Bayesian Modelling of the Dynamics of Preeclampsia and Blood Pressure Data of Pregnant Women

Bayesian Modelling of the Dynamics of Preeclampsia and Blood Pressure Data of Pregnant Women | 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 Article Bayesian Modelling of the Dynamics of Preeclampsia and Blood Pressure Data of Pregnant Women Merga Abdissa Aga, Ayele Taye Goshu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6405478/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Hypertensive disorders of pregnancy pose a significant risk to maternal health, leading to high rates of maternal mortality and severe morbidity. This study aimed to jointly model the longitudinal changes in systolic and diastolic blood pressures and the time to develop preeclampsia among pregnant women. A retrospective study design was employed, collecting data from the medical charts of 549 pregnant women undergoing follow-up at Bishoftu General Hospital. Various methods, including summary statistics measures, individual profile plots, and Kaplan-Meier plots, were used to explore the data. Joint Bayesian multivariate models were employed to obtain inferences of progression of the disease. Among the 549 pregnant women, 10.9% of them were diagnosed with preeclampsia. Out of those diagnosed, unfortunately, 6.7% of women diagnosed with preeclampsia did not survive. Additionally, 10% of the pregnant women underwent pregnancy termination, and among them, 45% of the terminations were due to preeclampsia. There were 17 cases (3.1%) of stillbirths among the total cases, with 13.3% attributed to preeclampsia. Furthermore, 8.38% of pregnant women experienced complications, and among them, 40% of the complications were due to developing preeclampsia. The association parameter suggests that a one-unit increase in systolic blood pressure (SBP) raises the relative risk of developing preeclampsia by approximately 6%, while a one-unit increase in diastolic blood pressure (DBP) increases the relative risk by about 7%, after adjusting for other factors. The study findings indicate that systolic blood pressure and diastolic blood pressure are related to time to developing preeclampsia during pregnancy. Higher baseline blood pressure measurement may indicate an increased time to developing preeclampsia, while lower baseline blood pressure measurement may lead to a higher survival time from preeclampsia. The factors significantly affecting the survival time of developing preeclampsia are mother’s age, weight, family history of blood pressure, previous experience of abortion and gravidity. By monitoring blood pressure status, specifically SBP and DBP, throughout pregnancy period of the mother can minimize the risk of developing preeclampsia, and hence, enhance maternal and infant health. Biological sciences/Genetics Health sciences/Health care Health sciences/Risk factors Bayesian joint model Cox model Diastolic blood pressure Linear mixed model Preeclampsia Pregnancy Systolic blood pressure Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Blood pressure refers to the force exerted by blood against wall of blood vessel as it circulates throughout the human body. Blood pressure is measured as two levels: systolic blood pressure (SBP), which represents the maximum pressure reached during each heartbeat, and diastolic blood pressure (DBP), which reflects the minimum pressure between two heartbeats. In adults, a normal resting blood pressure is approximately 120 mmHg systolic over 80 mmHg diastolic, commonly expressed as 120/80 mmHg [ 1 ]. Blood pressure readings ranging from 120/70 to 140/90 mmHg are generally considered to indicate an increased risk of high blood pressure. Hypertensive disorders of pregnancy pose a significant risk to maternal health, leading to high rates of maternal mortality and severe morbidity. Hypertension refers to high blood pressure, and globally, it complicates around 3–10% of all pregnancies, contributing to significant maternal and perinatal complications [ 2 ]. Approximately 18% of maternal deaths worldwide can be attributed to hypertensive disorders of pregnancy, resulting in an estimated 62,000–77,000 deaths annually [ 3 ]. This problem is particularly pronounced in low and middle-income countries, where access to healthcare services is limited, and the quality of maternal and neonatal care is inadequate. Hypertension significantly increases the risk of various cardiovascular conditions, including coronary heart disease, congestive heart failure, stroke, renal failure, and other arterial diseases [ 4 ]. The global burden of cardiovascular diseases has been largely influenced by lifestyle factors such as changes in diet and physical activity [ 5 ]. Literature shows that preeclampsia has already affected 2–8% of pregnant women worldwide. It is characterized by elevated blood pressure and the presence of proteinuria in the urine during the second or third trimester of pregnancy [ 6 ]. Preeclampsia typically develops in the later stages of pregnancy but can occur at any point during the second half of gestation, during labor, or up to six weeks postpartum. It is a serious and not fully understood pregnancy complication that can result in adverse health outcomes for both the mother and baby [ 7 ]. According to the World Health Organization [ 8 ] report global, preeclampsia and related conditions such as gestational hypertension are cause for the deaths of approximately 76,000 mothers and 500,000 babies worldwide each year. The majority of perinatal deaths resulting from complications of pregnancy-related hypertension occur in low and middle-income countries. A WHO report from 2015 highlights that out of the 830 daily maternal deaths, 550 occur in Sub-Saharan Africa, 180 in Southern Asia, and only 5 in developed countries. The risk of a woman in a developing country dying from maternal-related causes during their lifetime is approximately 33 times higher compared to a woman living in a developed country. Hypertension ranks as the second most common direct cause of maternal death globally, contributing to an estimated 14% of all maternal deaths, which amounts to approximately 303,000 deaths. It is worth noting that in developed countries, maternal mortality resulting from hypertension is relatively rare, accounting for 12.9% of maternal deaths. However, in developing regions, where the vast majority (99%) of maternal deaths occur, hypertension accounts for 14% of these deaths. In specific regions such as sub-Saharan Africa, including Ethiopia, the burden of maternal mortality