal (OR = 43.95, 95% CI: 10.93–176.71) or elective cesarean section (OR = 222.76, 95% CI: 28.70–1728.4), and children with low birth size (OR = 70,401, 95% CI: 209.8–>1 million) were associated with markedly elevated odds of death. These findings highlight the profound impact of maternal education, household income, and delivery conditions on child survival. Interaction effects revealed critical patterns of vulnerability (Table 5). Male children living in rural areas faced significantly higher odds of mortality compared to rural females (OR = 6.34, 95% CI: 1.38–29.14), suggesting gender and geographic disparities. Likewise, children from urban poor families were extremely vulnerable (OR = 100.56, 95% CI: 18.85–536.6), and rural poor children also had elevated odds (OR = 13.94, 95% CI: 1.44–135.4), emphasizing the joint impact of place of residence and economic status. Children of housewives with average income also had increased risk (OR = 15.88, 95% CI: 2.06–122.4), and male children born to housewives had unexpectedly low odds of death (OR = 0.01, 95% CI: 0.001–0.08), warranting further investigation for possible data or model specification issues. Child-level biological and demographic factors were also important. Male children had significantly lower odds of death than females (OR = 0.24, 95% CI: 0.13–0.42), although this counterintuitive finding requires cautious interpretation. Children with prior sibling deaths (OR = 0.06, 95% CI: 0.01–0.76) or born to mothers with a history of stillbirth (OR = 12.66, 95% CI: 2.30–69.86) also showed strong associations. Birth order interacted with income level: in low-income families, children of higher birth rank had lower odds of death (OR = 0.34, 95% CI: 0.20–0.57). These results emphasize the complex interplay of family, health history, and socioeconomic circumstances in shaping child mortality outcomes. Table 5: The interaction effects of covariates for infant mortality Parameter Estimate HR SE Chi-Square P-value Place of residence & sex of the child (Ref. Rural and female) Urban Male 0.843 2.324 1.098 0.590 0.4426 Female 0.428 1.534 0.979 0.191 0.6621 Rural Male 1.847 6.338 0.818 5.095 0.0240 Mother Occupation & sex of the child (Ref. Employee and female) Housewife Male -4.604 0.010 1.238 13.824 0.0002 Female -0.463 0.630 1.019 0.206 0.6500 Teacher Male -21.275 0.000 965.656 0.001 0.9824 Female 1.156 3.178 0.821 1.985 0.1588 Rank of the child & sex of the child (Ref. female) Male 1.165 3.207 0.216 29.071 <.0001 Female 0.220 1.246 0.210 1.098 0.2946 Residence & stillbirth (ref. Urban and no) Urban Yes -0.463 0.629 0.602 0.592 0.4415 Rural Yes 0.742 2.100 0.390 3.624 0.0570 Residence & family income (Ref. Urban and higher income) Urban Low Income 4.611 100.558 0.879 27.513 <.0001 Average Income 1.537 4.652 0.900 2.919 0.0876 Rural Low Income 2.635 13.942 1.272 4.291 0.0383 Average Income 1.227 3.411 0.866 2.007 0.1566 Mother Occupation & family income (Ref. Employee) Housewife Average Income 2.765 15.881 1.079 6.566 0.0104 Teacher Average Income -9.737 0.000 1138.000 0.000 0.9932 Rank of the child & family income (ref.Higher income) Low Income -1.094 0.335 0.276 15.750 <.0001 Average Income -1.026 0.358 0.229 20.007 <.0001 Figure 2 presents the Kaplan-Meier survival curve, illustrating the estimated probability of child survival as a function of age. The Y-axis shows the survival probability, while the X-axis denotes the child’s age in years. The stepwise pattern reflects the product-limit estimate of the survival distribution, where each downward step corresponds to an observed death event. Open circles indicate censored observations—children who were either still alive or lost to follow-up at the time of analysis. Survival probability remains high during the early years of life but declines progressively with increasing age. Notable drops in survival are observed after approximately ages 5 and 10, suggesting heightened mortality risk during these periods. The presence of several censored observations highlights the incomplete follow-up for some children, which is appropriately accounted for in the Kaplan-Meier estimation. Overall, this non-parametric survival estimate provides an initial descriptive insight into the mortality pattern within the cohort. It serves as a foundational step before employing multivariable models, such as Cox proportional hazards regression, to assess the effects of covariates on survival. Discussion and conclusions The likelihood of a child dying before their first birthday is highest in sub-Saharan African countries. Over 70 deaths occur for every 1000 live births, which is the infant mortality rate [ 19 ]. In low-income nations, there were 72 infant deaths for every 1000 live births in 2023. Compared to high-income countries, where the average rate is 3.5 deaths per 1000 live births, it is more than 15 times higher. It is imperative to eliminate avoidable child deaths and decrease disparities between nations so that more children can be saved [ 20 ]. Sudan's infant mortality rates have significantly decreased. As of 2023, Sudan has an infant mortality rate of 38.04 deaths per 1000 live births. This outcome indicates a decrease of 2.47% from 2022. Under-five mortalities in Eastern Africa have declined throughout the past 25 years. But, in the last ten years, there has been a more pronounced reduction[ 21 ]. The reduction of under-five mortality has been supported by a recent study conducted by Ayele, Zewotir, and Mwambi in 2015 [ 22 ]. However, despite these decreases, mortality rates are still high in Ethiopia. Therefore, survival analysis has been used to identify the socioeconomic and demographic factors influencing under-five mortality. The July 2021 to December 2021 infant mortality survey conducted in Gezira State, Sudan was used for this study. The study investigated the socioeconomic, demographic, and geographic predictors of infant mortality in Gezira State, Sudan. The critical socioeconomic, demographic, and geographic factors influencing infant mortality were determined using the Cox regression analysis method. When the relative risk values for various levels of variables are measured at different levels of socioeconomic, demographic, and geographic variables, a Cox regression model is employed. Despite declining rates in recent decades, Sub-Saharan Africa continues to have the highest rates of infant and under-five mortality worldwide. There is a well-documented decline in mortality, but identifying the socioeconomic, demographic, and geographic factors behind this decline has been challenging. The factors influencing infant mortality were identified using the Cox regression model. One useful procedure is to model the time to a given event based on the values of given covariates. For this study, 28 covariates were used to determine the infant mortality status. The death of a child (status variable), a binary variable, is the dependent variable in this study's Cox regression analysis. The time variable is the age at death. This study's covariates/independent variables comprise both continuous and categorical variables. The results showed that, under some circumstances, social, demographic, and socioeconomic factors significantly impact a child's survival until the age of one. This model was applied to infant mortality in Gezira State, Sudan. Moreover, the Cox regression model showed how crucial the information gathered from the infant mortality survey is. According to the study's findings, mothers of infant children with only a primary or illiterate education have a higher risk of their child dying than mothers with a university degree or higher. However, fathers who have primary education/illiterate had a lower chance of having a child who would die before reaching their first years compared to those respondents who have university or above education. Similarly, fathers who are farmers have a lower chance of a child dying before the first year than fathers who have free businesses. Moreover, compared to those who were larger than normal at birth, infant mortality was higher for those whose birth weight was below average. Furthermore, the risk of infant mortality increases annually with each year that a mother's age at first marriage rises. The analysis of interaction effects adds nuance to the findings. For instance, rural male infants were found to be especially vulnerable. At the same time, children of housewives with average income had a disproportionately high risk, possibly indicating a gap in maternal health knowledge or autonomy in healthcare decisions. Moreover, some combinations of parental occupation, income, and child sex yielded counterintuitive results (e.g., very low odds of death among male children of housewives), suggesting areas where further qualitative or mixed-methods research could elucidate underlying dynamics. Taken together, these results underscore the importance of multifaceted public health interventions in reducing infant mortality. Policies aimed at improving maternal education, expanding institutional delivery coverage, enhancing antenatal and postnatal care, and addressing poverty through targeted subsidies or conditional cash transfers could substantially reduce preventable deaths. Interventions should also account for geographic disparities and intersectional vulnerabilities, such as those based on sex, location, and economic status, by strengthening rural health systems and tailoring programs to local needs. The methodological strength of this study lies in its use of survival analysis, which accounts for time-to-event data and censored observations, thereby making the findings robust and statistically sound. The use of diagnostic plots and model fit assessments ensures the credibility of results. Nonetheless, limitations include reliance on self-reported data, potential misclassification bias, and the absence of health system-level variables (e.g., facility quality, distance to care). In conclusion, this research provides valuable insights into the determinants of infant mortality in Gezira State, with implications that extend across similar low-resource settings. By identifying key modifiable risk factors and high-risk subgroups, the findings offer a foundation for designing more equitable, evidence-based maternal and child health policies aligned with the Sustainable Development Goals (SDGs). Declarations Ethics approval and consent to participate The Deanship of Research at the University of Gezira reviewed and approved the research protocol, which was represented by the Faculty of Economics and Rural Development, Department of Applied Statistics and Demography, Sudan. The date is (9/2/2021, GG/KITR/GITD/21). Consent for publication Not applicable. Competing interests The authors declare that there was no competing interest. Clinical trial number Not applicable Funding This research was funded by the Deanship of the Research of Prince Sattam Bin Abdulaziz University. Author Contribution MOMM conceived of the presented idea, developed the theory, literature review and performed the computations, responsible for analysis and interpretation of data, discussed the results and contributed to the final manuscript. DGA conceived of the presented idea, developed the theory, and performed the computations, responsible for analysis and interpretation of data, discussed the results and contributed to the final manuscript. ASAR conceived of the presented idea, developed thetheory, involve in data acquisition, study design, discussed the results, and contributed tothe final manuscript. AASM involve in data acquisition, discussed the results , and contributed to thefinal manuscript. All authors reviewed and approved the final manuscript. ACKNOWLEDGEMENTS This study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2025/R/1446). Data Availability All the data and materials are available on request. References Moucheraud C, Worku A, Molla M, Finlay JE, Leaning J, Yamin AE. Consequences of maternal mortality on infant and child survival: a 25-year longitudinal analysis in Butajira Ethiopia (1987–2011). Reproductive Health, pp. 1–8, 2015. Finlay JE, Moucheraud C, Goshev S, Levira F, Mrema S, Canning D, Masanja H, Yamin AE. The Effects of Maternal Mortality on Infant and Child Survival in Rural Tanzania: A Cohort Study, Matern Child Health J , 2015. Kiross GT, Chojenta C, Barker D, Tiruye TY, Loxton D. The effect of maternal education on infant mortality in Ethiopia: A systematic review and meta-analysis. PLoS ONE, 2015. Schellekens J. Maternal education and infant mortality decline:The evidence from Indonesia, 1980–2015. Demographic Res. 2021;45(24):807–24. Tymicki K. Correlates of infant and childhood mortality: A theoretical overview and new evidence from the analysis of longitudinal data of the Bejsce (Poland) parish register reconstitution study of the 18th-20th centuries. Demographic Res. 2009;20(23):559–94. Satti MI, Ali MW, Irshad A, Shah MA. Studying infant mortality: A demographic analysis based on data mining models. Open Life Sci. 2023;18(1):1–10. Wellington O. Determinant of Infant Mortality Rate: A Panel Data Analysis of African Countries. Developing Ctry Stud. 2014;4(18):111–5. Fasina F, Oni G, Azuh D, Oduaran A. Impact of mothers’socio-demographic factorsand antenatal clinic attendance on neonatalmortality in Nigeria. Cogent Social Sci. 2020;6(1):1–15. Kumar S, Sahu D. Socio-economic, demographic and environmental factors effects on under-five mortality in Empowered Action Group States of India: an evidence from NFHS-4. Global Health. 2019;9(2):23–9. Wu H. The Effect of Maternal Education on Child Mortality in Bangladesh. Popul Dev Rev. 2022;48(2):475–503. Hosmer DW, Lemeshow S. Applied survival analysis Regression modeling of time to event data. New York: John Wlley and Sons; 1999. Kleinbaum DG, Klein M. Survival analysis A Self-Learning Text. New York: Springers; 2012. Emmert-Streib F, Dehmer M. Introduction to Survival Analysis in Practice, Machine learning and knowledge extraction , pp. 1013–38, 2019. Clark TG, Bradburn MJ, Love SB, Altman DG. Survival Analysis Part I: Basic concepts and first analyses. Br J Cance. 2003;89(2):232–8. Leung KM, Elashoff RM, Afifi AA. Censoring issues in survival analysis. Annu Rev Public Health. 1997;18:83–104. United, Nation. The Millennium Development Goals Report, 2015. Kurinczuk JJ, Hollowell J, Brocklehurst P, Gray R. Infant mortality: overview and context. Natl Perinat Epidemiol Unit Univ Oxf, 2009. Ayele DG, Zewotir TT, Mwambi H. Using Rasch Modeling to Re-Evaluate Rapid Malaria Diagnosis Test Analyses. Int J Environ Res Public Health. 2014;11(7):6681–91. WHO. Infant Mortality. World Health Organization; 2021. Bank W. Mortality rate, infant (per 1,000 live births) - Sub-Saharan Africa. The World Bank; 2021. WHO. Newborn Mortality. World Health Organization; 2022. Mulugeta SS, Muluneh MW, Belay AT, Moyehodie YA, Agegn SB, Masresha BM, Wassihun SG. Multilevel log linear model to estimate the risk factors associated with infant mortality in Ethiopia: further analysis of 2016 EDHS. BMC Pregnancy Childbirth, 22, 597, 2022. Baraki AG, Akalu TY, Wolde HF, Lakew AM, Gonete KA. Factors affecting infant mortality in the general population: evidence from the 2016 Ethiopian demographic and health survey (EDHS); a multilevel analysis. BMC Pregnancy Childbirth, 20, 299, 2020. Islam MA, bassum TrT, Moni MA. Exploring the influencing factors for infant mortality: a mixed-method study of 24 developing countries based on demographic and health survey data. Family Med Prim Care Rev. 2022;24(3):227–36. QAYYUM A. Comparative Analysis of Factors Affecting. Res J Social Sci. 2015;4(2):1–17. Shobiye DM, Omotola A, Zhao Y, Zhang J, Ekawati FM, Shobiye HO. Infant mortality and risk factors in Nigeria in 2013 – 2017: A population-level study, eClinicalMedicine , 2022. Bank W. Mortality rate, infant (per 1,000 live births). World Bank Group; 2021. Bank W. Mortality rate, infant (per 1,000 live births) - Sudan. The World Bank; 2021. Wolde HF, Gonete KA, Akalu TY, Baraki AG, Lakew AM. Factors affecting neonatal mortality in the general population: evidence from the 2016 Ethiopian Demographic and Health Survey (EDHS)—multilevel analysis. BMC Res Notes, 12, 610, 2019. Ahmed Z, Kamal A. Kamal2, Statistical Analysis of Factors Affecting Child Mortality in Pakistan. J Coll Physicians Surg Pakistan. 2016;26(6):543–4. Dadi AF. A Systematic Review and Meta-Analysis of the Effect of Short Birth Interval on Infant Mortality in Ethiopia. PLoS ONE, 10, 5, 2015. Bashir AO, Ibrahim GH, Bashier IA, Adam I. Neonatal mortality in Sudan: analysis of the Sudan household survey, 2010. BMC Public Health, 13, 287, 2013. Mugo NS, Agho KE, Zwi AB, Damundu EY, Dibley MJ. Determinants of neonatal, infant and under-five mortality in a war-affected country: analysis of the 2010 Household Health Survey in South Sudan. BMJ Glob Health, 2017. Cochran WG. Sampling Techniques. New York: John Wiely & Sons; 1997. Cochrane LJOD. SH, Parental education and child health: intracountry evidence, Health Policy Education , pp. 213 – 50, 1982. UNICEF. Infant mortality rate, 27 10 2023. [Online]. Ewbank GJ. DC, Effects of health programs on child mortality in sub-Saharan Africa. Washington, D.C.: National Academic Press,; 1993. Ayele D, Zewotir T. Comparison of under-five mortality for. BMC Public Health, 2016. Ayele D, Zewotir T, Mwambi H. Survival analysis of under-five mortality using Cox and frailty models in Ethiopia. J Health Popul Nutr, 2017. Additional Declarations No competing interests reported. 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Ayele","email":"","orcid":"","institution":"D.C. Health","correspondingAuthor":false,"prefix":"","firstName":"Dawit","middleName":"G.","lastName":"Ayele","suffix":""},{"id":488150084,"identity":"9ed5705a-aa09-423e-bef9-2458b3d1b2bf","order_by":2,"name":"Ahmed Saied","email":"","orcid":"","institution":"Prince Sattam Bin Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Saied","suffix":""},{"id":488150085,"identity":"fb6f6624-490b-496b-ba46-acb72f22ffe4","order_by":3,"name":"Aziza Ahmed Seneen Mohammed","email":"","orcid":"","institution":"University of Gezira","correspondingAuthor":false,"prefix":"","firstName":"Aziza","middleName":"Ahmed Seneen","lastName":"Mohammed","suffix":""}],"badges":[],"createdAt":"2025-06-16 07:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6902631/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6902631/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87164713,"identity":"0e7d7b69-3262-4bb1-8dc5-d5bce715b861","added_by":"auto","created_at":"2025-07-21 05:58:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3719296,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6902631/v1/4fa09d7d-1446-492e-8db7-98e2fccf5cf7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determinants of Infant Mortality in Gezira State, Sudan: A Survival Analysis Using Cox Proportional Hazards Model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eReducing neonatal and under-five mortality to 25 or fewer deaths per 1,000 live births by 2030 is a key target of the United Nations Sustainable Development Goals (SDGs). In Sudan, this target aligns with national child survival priorities. Studies have shown that under-five mortality rates in conflict-affected regions can be up to 80 times higher than in stable regions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Infant mortality is widely recognized as a sensitive indicator of population health and a proxy for a country\u0026rsquo;s level of socioeconomic development [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInfant mortality refers to the death of a child before reaching their first birthday. The infant mortality rate (IMR) is defined as the number of deaths of infants under one year of age per 1,000 live births [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Globally, the average IMR is approximately 28 per 1,000 live births. However, this rate varies widely across regions due to differences in socioeconomic conditions, public health systems, and access to quality maternal and child healthcare services [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Sub-Saharan Africa has the highest infant mortality rate (IMR) in the world, with the World Bank reporting an average of 47 deaths per 1,000 live births in 2020. Some countries within the region report rates as high as 100 deaths per 1,000 live births [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In Sudan, the IMR was reported at 49 deaths per 1,000 live births in 2020, slightly higher than the sub-Saharan average. Although this represents progress from previous years (e.g., 66 deaths per 1,000 in earlier estimates), infant mortality remains a major public health concern.\u003c/p\u003e\u003cp\u003eNumerous studies have identified a range of factors associated with infant mortality. These include maternal age, education level, household size, breastfeeding practices, birth weight, birth order, contraceptive use, father\u0026rsquo;s education, and spacing between births [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additional studies from Ethiopia and other African countries highlight the influence of rural residence, prematurity, multiple births, and child sex on mortality risk [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A lack of antenatal care (ANC), low maternal education, and low household income have also been shown to increase the risk of infant death significantly [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, cultural practices, early maternal age at first birth, and the location and quality of delivery services have been linked to infant survival outcomes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSocioeconomic status is consistently reported as a key determinant of infant mortality. Maternal education has a significant inverse relationship with infant deaths\u0026mdash;educated mothers are more likely to access prenatal care, adopt healthy behaviors, and seek timely medical attention. Similarly, household wealth and urban residence are associated with better child health outcomes due to improved access to healthcare and sanitation. Conversely, poverty and rural living conditions are linked to higher mortality, largely due to limited access to skilled birth attendants, vaccines, and safe drinking water [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUnderstanding the determinants of infant mortality is crucial for informing public health interventions. By identifying high-risk groups and contributing factors, health authorities and policymakers can design targeted strategies to reduce infant deaths and improve maternal and child health. In this context, survival analysis provides a valuable tool for examining time-to-event data and evaluating the impact of multiple covariates on infant survival.\u003c/p\u003e\u003cp\u003eTherefore, this study aims to identify the socioeconomic and demographic determinants of infant mortality in Gezira State, Sudan, using the Cox proportional hazards model.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003eData for the study period, which spanned from July 2021 to December 2021, was collected through a structured survey using a standardized questionnaire. The study population consisted of residents from various localities within Sudan's Gezira State. A sample of 332 participants was selected using a simple random sampling technique, appropriate due to the assumed homogeneity of the population [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The dependent variable was estimated based on responses obtained through this method. All experimental procedures adhered to relevant ethical guidelines and regulations. Informed consent was obtained from all participants or their legal guardians prior to participation, ensuring they were fully informed about the study\u0026rsquo;s purpose, procedures, potential risks, and benefits.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Variables\u003c/h3\u003e\n\u003cp\u003eThe variables were classified to align with the study's objectives and the chosen method of analysis. The dependent variable was the child's survival status, categorized as either alive or deceased. The explanatory variables were grouped into five main categories: health-related, maternal, demographic, socioeconomic, and environmental factors. This classification approach is consistent with the framework adopted in several previous studies that have examined similar determinants of child mortality [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eStatistical Model\u003c/h3\u003e\n\u003cp\u003eInfant death before the age of one is the study's target outcome. One Millennium Development Goal (MDG) objective is to lower infant mortality. Examining diverse circumstances and changes in infant mortality between the wealthiest and poorest as progress is made toward the MDGs is crucial to pinpoint the socioeconomic, demographic, and geographic determinants. As a result, there is more attention on the connection between child health and socioeconomic and geographic status [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The study includes the following variables, as poverty is correlated with socioeconomic and geographic factors. The socioeconomic, demographic, and geographic factors, including place of residence, mother's and father's educational levels, parents' occupations, family income, and child type, were the explanatory variables.\u003c/p\u003e\u003cp\u003eInfant children served as the unit of analysis for the study. It is possible to evaluate the impacts of the contributing factors on the hazard function using the Cox proportional hazard regression models. The Cox proportional hazard models are necessary when analyzing lifetime data. For the proportional hazard hypothesis that X gives, the continuous random predictor in the model represents an individual's lifetime (t) and the vector of explanatory variables related to (X). Thus, let x\u003csub\u003e1\u003c/sub\u003e, x\u003csub\u003e2\u003c/sub\u003e,..., x\u003csub\u003ep\u003c/sub\u003e represent the p covariates X\u003csub\u003e1\u003c/sub\u003e, X\u003csub\u003e2\u003c/sub\u003e,..., X\u003csub\u003ep\u003c/sub\u003e values. The following is the hazard function as per the Cox regression model:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:h\\left(t,X\\right)={h}_{0}\\left(t\\right){\\Psi\\:}\\left(\\text{X}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Psi\\:}\\left(\\text{X}\\right)=\\text{exp}\\left(\\sum\\:_{i=1}^{p}{\\beta\\:}_{i}{x}_{i}\\right),\\beta\\:=({\\beta\\:}_{1},{\\beta\\:}_{2},\\:.