related to hypertension is even higher, accounting for 16% of maternal deaths [ 9 ], [ 10 ]. Globally, preeclampsia affects approximately 8–10% of pregnancies, making it a relatively common condition during pregnancy. It is a leading cause of preterm delivery, which carries its own set of risks and complications for the newborn. In fact, preeclampsia accounts for around 20% of all neonatal intensive care admissions, further highlighting its impact on newborn health. In Africa and Asia, preeclampsia accounts for approximately 10% of maternal deaths [ 11 ]. In Ethiopia specifically, it has been identified as the second most common cause of maternal morbidity and the third leading cause of maternal mortality (FMOH). Preeclampsia affects around 5.47% of pregnancies in Ethiopia, indicating a significant burden within the country [ 12 ]. Severe preeclampsia or eclampsia, which represents the severe form of the condition, contributes to approximately 10% of maternal mortality cases in Ethiopia [ 13 ]. According to the Ethiopian Demographic Health Survey conducted in 2016, the maternal mortality rate in Ethiopia is estimated at 412 deaths per 100,000 live births, equating to approximately 4 maternal deaths for every 1,000 live births [ 14 ]. The Ethiopian National Emergency Obstetric and Newborn Care (EMONC) data indicates that preeclampsia contributes to complications in approximately 1 out of every 5 pregnancies and 1 out of every 16 deliveries. The preeclampsia or its complications account for 16% of direct maternal deaths in Ethiopia [ 9 ]. According to the research, the trend of maternal mortality due to preeclampsia in the country reveals an increasing over time. Indeed, the prevalence of preeclampsia can vary within and between countries, including developing nations. In developing countries, the reported prevalence of preeclampsia ranges from 1.8–16.7% [ 15 ] [ 16 ]). Similarly, in Ethiopia, the prevalence of preeclampsia varies from 1.2–19.1% [ 17 ]. Preeclampsia accounts for 16% of maternal deaths in Sub-Saharan Africa and 16.9% in Ethiopia [ 18 ]. The Ethiopian government has recognized the importance of reducing maternal and newborn morbidity and mortality and has implemented various measures to improve access to and quality of facility-based maternal and newborn services. These efforts aim to enhance the provision of emergency obstetric care, including the management of pregnancy-induced hypertension and its complications. Preeclampsia is a significant health concern during pregnancy that can have adverse effects on both the mother and the fetus in Ethiopia, and so requires detailed investigations. The aim of this study is to address the gap in research and understanding by employing the Bayesian joint modelling approach to analyze the relationship among blood pressure (SBP and DBP), and the development of preeclampsia disease for the pregnant woman. 2. Methodology of the Study This study was used the different methodology to address the study gap and achieve the objectives of the study. 2.2 Study Population and Area The study was conducted in Bishoftu’s General Hospital located in Oromia Region, Ethiopia. The study population covers of pregnant women who attended maternal delivery during the study period at Bishoftu’s General Hospital in the year 2023. Bishoftu General Hospital is located in Bishoftu town. With a population of about 2.4 million people living in three towns and five districts within the woreda, Bishoftu General Hospital serves approximately 800 to 900 patients every day. The facility has around 24 service delivery departments and approximately 203 operational patient beds. 2. 3 Research Philosophy The intellectual underpinnings of this study are grounded in post positivism. In post-positivist research, credible knowledge claims emerge as competing interpretations and possible courses of action was discussed and negotiated among the members of a community. In this situation, it would seem appropriate to share our thoughts and potential applications of the ideas with our responders rather than asking them to affirm or refute them. Post positivism is the philosophical aspect of the research dealing with the theory of verification and adopts the quantitative research approach [ 19 ]. 2.4 Research Approach The quantitative research approaches was conducted in this study in order to achieve the objectives of the study that is to model blood pressure data of pregnant women who are under prenatal follow up. On the other hand a path analysis model is finding to be suitable of explaining the association or relationship blood pressure and time to event (develop preeclampsia). 2.5 Study Design A retrospective longitudinal study was carried out in 2023, utilizing follow-up or repeated measurements of maternal delivery data at Bishoftu General Hospital. Both longitudinal and survival data were collected from the follow-up cards of pregnant women, which included relevant information for all women who attended maternal delivery during the study period, from January to December 2023. 2.5 Inclusion and Exclusion Criteria Inclusion Criteria Pregnant women who attended maternal delivery service and have at least two follow up at the Bishoftu’s General Hospital were included in the study or sampling procedure. Exclusion Criteria The women who have incomplete inf ormation regarding to the study variables on the registration card were not eligible for the study. Pregnant women who discontinued the ANC services were not included in this study. 2.6 Sampling Procedure The researchers adopted a systematic random sampling technique to select a representative sample from medical charts containing the names and identification numbers of pregnant women. During the study period, a total of 5989 women attended ANC (Antenatal Care) follow-ups. The sampling interval (K) was determined by dividing the total number of women receiving ANC follow-up at the hospital during the study period by the required sample size. As a result, every 11th pregnant woman attending antenatal care follow-up was included in the evaluation. 