\\:.\\:.\\:,{\\beta\\:}_{p},)\\)\u003c/span\u003e\u003c/span\u003e is \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1\\:X\\:P\\)\u003c/span\u003e\u003c/span\u003e vector of regression parameters and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{0}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e is the baseline function at that time. Documentation for the Cox proportional hazard regression model can be found in various sources. The detailed explanation can be found in multiple books and articles [18\u0026ndash;21, 25, 26 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]].\u003c/p\u003e\u003cp\u003eThe model can be evaluated using various model diagnosis techniques. Most diagnostic examinations rely on various residual outcomes. The application of the martingale residual is the first model diagnosis technique. This approach is useful for evaluating the quality of predictor variables and is the default method for model diagnostics. The deviation residual serves as the foundation for the other model diagnosis method. The normalized transform from the martingale residual is used in this procedure. The deviation residual diagnosis technique is employed to identify individuals who were poorly anticipated. The Cox-Snell residual is the alternative diagnostic technique. The same approach as in parametric models is used in this one. Plots of the Cox-Snell residuals' cumulative hazard function provide a useful method for determining the model's fit quality.\u003c/p\u003e\u003cp\u003eWhen multiple risk factors are considered, the SAS procedure known as PROC PHREG can be used to investigate the determinants of infant mortality. The model's goodness of fit to the data must be assessed before any conclusions can be drawn. Among the methods for diagnosing a model are residuals, influence diagnostics, assessment of distributional assumptions, and outlier diagnostics [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. It is essential to evaluate the suitability of Cox's regression model with martingale residuals. In data analysis, selecting a suitable model from among various options is crucial. Selecting the most appropriate model aids in managing confounding effects.\u003c/p\u003e\u003cp\u003eTwo phases of model building were therefore employed. One by one, each predictor variable was fitted. Significant covariates have not been removed from the model. Furthermore, a potential interaction was discovered.\u003c/p\u003e\u003cp\u003eThe primary outcome of this study was infant death before the age of one year, analyzed as a time-to-event variable. Given the relevance of infant mortality as a public health indicator and its alignment with international targets such as the Millennium Development Goals (MDGs) and Sustainable Development Goals (SDGs), it is essential to investigate the socioeconomic and demographic disparities associated with infant survival, especially in low-resource settings like Gezira State, Sudan [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe study incorporated several explanatory variables, including socioeconomic, demographic, and geographic factors. These included residence (urban/rural), parental education levels, parental occupations, family income, and type of child (single/twin). These variables were selected based on their theoretical and empirical relevance to poverty and health outcomes.\u003c/p\u003e\u003cp\u003eThe analysis was conducted using the Cox proportional hazards regression model, an appropriate method for evaluating survival data with censoring. The Cox model estimates the hazard of an event (infant death) at time \u003cem\u003et\u003c/em\u003e, conditional on a set of covariates.\u003c/p\u003e\u003cp\u003eFor the proportional hazard hypothesis that X gives, the continuous random predictor in the model represents an individual's lifetime (t) and the vector of explanatory variables related to (X). Thus, let x\u003csub\u003e1\u003c/sub\u003e, x\u003csub\u003e2\u003c/sub\u003e,..., x\u003csub\u003ep\u003c/sub\u003e represent the p covariates X\u003csub\u003e1\u003c/sub\u003e, X\u003csub\u003e2\u003c/sub\u003e,..., X\u003csub\u003ep\u003c/sub\u003e values. The following is the hazard function as per the Cox regression model:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:h\\left(t,X\\right)={h}_{0}\\left(t\\right){\\Psi\\:}\\left(\\text{X}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Psi\\:}\\left(\\text{X}\\right)=\\text{exp}\\left(\\sum\\:_{i=1}^{p}{\\beta\\:}_{i}{x}_{i}\\right),\\beta\\:=({\\beta\\:}_{1},{\\beta\\:}_{2},\\:.\\:.\\:.\\:,{\\beta\\:}_{p},)\\)\u003c/span\u003e\u003c/span\u003e is \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1\\:X\\:P\\)\u003c/span\u003e\u003c/span\u003e vector of regression parameters and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{0}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e is the baseline function at that time. Documentation for the Cox proportional hazard regression model can be found in various sources. The detailed explanation can be found in multiple books and articles [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe model was evaluated using a range of diagnostic techniques, most of which are based on different types of residuals. The martingale residual was the primary diagnostic tool applied and is particularly useful for assessing the adequacy of covariates in the model. It serves as the default method for evaluating the quality of predictors. The deviance residual, derived as a normalized transformation of the martingale residual, was also used to identify individuals whose outcomes were poorly predicted by the model.\u003c/p\u003e\u003cp\u003eAnother key diagnostic tool employed was the Cox-Snell residual, which follows the same principles as in parametric survival models. By plotting the cumulative hazard function of the Cox-Snell residuals, the model's overall goodness of fit can be visually assessed. These diagnostic approaches help ensure the robustness and reliability of the fitted model.\u003c/p\u003e\u003cp\u003eTo investigate the determinants of infant mortality while accounting for multiple risk factors, the SAS procedure PROC PHREG was used to fit a Cox proportional hazards model. Prior to interpreting the results, the model\u0026rsquo;s adequacy was examined through diagnostics such as residual analysis, influence diagnostics, assessments of distributional assumptions, and detection of outliers [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Evaluating the appropriateness of the Cox regression model using martingale residuals is critical for validating the model's assumptions and performance.\u003c/p\u003e\u003cp\u003eModel building was carried out in two stages. In the first stage, each predictor was assessed individually. All significant covariates were retained in the model to preserve explanatory power. Additionally, possible interaction effects were explored and identified, further enhancing the model\u0026rsquo;s explanatory capacity. Proper model selection is essential in minimizing confounding and improving the accuracy of inference.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cspan\u003eThe data reveals important insights into factors influencing child survival, based on statistical tests comparing dead and alive children across different groups. First, several factors strongly affect child survival (Table 1). Higher parental education (both mother\u0026rsquo;s and father\u0026rsquo;s), father\u0026rsquo;s occupation, mother\u0026rsquo;s occupation, and the ability of the child to breast or bottle feed are all significantly associated with better child survival rates. For example, children of university-educated parents or mothers who are employed show higher survival rates. Additionally, children without genetic diseases, males, those without deceased siblings, and those with a history of no stillbirths generally survive better. Notably, all children with genetic diseases in this sample died, highlighting the severity of genetic conditions on survival.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003eSecond, some variables, such as place of delivery, family income, type of delivery, and whether the child was a single or twin, do not show statistically significant associations with survival in this dataset. Although the family income\u0026rsquo;s p-value is close to significance, it does not conclusively predict survival here. This suggests these factors may have a less direct impact, or that other unmeasured variables could play a modifying role.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan\u003eTable 1: Association between infant mortality and demographic variables\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eVariables\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eChild Survival status\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eDead\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eAlive\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eTotal\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eChi-square\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eNumber\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e%\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eNumber\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e%\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eResidence\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eUrban\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e85\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e58.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e61\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e41.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e146\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cspan\u003e0.046\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eRural\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e128\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e68.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e58\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e31.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e186\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTotal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e213\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e64.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e119\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e35.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e332\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cspan\u003eMother\u0026apos;s educational level\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eIlliterate/Primary\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e49\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e65.3%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e26\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e34.7%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e75\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e\u003cspan\u003e0.000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eSecondary\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e92\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e76.7%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e28\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e23.3%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e120\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eUniversity and above\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e72\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e52.6%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e65\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e47.4%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e137\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTotal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e213\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e64.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e119\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e35.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e332\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cspan\u003eFather\u0026apos;s educational level\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eIlliterate/Primary\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e63\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e75.9%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e20\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e24.1%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e83\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e\u003cspan\u003e0.000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eSecondary\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e70.6%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e29.4%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e136\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eUniversity and above\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e54\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e47.