2.7 Sample Size Determination The sample size formula presented in this section is based on the three criteria, which minimize overfitting whilst ensuring precise estimates of overall outcome risk [ 20 ]. The first criterion, aims to ensure the optimism of predictor effect estimates is small, as defined by a global shrinkage factor of ≥ 0.9. The second criterion extends this idea to ensure the optimism is small on the R 2 Nagelkerke scale, such that there is a difference of ≤ 5% in the apparent and adjusted percentage of variation explained by the model. Lastly, the third criterion ensures the sample size will precisely estimate the overall outcome risk, which is fundamental. Hence, the minimum sample size required for this study is calculated as follows: $$\:n=\frac{p}{{(S}_{VH}-1)\text{log}(1-\frac{{{\:R}_{cs\:adj}}^{2}}{{S}_{VH}})}$$ where \(\:p\) is the numbers of candidate predictor’s variables, S VH is Van Houwelingen’s global shrinkage factor (at least 0.9) and R 2 CS_adj is the adjusted Cox-Snell R 2 estimate of the model's (at least 0.1) based on the previous studies in the same setting and population [ 20 ]. Hence, in this study we have 10 numbers of candidate predictor’s variables and no existing studies or information to identify a sensible value of the expected Cox-Snell R 2 . According to [ 20 ], in the absence of any other information, we suggest that sample sizes be derived assuming the value of R 2 CS_adj corresponds to an R 2 Nagelkerke of 0.15. Therefore, \(\:n\) is computed as: $$\:\:\:n=\frac{p}{{(S}_{VH}-1)\text{log}(1-\frac{{{\:R}_{cs\:adj}}^{2}}{{S}_{VH}})}=\frac{10}{(0.9-1)\text{log}(1-\frac{0.15}{0.9})}=\frac{10}{\left(0.01823\right)}=548.5463\:=\:549$$ The data were collected using specific checklist from Bishoftu’s General Hospital during January to December 2023. Variables of the Study Response variables : This study considered three outcome variables: longitudinal measurements of Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) in millimeters of mercury (mmHg), as well as the survival outcome, defined as the time to onset preeclampsia from antenatal care follow-up among pregnant women at the hospital. Covariates The study considered factors such as maternal age, Weight in kg, parity (nulliparous, multipara), gravidity(prim gravida, multigravida), family history blood pressure (yes, no), previous history of preeclampsia (yes, no), previous abortion (yes, no), gestational age, number of antenatal care visit (less than 4, 4 and more times), and pregnant multiplicity (single, twin). 2.9 Statistical Models and Analyses To achieve the research objectives, joint models integrating longitudinal and survival data were employed. The analysis included exploratory data analysis, linear mixed-effects models (LME) for longitudinal data, Cox proportional hazards models for survival data, and joint models combining both. Linear Mixed Effects Models LME models account for within-subject correlations and between-subject variability, making them suitable for longitudinal data. The model is expressed as [ 21 ]: $$\:{Y}_{ij}\left(t\right)\:=\:{{x}^{{\prime\:}}}_{ij}\left(t\right){\beta\:}_{j}\:+\:{{z}^{{\prime\:}}\:}_{ij}\left(t\right){b}_{ij}\:+\:{\epsilon\:}_{i}\left(t\right),$$ 1 Where \(\:{\epsilon\:}_{ij}\left(t\right)\sim\:\:N\left(0,\:{\sigma\:}^{2}\right),\) \(\:{b}_{ij}\) ∼ N (0, D ) Consider a study with 𝑛 randomly selected and independent subjects, and m i represents the number of observations for individual i, i = 1, 2 …….n. Let \(\:{y}_{ij}\) be the mi vector of responses for individual i for jth response variable [ 21 ]. Where β is a vector of fixed effects of X ij (t) time-varying covariate matrix, b ij is a vector of random slope effects of Z ij (t) time-varying covariate matrix. \(\:{\epsilon\:}_{i}\left(t\right)\) is normally distributed with variance \(\:{\sigma\:}_{\epsilon\:}^{2}\) . Survival Models Survival analysis is a statistical approach used to analyze time-to-event data, where the primary focus is on the time until a specific event of interest occurs. This response variable often termed failure time, survival time, or event time is typically continuous but may be incompletely observed for some individuals. Such incomplete observations, known as censored data, occur when the exact event time is not recorded but is known to exceed a specific value 𝑡. Let \(\:T\) be a non-negative random variable representing survival times. Lifetime distributions can be defined using any of the following key measures[ 21 ] [ 22 ] [ 23 ]: The observed data are denoted by (T, δ), where T = min (X, C) is the follow-up time, and $$\:\delta\:\:=\:I(X\le\:C)\:is\:an\:indicator\:for\:status\:at\:the\:end\:of\:follow-up,$$ $$\:\delta\:\:=\:I\left(X\le\:C\right)\:=\:\:\:\:\:\:\:\:\:\:\:\:0\:if\:\:\:X\:>\:C\:\left(observed\:censoring\right)$$ $$\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:1\:if\:\:\:X\:\le\:\:C\:\left(observed\:failure\right)$$ Right censoring arises due to various factors, including the absence of the event of interest before the study concludes, participants being lost to follow-up during the study, or individuals withdrawing for other reasons, which may involve competing risks. In such scenarios, the observed survival time under right censoring is shorter than the true survival time [ 24 ]. The Kaplan-Meier Estimates of Survival Function The Kaplan-Meier (K-M) estimator is widely used for individual-level survival data analysis. Unlike the life table method, which is applied to grouped data, the K-M estimator utilizes individual event times, providing greater precision. Here, we will focus on describing the K-M estimator. Assume that 𝑟 individuals experience failures in a group of individuals. \(\:Let\:0\:<\:t\left(1\right)\:<\:\dots\:\dots\:<\:\:t\left(r\right)\:<\:1\) represents, the observed ordered death times. Let r j denote the size of the risk set at time t (j), where the risk set refers to the collection of individuals who are alive and uncensored just before t (j) . Let d j represent the number of observed deaths at time t (j), j = 1…. r. The Kaplan-Meier (K-M) estimator of the survival function S (t) is defined by: $$\:\widehat{s\left(t\right)}=\:\prod\:_{j:t{t}_{\left(j\right)<t}}(1-\frac{{d}_{j}}{{r}_{j}})$$ 2 This estimator is a step function, meaning that it only changes values at the time of each death [ 25 ][ 26 ]. Parametric Distributions for Survival Analysis For modeling the time until an event occurs, survival analysis frequently utilizes probability density functions (PDFs) such as Weibull, Exponential, Log-Logistic, Log-Normal, Gamma, and Gompertz distributions [ 21 ]. These distributions are frequently employed to model the time until an event occurs, each having distinct characteristics that make them suitable for different types of survival data [ 21 ][ 22 ]. Cox Regression Model The Cox Proportional Hazards (PH) model is widely used to assess the effects of covariates on hazard rates without requiring a specific form for the baseline hazard function [ 21 ][ 27 ] [ 28 ] [ 22 ]. $$\:h\left(t|\varvec{x}\right)={h}_{0}\left(t\right)\:\text{e}\text{x}\text{p}\left({\varvec{w}}^{{\prime\:}}\varvec{\gamma\:}\right)$$ 3 In this equation, \(\:{h}_{0}\left(t\right)\) represents the baseline hazard function, \(\:w\) is a vector of covariates, and \(\:\varvec{\gamma\:}\) is the corresponding vector of regression coefficients. \(\:{h}_{0}\left(t\right)\) , can either have a specified parametric form or be left as an arbitrary nonnegative function. The semi-parametric Cox PH model is assumes an arbitrary (unspecified) nonnegative function, while parametric PH models \(\:\:{h}_{0}\left(t\right)\) whereas parametric PH models [ 22 ] assume a parametric form for \(\:{h}_{0}\left(t\right)\) such as Weibull or exponential[ 21 ]. It is important to note that the assumption of proportional hazards is strong, and this assumption should be thoroughly checked [ 21 ]. When this assumption is violated, the Accelerated Failure Time (AFT) family offers an attractive alternative to PH models [ 29 ][ 30 ]. 2.9.5 Joint Model The standard approach in joint modeling involves two sub-models: a longitudinal model that handles measurement errors and missing data to estimate the true values of the time-dependent covariate, and a time-to-event model that uses these estimated values to quantify the relationship between the covariate and the time until the event occurs [ 21 ][ 31 ]. The key idea of joint modeling is to link the time-to-event model with the longitudinal model. In this approach, a linear mixed-effects model is typically used for the time-dependent covariate, representing the longitudinal response. This model accounts for random effects that capture subject-specific variations and incorporates measurement errors and missing data [ 32 ]. On the other hand, a proportional hazards (PH) model is employed to analyze the association between the covariate and the time to the event [ 33 ]. $$\:{h}_{i}\left(t\:\right|{M}_{i}\left(t\right))\:={\:h}_{0}(t\left)\:exp\right\{{\gamma\:{\prime\:}w}_{i}\:+\:\sum\:_{j=1}^{J}{{\alpha\:}_{j}m}_{ij}\left(t\right)\},$$ 4 $$\:{y}_{i}\left(t\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:=\:{m}_{i}\left(t\right)+\:{\epsilon\:}_{i}\left(t\right)\:\:=\:{x{\prime\:}}_{i}\left(t\right)\beta\:\:+\:{z{\prime\:}}_{i}\:\left(t\right){b}_{i}\:+\:{\epsilon\:}_{i}\left(t\right),$$ Where \(\:\:\:\:\:{M}_{i}\left(t\right)\:=\:\left\{{m}_{i}\right(s),\:0\:\le\:\:s\:<\:t\}\) longitudinal history α -quantifies the association between the time-varying covariate and the risk of an event and w i baseline covariates. \(\:\gamma\:\) -represents the vector of coefficients associated with covariates \(\:{w}_{i}\:\) in the survival sub model. J -represents the total number of longitudinal measurements included in the model. \(\:\:{ϵ\:}_{i}\left(t\right)\) -represents the error term or random effects associated with the longitudinal sub model. This joint modeling framework combines these two components to capture the association between the longitudinal measurements and the survival time of outcome. Maximum Likelihood Estimation The maximum likelihood method is a commonly used approach for statistical inference [ 34 ][ 35 ]. Maximum likelihood estimation (MLE) has been the dominant method for parameter estimation in joint modeling, as it enables the simultaneous estimation of parameters from both the longitudinal and survival models. Due to the complexity of joint models, computational methods, such as Expectation-Maximization (EM) and Gauss-Hermite integration, have been developed to estimate parameters in the presence of unobserved data [ 36 ]. Bayesian Inference The Bayesian approach differs from traditional frequentist approaches primarily in how probability is explained [ 37 ]. Bayesian methods offer an effective approach for dealing with limited data and model uncertainty. In Bayesian analysis, prior distributions represent prior knowledge about parameters before observing data. These priors are updated to produce posterior distributions using Bayes' theorem. Sampling techniques, such as Markov Chain Monte Carlo (MCMC), are used to approximate the posterior distribution and provide estimates of the parameters and their uncertainties. This method is particularly useful in complex models where traditional estimation methods may be inadequate [ 38 ][ 39 ][ 40 ] [ 41 ]. To work with the posterior distribution, one common approach is to employ sampling techniques such as Markov chain Monte Carlo (MCMC) methods. By sampling values from the posterior distribution, we can approximate its behavior and compute sample statistics. The posterior density provides information about the parameter's behavior across a range of values within the parameter space. By generating a sample from the posterior distribution, we obtain a set of parameter values that reflect its uncertainty and variation. With this sample of values, we can estimate various quantities of interest, such as the posterior mean or median, standard deviation, and credible intervals. These estimates provide insights into the central tendency, dispersion, and uncertainty bounds of the parameter values based on the observed data. In Bayesian inference, Monte Carlo samples (generated by MCMC) drawn from the posterior distribution are used. MCMC algorithms such as the Gibbs sampler [ 42 ] are employed to obtain these samples[ 21 ]. Gibbs sampling has emerged as a valuable tool in Bayesian inference, especially when direct sampling from the joint distribution is challenging. Its versatility in handling complex models and providing approximate samples from the posterior distribution has made it widely embraced in diverse domains such as statistics, machine learning, and image analysis [ 43 ] [ 44 ]. Ethics approval and consent to participate Ethical approval for this study was granted by the Kotebe University of Education Ethical Review Committee. A formal approval letter was provided to Bishoftu General Hospital, where the hospital's review committee granted permission to access maternal delivery follow-up data. As this study involved retrospective data collection, the requirement for informed consent was waived by the ethical review committee. All methods were performed in accordance with the relevant guidelines and regulations. 