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e59\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e52.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e113\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTotal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e213\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e64.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e119\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e35.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e332\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e\u003cspan\u003eFather\u0026apos;s Occupation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eFarmer\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e16\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e84.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e15.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e19\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003e\u003cspan\u003e0.006\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eEmployee\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e88\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e56.4%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e68\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e43.6%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e156\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eDriver\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e11\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e52.4%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e10\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e47.6%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e21\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eFree Businesses\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e98\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e72.1%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e38\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e27.9%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e136\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTotal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e213\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e64.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e119\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e35.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e332\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003ePlace of child delivery\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eHospital/Health care\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e145\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e61.4%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e91\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e38.6%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e236\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.106\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eHome\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e68\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e70.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e28\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e29.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTotal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e213\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e64.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e119\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e35.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e332\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cspan\u003eMother Occupation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eHousewife\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e191\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e67.3%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e93\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e32.7%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e284\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e\u003cspan\u003e0.000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTeacher\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e10\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e76.9%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e23.1%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e13\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eEmployee\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e34.3%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e23\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e65.7%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e35\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTotal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e213\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e64.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e119\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e35.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e332\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cspan\u003eFamily Income\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eLow Income\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e71\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e74.0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e25\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e26.0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.053\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eAverage Income\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e92\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e61.3%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e58\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e38.7%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e150\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eHighe Income\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e58.1%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e41.9%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e86\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTotal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e213\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e64.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e119\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e35.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e332\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eNature of the chid\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eSingle\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e188\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e62.9%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e111\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e37.1%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e299\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.143\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTwins\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e25\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e75.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e24.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e33\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTotal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e213\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e64.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e119\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e35.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e332\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eGenetic diseases\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eYes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e26\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e100.0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e26\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cspan\u003e0.000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eNo\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e187\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e61.1%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e119\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e38.9%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e306\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTotal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e213\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e64.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e119\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e35.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e332\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eSex of the child\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eMale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e126\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e72.4%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e48\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e27.6%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e174\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cspan\u003e0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eFemale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e87\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e55.1%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e71\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e44.9%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e158\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTotal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e213\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e64.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e119\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e35.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e332\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eDead siblings\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eYes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e68\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e87.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e10\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e12.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e78\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cspan\u003e0.000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eNo\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e145\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e57.1%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e109\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e42.9%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e254\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTotal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e213\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e64.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e119\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e35.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e332\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eStillbirth\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eYes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e58\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e75.3%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e19\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e24.7%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e77\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cspan\u003e0.020\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eNo\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e155\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e60.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e100\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e39.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e255\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTotal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e213\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e64.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e119\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e35.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e332\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cspan\u003eKind of delivery\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eNormal/ vaginal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e150\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e62.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e89\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e37.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e239\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.279\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eElective Caesarean delivery\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e22\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e59.5%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e15\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e40.5%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e37\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eEmergency cesarean delivery\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e41\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e73.