3. Results and Discussion In this section, descriptive and inferential statistics results are presented as follows: 3.1 Descriptive Results A total of 549 pregnant women who attended maternal delivery during the study period were included in the study. All participants were followed for one year, from January 1, 2023, to December 31, 2023, with each woman having at least two follow-up visits. A total of 2,760 systolic blood pressure (SBP) and diastolic blood pressure (DBP) measurements were recorded. Antenatal care was provided to the women throughout their pregnancies. The dataset collected is unbalanced, as not all subjects have the same number of observations. In addition to the longitudinal outcomes, several baseline covariates were recorded, and survival data is also available. Table 1: Cross tabulation of preeclampsia status and other variables Variable Preeclampsia Outcome Censored (%) Event (%) Total (%) Mortality No 484 (88.2) 56 (10.2) 540 (98.4) Yes 5 (0.9) 4 (0.7) 9 (1.6) Total 489 (89.1) 60 (10.9) 549 (100) Termination of pregnancy Yes 20 (3.7) 33 (6) 53 (9.7) No 469 (85.4) 27 (4.9) 496 (90.3) Total 489 (89.1) 60 (10.9) 549 (100) Birth Outcome Still Birth 9 (1.6) 8 (1.46) 17 (3.1) Alive 480 (87.4) 52 (9.5) 532 (96.9) Total 489 (89.1) 60 (10.9) 549 (100) Develop complications No 467 (85) 36 (6.6) 503 (91.6) Yes 22 (4) 24 (4.4) 46 (8.4) Total 489 (89.1) 60 (10.9) 549 (100) History preeclampsia No 472 (86.0) 40 (7.28) 512 (93.3) Yes 17 (3.1) 20 (3.6) 37 (6.7) Total 489 (89.1) 60 (10.9) 549 (100) Previous abortion No 475 (86.5) 39 (7.1) 514 (93.6) Yes 14 (2.6) 21 (3.8) 35 (6.4) Total 489 (89.1) 60 (10.9) 549 (100) Family history of BP No 472 (85.97) 32 (5.8) 504 (91.8) Yes 17 (3.1) 28 (5.1) 45 (8.2) Total 489 (89.1) 60 (10.9) 549 (100) Parity Nullipara 189 (34.4) 45 (8.2) 234 (42.6) 1 para 300 (54.6) 15 (2.7) 315 (57.4) Total 489 (89.1) 60 (10.9) 549 (100) Gravidity prim gravida 174 (31.7) 38 (6.9) 212 (38.6) Multigravida 315 (57.4) 22 (4) 337 (61.4) Total 489 (89.1) 60 (10.9) 549 (100) Age grouped < 30 years 6 (1.1) 2 (0.4) 8 (1.5) 30 years 483 (87.98) 58 (10.5) 541 (98.5) Total 489 (89.1) 60 (10.9) 549 (100) Pregnant multiplicity Singleton 479 (87.2) 57 (10.4) 536 (97.6) Twin 10 (1.8) 3 (0.5) 13 (2.4) Total 489 (89.1) 60 (10.9) 549 (100) Number of ANC visit <4 6 (1.1) 2 (0.4) 8 (1.5) 4 483 (87.98) 58 (10.5) 541 ( 98.5) Total 489 (89.1) 60 (10.9) 549 (100) Mode of Delivery Normal delivery 380 (69.2) 46 (8.4) 426 (77.6) Vacuum /Forceps 12 (2.2) 3 (0.5) 15 (2.7) C-section 97 (17.67) 11 (2) 108 (19.7) Total 489 (89.1) 60 (10.9) 549 (100) Mode of onset labour Spontaneous 283 (51.6) 13 (2.4) 296 (53.9) Induced 206 (37.5) 47 (8.5) 253 (46.1) Total 489 (89.1) 60 (10.9) 549 (100) As it shown in Table 1, among a total of 549 cases, 10.9% (60 cases) were diagnosed with preeclampsia. Out of those diagnosed with preeclampsia, 93.3% (56 cases) did not result in death, while 6.7% (4 cases) unfortunately resulted in fatalities. Regarding the termination of pregnancy, 10% (53 cases) underwent this procedure. Among the cases with preeclampsia, 55% (33 cases) opted for termination, while 45% (27 cases) did not pursue it. There were 17 cases (3.1%) of stillbirths among the total cases. Among those diagnosed with preeclampsia, 86.7% (52 cases) resulted in live births, while 13.3% (8 cases) unfortunately ended in stillbirths, indicating that preeclampsia accounted for 13.3% of the recorded stillbirths. Furthermore, 8.38% (46 cases) of pregnant women experienced complications. Among the cases with preeclampsia, 60% (36 cases) did not develop complications, while 40% (24 cases) did face additional complications. The result in Table 1 shows that History of Preeclampsia: Among the cases, 6.74% (37 cases) had a history of preeclampsia. Out of those with preeclampsia, 66.7% (40 cases) did not have a previous history of preeclampsia, while 33.3% (20 cases) had a history of preeclampsia. Previous Abortion: Among all the cases, 6.37% (35 cases) had a history of previous abortion. Within the preeclampsia cases, 65% (39 cases) did not have a history of previous abortion, while 35% (21 cases) had experienced previous abortions. Family History of High Blood Pressure: A total of 8.2% (45 cases) had a family history of high blood pressure. Among the cases with preeclampsia, 53.3% (32 cases) did not have a family history of high blood pressure, while 46.7% (28 cases) had a family history of high blood pressure. Parity (Number of Pregnancies): Among all the women, 42.6% (234 cases) were nulliparous (had no previous pregnancies), while 57.4% (315 cases) were multiparous (had previous pregnancies). In cases with preeclampsia, 75% (45 cases) were nulliparous, while 25% (15 cases) were multiparous. Gravidity (Number of Times Pregnant): Among all the women, 38.6% (212 cases) were prim-gravida (first-time pregnant), while 61.4% (337 cases) were multigravida (had been pregnant before). Among the cases with preeclampsia, 63.3% (38 cases) were prim-gravida, while 36.7% (22 cases) were multigravida. Regarding age: Among the cases with preeclampsia, 3.3% (2 women) were below 30 years of age, while 96.7% (58 women) were 30 years or older. Singleton vs. Twin Pregnancies: The majority of women, 97.6% (536 cases), had singleton pregnancies, while 2.4% (13 cases) had twin pregnancies. Among the cases with preeclampsia, 95% (57 cases) had singleton pregnancies, while 5% (3 cases) had twin pregnancies. As it shown in Table 1 antenatal care (ANC) follow-up: The majority of women, 98.5% (541 cases), had four or more ANC follow-ups, indicating regular prenatal care. Only 1.5% (8 cases) had fewer than four ANC follow-ups. Among the cases with preeclampsia, 96.7% (58 cases) had four or more ANC follow-ups, while 3.3% (2 cases) had fewer than four ANC follow-ups. Mode of Delivery: The mode of delivery for pregnant women varied. Spontaneous normal deliveries accounted for the majority, with 77.6% (426 cases). Instrumental deliveries (vacuum/forceps) were relatively less common, representing 2.7% (15 cases), while cesarean section deliveries accounted for 19.67% (108 cases). Among the cases with preeclampsia, the distribution of delivery modes was as follows: 76.7% (46 cases) had spontaneous normal deliveries, 5% (3 cases) had vacuum-assisted deliveries, and 18.3% (11 cases) underwent Cesarean sections. Regarding the mode of onset of labor: Among all the women, 53.9% (296 cases) experienced spontaneous onset of labor, where labor began naturally without any external interventions. A majority of women, 46.7% (253 cases), had labor induced, indicating that medical interventions were employed to initiate labor. Among the cases with preeclampsia, the distribution of labor onset was as follows: 21.7% (13 cases) experienced spontaneous labor onset. The majority, 