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e15\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e26.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e56\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTotal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e213\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e64.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e119\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e35.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e332\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eChild Size\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eLow than natural\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e25\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e75.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e24.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e33\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e\u003cspan\u003e0.002\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eNatural\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e171\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e60.6%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e111\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e39.4%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e282\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eBiger than natural\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e17\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e100.0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e17\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTotal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e213\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e64.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e119\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e35.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e332\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eThe ability of the child to breast or bottle\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eYes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e133\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e53.6%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e115\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e46.4%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e248\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cspan\u003e0.000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eNo\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e80\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e95.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e4.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e84\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTotal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e213\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e64.2%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e119\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e35.8%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e332\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTo identify the factors associated with infant mortality, data from the infant mortality survey conducted in Gezira State, Sudan, between July 2021 and December 2021 were analyzed using the \u003cstrong\u003eCox proportional hazards regression model\u003c/strong\u003e. The analysis was performed in \u003cstrong\u003eSAS 9.4\u003c/strong\u003e using the \u003ccode\u003e\u003cspan\u003ePROC PHREG\u003c/span\u003e\u003c/code\u003e procedure, which also incorporated additional options for model diagnostics. The response variables for this analysis were \u003cstrong\u003eage at death\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003einfant survival status\u003c/strong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comprehensive set of covariates was included in the model to assess their influence on infant mortality. These predictors comprised: the infant\u0026rsquo;s ability to breastfeed or bottle-feed, size at birth, number of deceased siblings, family income, father\u0026apos;s educational level, father\u0026apos;s occupation, presence of genetic diseases, type of delivery, mother\u0026apos;s occupation, mother\u0026apos;s educational level, nature of the child at birth, place of delivery, sex of the child, history of stillbirth, mother\u0026rsquo;s age at first marriage, current age of the mother, birth order (child\u0026rsquo;s rank), and the total number of deliveries. These variables were selected based on theoretical relevance and evidence from prior studies on determinants of child survival.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003eFigure 1 presents the residual assessment plot based on Martingale and Deviance residuals. The Martingale residuals (Y-axis) evaluate the goodness of fit for individual observations and are typically skewed and non-symmetric. In contrast, the Deviance residuals (X-axis) are symmetrized transformations of the Martingale residuals, making it easier to detect outliers. The curved relationship observed in the plot is expected, as Martingale residuals are nonlinear functions of the Deviance residuals. Most data points lie close to the fitted curve (solid line), indicating a satisfactory model fit. Notably, there are no pronounced outliers or irregular patterns suggesting model inadequacy. Overall, the residuals exhibit behavior consistent with the assumptions of a well-fitting Cox proportional hazards model.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003eTable 2 shows the Supremum Test for Functional Form. To evaluate the adequacy of the functional form for the covariate \u003cstrong\u003eage of mother at first marriage\u003c/strong\u003e (Q21) in the Cox proportional hazards model, a Supremum Test based on cumulative martingale residuals was performed for each spline basis term. The test results showed that four of the six spline components\u0026mdash;splineQ212 (p = 0.1440), splineQ213 (p = 0.1390), splineQ214 (p = 0.2000), and splineQ215 (p = 0.0980)\u0026mdash;did not exhibit significant departures from the expected residual behavior, indicating a good fit. However, splineQ211 (p = 0.0470) and splineQ216 (p = 0.0220) were statistically significant at the 5% level, suggesting minor deviations from the assumed functional form. Despite these two components being marginally significant, the overall pattern of the residuals and the improved fit relative to the linear specification support the conclusion that the spline transformation of Q21 provides a substantially better approximation of the underlying relationship with the outcome variable.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan\u003eTable 2: \u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan\u003eSupremum Test for Functional Form\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable summary=\"Procedure PHReg: Supremum Test for Functional Form\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eVariable\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eMaximum Absolute\u003cbr\u003e\u0026nbsp;Value\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eReplications\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eSeed\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003ePr \u0026gt;\u0026nbsp;\u003cbr\u003e\u0026nbsp;MaxAbsVal\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003esplineQ211\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2.6861\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1275827554\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0470\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003esplineQ212\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2.9379\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1275827554\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.1440\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003esplineQ213\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e3.7371\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1275827554\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.1390\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003esplineQ214\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2.9847\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1275827554\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.2000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003esplineQ215\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e3.2364\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1275827554\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0980\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003esplineQ216\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2.9379\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1275827554\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0220\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cspan\u003eThe coefficient estimates and related statistics for the covariates are shown in Table 3. The table shows that a few factors influence the risk of infant death. The findings showed that the following factors were significant at the 5% significant level: mother and father\u0026apos;s educational level, father\u0026apos;s occupation, family income, child sex, dead siblings, stillbirth, place of delivery, type of delivery, child size at birth, ability to breastfeed or bottle-feed, mother\u0026apos;s age at first marriage, mother\u0026apos;s current age, mother\u0026apos;s number of deliveries, and length of the child\u0026apos;s pregnancy. In Gezira State, Sudan, interaction effects have also impacted infant mortality in addition to the main effect. These consequences include the child\u0026apos;s residence and sex, the mother\u0026apos;s occupation, and the\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan\u003eTable 3: Type III tests for covariates and interaction effects\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eEffect\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eDF\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eWald Chi-Square\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eP-value\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eResidence\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.474\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.4910\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eMother\u0026apos;s educational level\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e38.722\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026lt;.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eFather\u0026apos;s educational level\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e7.922\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0190\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eFather\u0026apos;s Occupation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e21.485\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026lt;.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eMother Occupation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e5.473\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.0648\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eFamily Income\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e16.164\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0003\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eNature of the child\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.176\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.6745\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eGenetic diseases\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.9945\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eSex of the child\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e17.078\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026lt;.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eDead siblings\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e4.903\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0268\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eStill Birth\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e8.784\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0030\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003ePlace of child delivery\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e11.717\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0006\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eKind of delivery\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e40.019\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026lt;.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eChild Size\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e32.417\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026lt;.