78.3% (47 cases), had labor induced, indicating the need for medical intervention to initiate labor. Table 2: Complications developed during pregnancy Variable Category Preeclampsia No Yes Total Developed complication Liver failure 0 8 8 Renal failure 2 1 3 DIC 13 11 24 HELLP syndrome 0 2 2 All above 2 1 3 Liver and renal failure 4 2 6 Table 2 among women who developed complication (46 cases), 8 individuals (8 cases) developed liver failure due to preeclampsia, while none of the cases without preeclampsia experienced liver failure. This suggests that liver failure is a complication specifically associated with preeclampsia. One of the cases with preeclampsia had renal failure, while 2 cases without preeclampsia developed renal failure. Disseminated Intravascular Coagulation (DIC): Among the cases with preeclampsia, 11 individuals (11 cases) developed DIC, while 13 cases without preeclampsia experienced DIC. Both groups showed instances of DIC, indicating that this complication can occur in both preeclampsia and non-preeclampsia cases. HELLP Syndrome: Two cases with preeclampsia developed HELLP syndrome, while none of the cases without preeclampsia had this complication. This suggests that HELLP syndrome is specifically associated with preeclampsia. Combined Complications: Among all the cases, 3 individuals (3 cases) experienced all the complications mentioned above (liver, renal failure, DIC and HELPP syndrome). One case occurred in the group without preeclampsia, while two cases were observed in the preeclampsia group. Liver and Renal Failure: Six cases in total (4 cases with preeclampsia and 2 cases without preeclampsia) developed both liver and renal failure. This indicates that the occurrence of both liver and renal failure is associated to preeclampsia. Table 3 : Summary of continuous variables variable Mean Sd Median Min Max Maternal Age 27.68 5.44 28 17 45 Weight in Kg 27.68 5.44 28 49 97 GA at admission 38.81 2.32 39 28 42 Time Visit 26.39 8.28 26 4 42 SBP 114.04 13.40 111 94 190 DBP 68.54 12.03 67 49 124 Result in Table 3 shows that the average age is approximately 27.68 with a standard deviation of approximately 5.44. The median age is 28, which means that half of the values are greater than or equal to 28, and half are less than or equal to 28. The average weight is approximately 27.68 with a standard deviation of approximately 5.44. The average gestational age is approximately 38.81 with a standard deviation of approximately 2.32. The average time of visit is approximately 26.39 with a standard deviation of approximately 8.28. The average systolic blood pressure is approximately 114.04 with a standard deviation of approximately 13.40. The median systolic blood pressure is 111, indicating that half of the values are greater than or equal to 111, and half are less than or equal to 111. The average diastolic blood pressure is approximately 68.54 with a standard deviation of approximately 12.03. The median diastolic blood pressure is 67, indicating that half of the values are greater than or equal to 67, and half are less than or equal to 67. Next, survival probability of developing preeclampsia is estimated using Kaplan Meier plot. Kaplan Meier plot in Figure 1 shows that probability of developing preeclampsia over time in weeks, indicating the risk of developing preeclampsia is increase as time increase. It appears that multigravida pregnancies have a higher survival probability in terms of developing preeclampsia compared to prim-gravida pregnancies. This suggests that individuals with multiple pregnancies (multigravida) have a relatively lower risk of developing preeclampsia compared to those with a single pregnancy (prim-gravida). The Kaplan Meier plot showed that pregnant women who are less than 30 years old have a higher survival probability in terms of developing preeclampsia. On the other hand, pregnant women aged 30 years or more seem to have a higher risk of developing preeclampsia compared to those who are younger than 30 years old. Figure 1 revealed that pregnant women who have no family history of blood pressure have a higher survival probability. Conversely, pregnant women who have a family history of blood pressure problems are at a higher risk of developing preeclampsia compared to those without a family history of the condition (Figure 1). Figure 2 shows the observed longitudinal measures plotted against time in weeks for the 549 pregnant women included in the analysis. The heterogeneity of the pregnant women systolic blood pressure (SBP) diastolic blood pressure (DBP) is apparent in this plot (see Figure 2). The smooth curve represents the average trajectory. This indicates that a random effect on the average score might be adequate. 3.2 Inferential results Ensuring the normality of residuals and random effects in linear mixed models is fundamental for achieving robust and reliable statistical inferences. The normality assumption was assessed using Q-Q plots, which demonstrated that the residuals and random effects follow a normal distribution. This adherence enhances the model's ability to capture the underlying patterns in the data accurately. Consequently, it improves model fit, leading to more reliable predictions and well-calibrated prediction intervals, thereby strengthening the validity of the analysis (Figure 3). Diagnostics of the Cox PH model: The assumptions of the cox PH model are checked by employing log-Minus-Log survival curves, Schoenfeld Residuals plot (see figure 4-5). The log-minus-log survival curve is parallel. Parallel curves indicate that the hazard ratios remain constant over time, supporting the assumption (Figure 4). The figure 5, showed the residuals display a random scatter around zero, it indicates that the effect of the predictor variables on the hazard rate remains constant over time. This is consistent with the proportional hazards assumption, which assumes that the hazard ratios for the predictor variables are constant over time. Variable Selection for Sub-models Variable selection in joint models involves selecting the covariates to include in the sub-models for both the longitudinal and time-to-event components. Stepwise Selection: The stepwise selection methods, such as forward selection or backward elimination, to iteratively add or remove covariates from the sub-models. These methods evaluate the significance of covariates based on statistical criteria, such as Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC). As candidate, Age, weight, gravidity, parity, family history of blood pressure, previous abortion, History of preeclampsia, number of ANC visit, pregnant multiplicity, and gestational age (GA) variables are considered for each models. Hence, Age, gravidity, family history of blood pressure, previous abortion, number of ANC visit and pregnant multiplicity variables are selected for Cox Ph model using stepwise method. Besides, Age, Weight, family history of blood pressure, and previous abortion variables are selected for longitudinal sub-model. Diagnostics of Bayesian Joint Model Trace plots (Figure 6) for parameters of the longitudinal and survival sub-models show rapid oscillations without trends, indicating efficient mixing and swift convergence of the Markov chains. Density plots display smooth, unimodal distributions, confirming accurate sampling from the target distribution. The Gelman-Rubin statistic ( for all parameters is close to 1, further validating convergence and reliability of the results (see Table 4). These diagnostics confirm the robustness of the Bayesian joint model implemented using the JMbayes2 package in R software. This confirms that the Markov chains exhibit strong convergence and good mixing, ensuring the reliability of the results. We provide the posterior summaries of all the parameters in the Bayesian joint model of interest in Table 4. The results of the Bayesian joint Cox model for the longitudinal SBP process provide valuable insights into the factors influencing SBP: The estimated overall mean SBP is 101.5 mmHg, with a 95% credible interval of (97.06, 105.90), indicating a statistically significant baseline measurement. For each additional week of follow-up, SBP increases by an average of 0.56 mmHg (95% credible interval: 0.51, 0.63), demonstrating a significant and consistent upward trend in SBP over time. Participants' age shows a significant positive effect on SBP, with a mean increase of 0.16 mmHg per year (95% credible interval: 0.05, 0.26). Older individuals are more likely to have higher SBP. For every additional kilogram of body weight, SBP increases by 0.10 mmHg (95% credible interval: 0.04, 0.17), suggesting that higher body weight is associated with elevated SBP. Participants with a family history of hypertension exhibit significantly higher SBP levels, with an average increase of 2.24 mmHg (95% credible interval: 0.16, 4.33) compared to those without a family history. A history of previous abortion is associated with a mean increase in SBP of 2.92 mmHg (95% credible interval: 0.51, 5.29), indicating a noteworthy impact on SBP levels. All covariates have credible intervals that exclude 0, underscoring their robust effects on SBP dynamics, as shown in Table 4. Table 4: Bayesian joint multivariate longitudinal and survival time model Random-effects covariance matrix: StdDev Corr (Intr) 10.117 (Intr) Time (Intr) Time (in weeks) 0.5746 -0.9614 (Intr) 14.2183 0.0345 -0.0127 Time 0.6057 -0.3166 0.3473 -0.9299 Survival Outcome: Time to develop preeclampsia Mean StDev 2.5% 97.5% P Rhat Age (30 and more years) 0.7436 0.4140 0.0509 1.6043 0.0483 1.0003 < 30 years(ref) Gravidity (Multigravida) -0.6606 0.3264 -1.3165 -0.0276 0.0407 1.0062 prim gravida (ref) Family history BP(Yes) 0.9740 0.3683 0.2686 1.6935 0.0056 1.0261 NO(ref) Previous abortion(yes) 1.0776 0.3744 0.3383 1.8207 0.0034 1.0206 No (ref.) Number ANC (less than 4) 0.7430 0.8916 -0.8062 2 .6709 0.4033 1.0009 4 and more times(ref) Pregnant Multiplicity (single) 0.2545 0.6960 -1.2383 1.5067 0.6700 1.0017 Twin(ref) SBP (α 1 ) 0.0635 0.0277 0.0073 0.1196 0.0254 1.1247 DBP (α 2 ) 0.0739 0.0294 0.0183 0.1366 0.0071 1.0269 Longitudinal Outcome: Systolic blood pressure (SBP) Mean StDev 2.5% 97.5% P Rhat Intercept 101.5061 2.2586 97.0620 105.9024 0.0000 1.0106 Time (in weeks) 0.5693 0.0314 0.5080 0.6307 0.0000 1.1294 Women’s Age 0.1604 0.0540 0.0509 0.2641 0.0033 1.0227 Women’s Weight in kg 0.1067 0.0311 0.0451 0.1666 0.0003 1.0153 Family history of BP (yes) 2.2436 1.0711 0.1592 4.3321 0.0373 1.0023 No (ref) Previous abortion (yes) 2.9202 1.2316 0.5164 5.2882 0.0180 1.0008 No (ref) Sigma 9.5841 0.1499 9.2953 9.8741 0.0000 1.0255 Longitudinal Outcome: Diastolic blood pressure (DBP) Mean StDev 2.5% 97.5% P Rhat (Intercept) 52.6364 2.1275 48.4890 56.8711 0.0000 1.0180 Time (in weeks) 0.5148 0.0323 0.4518 0.5774 0.0000 1.0236 Age (in weeks) 0.0415 0.0480 -0.0524 0.1354 0.3813 1.0316 Weight in kg 0.0128 0.0290 -0.0430 0.0678 0.6583 1.0284 Family history of BP (yes) 2.1873 0.9380 0.3677 3.9944 0.0190 1.0050 No (ref) Previous abortion(yes) 1.0785 1.0709 -1.0540 3.1635 0.3207 1.0014 No (ref.) Sigma 7.9163 0.1271 7.6670 8.1683 0.0000 1.0066 The analysis was performed on a dataset with 549 groups and 60 events (10.9% event rate). The longitudinal outcomes systolic blood pressure (SBP) and diastolic blood pressure (DBP) have 2760 observations each. The estimated standard deviations of the random effect component indicate the amount of variability between individuals for the intercept and time random effects. The intercept random effect has a standard deviation of 10.1, suggesting substantial variability in the initial values among individuals. The time random effect has a standard deviation of 0.57, indicating relatively less variability in the rates of change over time in the systolic blood pressure outcome model. Relationship between intercept and time effects: The correlation between the intercept and time random effects are estimated to be -0.96. This negative correlation suggests that individuals with higher baseline values (intercept) tend to have slower rates of change over time. In other words, individuals who start with higher values have a tendency to experience smaller changes over the observed time period. The correlation between the intercept random effects is estimated to be 0.034, indicating a weak correlation between the initial values (intercepts) of different individuals. This suggests that the initial values of individuals are relatively independent of each other (Table 4). The intercept random effect of DBP outcome has a standard deviation of 14.2, suggesting substantial variability in the initial values among individuals. The time random effect has a standard deviation of 0.60 indicating relatively less variability in the rates of change over time in diastolic blood pressure outcome model. Relationship between time effects: The correlation between time random effects is estimated to be -0.93. This negative correlation indicates a relationship between the rates of change over time among individuals. Individuals with faster rates of change tend to have