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eThe ability of the child to breast or bottle\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e5.355\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0207\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eAge of mother at first marriage\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e28.160\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026lt;.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eMother current age\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e4.968\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0258\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eThe rank of the child\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e3.729\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.0535\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eNumber of deliveries for mother\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e6.914\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0086\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eDuration of pregnancy of the child\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e10.644\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0049\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eResidence \u0026amp; sex of the child\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e6.6189\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0101\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eMother Occupation \u0026amp; sex of the child\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e9.4311\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0090\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eThe rank of the child \u0026amp; sex of the child\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e20.2717\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026lt;.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eResidence \u0026amp; stillbirth\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e18.8877\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026lt;.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eResidence \u0026amp; family income\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e6.6159\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0366\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eMother\u0026apos;s Occupation \u0026amp; Family Income\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e16.4870\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0003\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eRank of the child \u0026amp; family income\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e45.7173\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026lt;.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe Cox regression analysis reveals several sociodemographic factors that are significantly associated with child mortality, as indicated by hazard ratios (HRs) that reflect the strength and direction of the associations. Children of mothers with illiterate or primary education had a markedly higher risk of mortality (HR = 25.17; 95% CI: 3.50\u0026ndash;181.30), while those with secondary-educated mothers had a significantly lower risk (HR = 0.08; 95% CI: 0.02\u0026ndash;0.27), compared to children of mothers with university-level education. Low and average family income also substantially increased the risk (HR = 5.53; 95% CI: 1.57\u0026ndash;19.45 and HR = 6.95; 95% CI: 2.61\u0026ndash;18.49, respectively) compared to high-income families. Father\u0026apos;s education and occupation showed mixed effects, with illiterate/primary education associated with reduced risk (HR = 0.25; 95% CI: 0.07\u0026ndash;0.93) and farming as an occupation having an extremely low hazard ratio (HR = 0.00; 95% CI: 0.00\u0026ndash;0.002), which may reflect small sample size or model instability (Table 4).\u003c/p\u003e\n\u003cp\u003eBirth and delivery-related characteristics showed even stronger associations. Children born at home had a much higher risk of mortality (HR = 7.33; 95% CI: 2.34\u0026ndash;22.91) compared to those delivered in health facilities. Stillbirth history was also a strong risk factor (HR = 12.66; 95% CI: 2.36\u0026ndash;67.85). Delivery mode significantly influenced outcomes: vaginal delivery (HR = 43.95; 95% CI: 11.60\u0026ndash;166.45) and elective cesarean section (HR = 222.76; 95% CI: 40.54\u0026ndash;1223.92) were both associated with much higher risks than emergency cesarean delivery. Extremely high risk was also found for children smaller than average at birth (HR = 70400.81; 95% CI: 1509.54\u0026ndash;3,283,308), indicating a critical vulnerability, though the magnitude suggests possible data quality or modeling concerns.\u003c/p\u003e\n\u003cp\u003eOther important predictors included the child\u0026rsquo;s sex, with males having lower risk (HR = 0.24; 95% CI: 0.12\u0026ndash;0.48) compared to females, and the presence of previous sibling death, which surprisingly showed a protective association (HR = 0.06; 95% CI: 0.004\u0026ndash;0.72). The ability to breastfeed or bottle-feed was associated with increased risk (HR = 17.94; 95% CI: 1.56\u0026ndash;206.93), which may reflect reverse causality or measurement issues. A gestational age of eight months was linked to extremely low survival (HR = 0.00; 95% CI: 0.00\u0026ndash;0.01). Maternal age at first marriage was protective (HR = 1.71; 95% CI: 1.40\u0026ndash;2.08), while older current maternal age slightly reduced risk (HR = 0.89; 95% CI: 0.81\u0026ndash;0.99). These findings emphasize that child mortality is shaped by a complex interplay of socioeconomic, biological, and healthcare factors, underscoring the need for integrated maternal and child health interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan\u003eTable 4: The main effects of covariates for infant mortality\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eFactors\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eEstimate\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eP-Value\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eH.R.\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e95% Hazard Ratio\u003cbr\u003e\u0026nbsp;Confidence Limits\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003ePlace of residence(Ref. Rural)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eUrban\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.253\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.4910\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1.287\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.627\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2.643\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eMother\u0026apos;s educational level (Ref. University and above)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eIlliterate/Primary\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e3.226\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0014\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e25.173\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e3.495\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e181.295\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eSecondary\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e-2.520\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026lt;.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.080\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.024\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.269\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eFather\u0026apos;s educational level (Ref.University and above)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eIlliterate/Primary\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e-1.383\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0384\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.251\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.068\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.929\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eSecondary\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.162\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.6504\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1.176\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.584\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2.369\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cspan\u003eFather\u0026apos;s Occupation (Ref. Free Businesses)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eFarmer\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e-10.742\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026lt;.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.002\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eEmployee\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e-0.509\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.1439\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.601\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.304\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1.190\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eDriver\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e-0.754\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.2519\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.471\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.130\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1.709\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eMother Occupation (Ref. Employee)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eHousewife\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1.129\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.0544\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e3.093\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.979\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e9.771\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTeacher\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e-0.488\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.6058\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.614\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.096\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e3.917\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eFamily Income (Ref. Higher Income)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eLow Income\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1.709\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0078\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e5.526\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1.570\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e19.446\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eAverage Income\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1.939\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e6.950\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2.613\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e18.486\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eNature of the child (Ref. Twins)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eSingle\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.298\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.6745\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1.347\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.336\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e5.405\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eGenetic diseases (Ref. No)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eYes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e-34.737\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.9945\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eSex of the child (Ref. Female)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eMale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e-1.417\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026lt;.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.242\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.124\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.475\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eDead siblings (Ref. No)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eYes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e-2.892\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0268\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.055\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.004\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.717\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eStill Birth (Ref. No)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eYes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2.539\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0030\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e12.661\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2.363\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e67.850\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003ePlace of child delivery (Ref. Hospital/Health care)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eHome\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1.991\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0006\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e7.326\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2.342\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e22.911\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eKind of delivery (Ref. Emergency\u003cbr\u003e\u0026nbsp;cesarean delivery)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003enormal/ vaginal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e3.783\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026lt;.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e43.946\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e11.603\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e166.445\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eElective cesarean\u003cbr\u003e\u0026nbsp;delivery\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e5.406\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026lt;.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e222.761\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e40.544\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1223.916\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eChild Size (Ref. Bigger than natural)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eLow than natural\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e11.162\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026lt;.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e70400.81\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1509.537\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e3283308\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eAbility of child to breast or\u003cbr\u003e\u0026nbsp;bottle (Ref. No)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eYes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2.887\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0207\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e17.942\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1.556\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e206.929\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eDuration of pregnancy of the\u003cbr\u003e\u0026nbsp;child (Ref. 9 months)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e7 months\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e-18.950\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.9699\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e8 months\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e-11.587\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0011\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.010\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eAge of mother at first marriage\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.535\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026lt;.0001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1.708\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1.402\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e2.082\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eMother current age\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e-0.112\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0258\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.894\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.810\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.987\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eRank of the child\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e-1.006\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003e0.0535\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.366\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.132\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1.015\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eNumber of deliveries for mother\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1.625\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e0.0086\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e5.076\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e1.512\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e17.038\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSeveral individual-level socioeconomic and demographic factors significantly influenced the survival status of children. Children born to mothers with no education or only primary education had significantly higher odds of mortality compared to those born to mothers with university-level education (OR = 25.17, 95% CI: 3.41\u0026ndash;185.9). Similarly, children from households with low income (OR = 5.53, 95% CI: 1.59\u0026ndash;19.24) or average income (OR = 6.95, 95% CI: 2.30\u0026ndash;20.98) were at increased risk of death compared to those from high-income households. Births that occurred at home (OR = 7.33, 95% CI: 2.18\u0026ndash;24.62), deliveries by vaginal (OR = 43.95, 95% CI: 10.93\u0026ndash;176.71) or elective cesarean section (OR = 222.76, 95% CI: 28.70\u0026ndash;1728.4), and children with low birth size (OR = 70,401, 95% CI: 209.8\u0026ndash;\u0026gt;1 million) were associated with markedly elevated odds of death. These findings highlight the profound impact of maternal education, household income, and delivery conditions on child survival.\u003c/p\u003e\n\u003cp\u003eInteraction effects revealed critical patterns of vulnerability (Table 5). Male children living in rural areas faced significantly higher odds of mortality compared to rural females (OR = 6.34, 95% CI: 1.38\u0026ndash;29.14), suggesting gender and geographic disparities. Likewise, children from urban poor families were extremely vulnerable (OR = 100.56, 95% CI: 18.85\u0026ndash;536.6), and rural poor children also had elevated odds (OR = 13.94, 95% CI: 1.44\u0026ndash;135.4), emphasizing the joint impact of place of residence and economic status. Children of housewives with average income also had increased risk (OR = 15.88, 95% CI: 2.06\u0026ndash;122.4), and male children born to housewives had unexpectedly low odds of death (OR = 0.01, 95% CI: 0.001\u0026ndash;0.08), warranting further investigation for possible data or model specification issues.\u003c/p\u003e\n\u003cp\u003eChild-level biological and demographic factors were also important. Male children had significantly lower odds of death than females (OR = 0.24, 95% CI: 0.13\u0026ndash;0.42), although this counterintuitive finding requires cautious interpretation. Children with prior sibling deaths (OR = 0.06, 95% CI: 0.01\u0026ndash;0.76) or born to mothers with a history of stillbirth (OR = 12.66, 95% CI: 2.30\u0026ndash;69.86) also showed strong associations. Birth order interacted with income level: in low-income families, children of higher birth rank had lower odds of death (OR = 0.34, 95% CI: 0.20\u0026ndash;0.57). These results emphasize the complex interplay of family, health history, and socioeconomic circumstances in shaping child mortality outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan\u003eTable 5: The interaction effects of covariates for infant mortality\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eParameter\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eEstimate\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eHR\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eSE\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eChi-Square\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan\u003eP-value\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003ePlace of residence \u0026amp; sex of the child (Ref. Rural and female)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eUrban\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eMale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eFemale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eRural\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eMale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003eMother Occupation \u0026amp; sex of the child (Ref. Employee and female)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eHousewife\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eMale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-4.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eFemale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTeacher\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eMale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-21.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e965.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eFemale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1588\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003eRank of the child \u0026amp; sex of the child (Ref. female)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eMale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eFemale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2946\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003eResidence \u0026amp; stillbirth (ref. Urban and no)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eUrban\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eYes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4415\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eRural\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eYes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003eResidence \u0026amp; family income (Ref. Urban and higher income)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eUrban\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eLow Income\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eAverage Income\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eRural\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eLow Income\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eAverage Income\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003eMother Occupation \u0026amp; family income (Ref. Employee)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eHousewife\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eAverage Income\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eTeacher\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003eAverage Income\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-9.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1138.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003eRank of the child \u0026amp; family income (ref.Higher income)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eLow Income\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cspan\u003eAverage Income\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cspan\u003eFigure 2 presents the Kaplan-Meier survival curve, illustrating the estimated probability of child survival as a function of age. The Y-axis shows the survival probability, while the X-axis denotes the child\u0026rsquo;s age in years. The stepwise pattern reflects the product-limit estimate of the survival distribution, where each downward step corresponds to an observed death event. Open circles indicate censored observations\u0026mdash;children who were either still alive or lost to follow-up at the time of analysis.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003eSurvival probability remains high during the early years of life but declines progressively with increasing age. Notable drops in survival are observed after approximately ages 5 and 10, suggesting heightened mortality risk during these periods. The presence of several censored observations highlights the incomplete follow-up for some children, which is appropriately accounted for in the Kaplan-Meier estimation.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003eOverall, this non-parametric survival estimate provides an initial descriptive insight into the mortality pattern within the cohort. It serves as a foundational step before employing multivariable models, such as Cox proportional hazards regression, to assess the effects of covariates on survival.\u003c/span\u003e\u003c/p\u003e"},{"header":"Discussion and conclusions","content":"\u003cp\u003eThe likelihood of a child dying before their first birthday is highest in sub-Saharan African countries. Over 70 deaths occur for every 1000 live births, which is the infant mortality rate [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In low-income nations, there were 72 infant deaths for every 1000 live births in 2023. Compared to high-income countries, where the average rate is 3.5 deaths per 1000 live births, it is more than 15 times higher. It is imperative to eliminate avoidable child deaths and decrease disparities between nations so that more children can be saved [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSudan's infant mortality rates have significantly decreased. As of 2023, Sudan has an infant mortality rate of 38.04 deaths per 1000 live births. This outcome indicates a decrease of 2.47% from 2022.\u003c/p\u003e\u003cp\u003eUnder-five mortalities in Eastern Africa have declined throughout the past 25 years. But, in the last ten years, there has been a more pronounced reduction[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The reduction of under-five mortality has been supported by a recent study conducted by Ayele, Zewotir, and Mwambi in 2015 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, despite these decreases, mortality rates are still high in Ethiopia. Therefore, survival analysis has been used to identify the socioeconomic and demographic factors influencing under-five mortality.\u003c/p\u003e\u003cp\u003eThe July 2021 to December 2021 infant mortality survey conducted in Gezira State, Sudan was used for this study. The study investigated the socioeconomic, demographic, and geographic predictors of infant mortality in Gezira State, Sudan. The critical socioeconomic, demographic, and geographic factors influencing infant mortality were determined using the Cox regression analysis method. When the relative risk values for various levels of variables are measured at different levels of socioeconomic, demographic, and geographic variables, a Cox regression model is employed. Despite declining rates in recent decades, Sub-Saharan Africa continues to have the highest rates of infant and under-five mortality worldwide. There is a well-documented decline in mortality, but identifying the socioeconomic, demographic, and geographic factors behind this decline has been challenging.\u003c/p\u003e\u003cp\u003eThe factors influencing infant mortality were identified using the Cox regression model. One useful procedure is to model the time to a given event based on the values of given covariates. For this study, 28 covariates were used to determine the infant mortality status. The death of a child (status variable), a binary variable, is the dependent variable in this study's Cox regression analysis. The time variable is the age at death. This study's covariates/independent variables comprise both continuous and categorical variables.\u003c/p\u003e\u003cp\u003eThe results showed that, under some circumstances, social, demographic, and socioeconomic factors significantly impact a child's survival until the age of one. This model was applied to infant mortality in Gezira State, Sudan. Moreover, the Cox regression model showed how crucial the information gathered from the infant mortality survey is. According to the study's findings, mothers of infant children with only a primary or illiterate education have a higher risk of their child dying than mothers with a university degree or higher. However, fathers who have primary education/illiterate had a lower chance of having a child who would die before reaching their first years compared to those respondents who have university or above education. Similarly, fathers who are farmers have a lower chance of a child dying before the first year than fathers who have free businesses. Moreover, compared to those who were larger than normal at birth, infant mortality was higher for those whose birth weight was below average. Furthermore, the risk of infant mortality increases annually with each year that a mother's age at first marriage rises.\u003c/p\u003e\u003cp\u003eThe analysis of interaction effects adds nuance to the findings. For instance, rural male infants were found to be especially vulnerable. At the same time, children of housewives with average income had a disproportionately high risk, possibly indicating a gap in maternal health knowledge or autonomy in healthcare decisions. Moreover, some combinations of parental occupation, income, and child sex yielded counterintuitive results (e.g., very low odds of death among male children of housewives), suggesting areas where further qualitative or mixed-methods research could elucidate underlying dynamics.\u003c/p\u003e\u003cp\u003eTaken together, these results underscore the importance of multifaceted public health interventions in reducing infant mortality. Policies aimed at improving maternal education, expanding institutional delivery coverage, enhancing antenatal and postnatal care, and addressing poverty through targeted subsidies or conditional cash transfers could substantially reduce preventable deaths. Interventions should also account for geographic disparities and intersectional vulnerabilities, such as those based on sex, location, and economic status, by strengthening rural health systems and tailoring programs to local needs.\u003c/p\u003e\u003cp\u003eThe methodological strength of this study lies in its use of survival analysis, which accounts for time-to-event data and censored observations, thereby making the findings robust and statistically sound. The use of diagnostic plots and model fit assessments ensures the credibility of results. Nonetheless, limitations include reliance on self-reported data, potential misclassification bias, and the absence of health system-level variables (e.g., facility quality, distance to care).\u003c/p\u003e\u003cp\u003eIn conclusion, this research provides valuable insights into the determinants of infant mortality in Gezira State, with implications that extend across similar low-resource settings. By identifying key modifiable risk factors and high-risk subgroups, the findings offer a foundation for designing more equitable, evidence-based maternal and child health policies aligned with the Sustainable Development Goals (SDGs).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Deanship of Research at the University of Gezira reviewed and approved the research protocol, which was represented by the Faculty of Economics and Rural Development, Department of Applied Statistics and Demography, Sudan. The date is (9/2/2021, GG/KITR/GITD/21).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there was no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cb\u003eClinical trial number\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research was funded by the Deanship of the Research of Prince Sattam Bin Abdulaziz University.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eMOMM conceived of the presented idea, developed the theory, literature review and performed the computations, responsible for analysis and interpretation of data, discussed the results and contributed to the final manuscript. DGA conceived of the presented idea, developed the theory, and performed the computations, responsible for analysis and interpretation of data, discussed the results and contributed to the final manuscript. ASAR conceived of the presented idea, developed thetheory, involve in data acquisition, study design, discussed the results, and contributed tothe final manuscript. AASM involve in data acquisition, discussed the results , and contributed to thefinal manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e\n\u003cp\u003eThis study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2025/R/1446).\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eAll the data and materials are available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMoucheraud C, Worku A, Molla M, Finlay JE, Leaning J, Yamin AE. Consequences of maternal mortality on infant and child survival: a 25-year longitudinal analysis in Butajira Ethiopia (1987\u0026ndash;2011). Reproductive Health, pp. 1\u0026ndash;8, 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFinlay JE, Moucheraud C, Goshev S, Levira F, Mrema S, Canning D, Masanja H, Yamin AE. The Effects of Maternal Mortality on Infant and Child Survival in Rural Tanzania: A Cohort Study, \u003cem\u003eMatern Child Health J\u003c/em\u003e, 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKiross GT, Chojenta C, Barker D, Tiruye TY, Loxton D. The effect of maternal education on infant mortality in Ethiopia: A systematic review and meta-analysis. PLoS ONE, 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchellekens J. Maternal education and infant mortality decline:The evidence from Indonesia, 1980\u0026ndash;2015. Demographic Res. 2021;45(24):807\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTymicki K. Correlates of infant and childhood mortality: A theoretical overview and new evidence from the analysis of longitudinal data of the Bejsce (Poland) parish register reconstitution study of the 18th-20th centuries. Demographic Res. 2009;20(23):559\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSatti MI, Ali MW, Irshad A, Shah MA. Studying infant mortality: A demographic analysis based on data mining models. Open Life Sci. 2023;18(1):1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWellington O. Determinant of Infant Mortality Rate: A Panel Data Analysis of African Countries. Developing Ctry Stud. 2014;4(18):111\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFasina F, Oni G, Azuh D, Oduaran A. Impact of mothers\u0026rsquo;socio-demographic factorsand antenatal clinic attendance on neonatalmortality in Nigeria. Cogent Social Sci. 2020;6(1):1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar S, Sahu D. Socio-economic, demographic and environmental factors effects on under-five mortality in Empowered Action Group States of India: an evidence from NFHS-4. 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Washington, D.C.: National Academic Press,; 1993.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAyele D, Zewotir T. Comparison of under-five mortality for. BMC Public Health, 2016.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAyele D, Zewotir T, Mwambi H. Survival analysis of under-five mortality using Cox and frailty models in Ethiopia. J Health Popul Nutr, 2017.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-social-science-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diss","sideBox":"Learn more about [Discover Social Science and Health](https://www.springer.com/journal/44155)","snPcode":"","submissionUrl":"","title":"Discover Social Science and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Infant Mortality Rate, survival analysis, Cox regression, Hazard Rate, Gezira State","lastPublishedDoi":"10.21203/rs.3.rs-6902631/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6902631/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eInfant mortality is a critical indicator of population health, with the highest rates observed in sub-Saharan Africa. This study aims to identify factors associated with infant mortality in Gezira State, Sudan.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eA cross-sectional survey was conducted from July to December 2021, involving 332 participants selected using simple random sampling. Data was collected through a structured questionnaire, and the Cox proportional hazards regression model was used to identify significant predictors of infant mortality.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eSignificant predictors of infant mortality included parental education, father's occupation, family income, sex of the child, dead siblings, stillbirth, delivery method, birth size, breastfeeding ability, and maternal age-related variables. Several interaction effects were also significant.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eEfforts to reduce infant mortality in Sudan should prioritize maternal education, healthcare access, and targeted interventions for high-risk groups identified in this study.\u003c/p\u003e","manuscriptTitle":"Determinants of Infant Mortality in Gezira State, Sudan: A Survival Analysis Using Cox Proportional Hazards Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-21 05:42:36","doi":"10.21203/rs.3.rs-6902631/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-04T14:37:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-04T10:38:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-29T00:30:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124165889548531385518543876965685804469","date":"2025-07-28T05:49:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151278422504743237353691001258253432220","date":"2025-07-27T23:52:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336933943021257805380410203963267593259","date":"2025-07-24T18:06:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-24T17:14:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57830496663662823633859197124495014032","date":"2025-07-16T13:39:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-16T12:54:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-09T06:37:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-18T10:48:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-18T10:46:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Social Science and Health","date":"2025-06-16T07:12:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-social-science-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diss","sideBox":"Learn more about [Discover Social Science and Health](https://www.springer.com/journal/44155)","snPcode":"","submissionUrl":"","title":"Discover Social Science and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d9804df-c312-4db1-abb3-84449caa3e9a","owner":[],"postedDate":"July 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T08:08:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-21 05:42:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6902631","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6902631","identity":"rs-6902631","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}