slower rates of change over time, and vice versa. Moreover, like joint model, maternal with family history of blood pressure" indicates that pregnant women with a family history of blood pressure issues tend to have higher systolic blood pressure measurements compared to pregnant women without a family history of blood pressure issues. The odds ratio would be approximately 9.39 (exp (2.24)). This means that pregnant women with a family history of blood pressure have approximately 9.39 times higher odds of having higher systolic blood pressure compared to pregnant women without a family history of blood pressure. This suggests that family history plays a role in influencing the systolic blood pressure values of pregnant women (Table 4). The systolic blood pressure (SBP) is measured in millimeters of mercury (mmHg), a coefficient of 2.92 would suggest that pregnant women with a previous abortion have, on average, a 2.92 mmHg higher SBP compared to pregnant women’s without a previous abortion, adjusting for other factors in the model. The coefficient of 0.56 indicates the change in the outcome variable (SBP) associated with a one-unit increase in the predictor variable (maternal age in years) while holding other variables constant. Similarly, the coefficient of 0.16 indicates the change in the outcome variable (SBP) associated with a one-unit increase in the predictor variable (maternal weight in kg) while holding other variables constant. The findings from the diastolic blood pressure (DBP) longitudinal analysis using the Bayesian joint log-normal Accelerated Failure Time (AFT) model reveal that the overall mean for the longitudinal process is statistically significant, with an estimate of 52.63 and a 95% credible interval ranging from 48.49 to 56.87. Among the covariates examined, follow-up time in weeks significantly influenced DBP, with an estimate of 0.51 and a 95% credible interval of (0.45, 0.57), excluding 0. Additionally, a family history of blood pressure emerged as a significant factor, with an estimate of 2.18 and a 95% credible interval of (0.36, 3.99), also excluding 0. The analysis suggests that pregnant women with a family history of blood pressure issues tend to experience higher diastolic blood pressure compared to those without such a history. The odds ratio for this association is approximately 8.9 (exp(2.18)), indicating that women with a family history of blood pressure are about 8.9 times more likely to have elevated DBP than those without a family history. This highlights the significant impact of family history on the diastolic blood pressure levels of pregnant women (Table 4). The results from the survival analysis indicate several significant factors influencing the occurrence of preeclampsia onset. Maternal age, specifically being under 30 years, shows a significant effect, with an estimate of 0.77 and a 95% credible interval of (0.05, 1.60), excluding 0. This suggests that pregnant women under 30 years have lower odds of developing preeclampsia, with an odds ratio of approximately 2.16 (exp(0.77)). In other words, younger women are 2.16 times less likely to develop preeclampsia compared to women aged 30 years or older, controlling for other covariates. A family history of blood pressure also plays a crucial role, with an estimate of 1.01 and a 95% credible interval of (0.26, 1.78), excluding 0. The odds ratio for this factor is approximately 2.73 (exp(1.01)), indicating that pregnant women with a family history of blood pressure issues are 2.73 times more likely to develop preeclampsia compared to those without such a family history. The presence of a previous abortion is another significant factor, with an estimate of 1.1 and a 95% credible interval of (0.33, 1.88), excluding 0. This corresponds to an odds ratio of approximately 3.01 (exp(1.1)), suggesting that women with a previous abortion have 3.01 times higher odds of developing preeclampsia compared to those without a prior abortion. Additionally, the analysis shows that primigravida women (those pregnant for the first time) have a higher relative risk of developing preeclampsia compared to multigravida women (those pregnant more than once). The odds ratio for this factor is 0.52 (exp(-0.64)), meaning that multigravida women have about 0.52 times the odds of developing preeclampsia compared to primigravida women after controlling for other variables. This suggests a lower likelihood of developing preeclampsia among women with previous pregnancies. These results indicate that maternal age, gravidity, previous abortion and family history of blood pressure are associated with the occurrence of onset preeclampsia. However, the effects of multiplicity of the pregnant and number of antenatal care (ANC) do not reach statistical significance in predicting the occurrence of onset preeclampsia in this analysis. The joint Cox proportional hazards (Cox PH) model analysis of time-to-event and a longitudinal process reveals a strong and statistically significant association between both systolic blood pressure (SBP) and diastolic blood pressure (DBP) as longitudinal processes, and the survival process (the relative risk of developing preeclampsia). The 95% credible interval for the association between SBP and the risk of developing preeclampsia does not include zero, with a credible interval of (0.007, 0.119), indicating that this relationship is statistically significant. Furthermore, the association parameter (α) for SBP suggests that each one-unit increase in SBP is associated with a 6.5% increase in the relative risk of developing preeclampsia (exp (0.0635) = 1.065), after controlling for other covariates. Similarly, the association between DBP and preeclampsia onset is also statistically significant, as indicated by the 95% credible interval of (0.018, 0.136), which excludes zero. The corresponding association parameter (α) for DBP indicates that each one-unit increase in DBP is associated with an 7.7% increase in the relative risk of preeclampsia (exp (0.074) = 1.077), controlling for other factors (Table 4). These findings underline a clear positive association between both SBP and DBP with the relative risk of developing preeclampsia. Specifically, higher measurements of both systolic and diastolic blood pressure are linked to a significantly increased risk of preeclampsia, emphasizing the importance of monitoring blood pressure levels in pregnant women for early identification of those at higher risk for this condition. 3.3 Discussion The aim of this study is to employ Bayesian joint modeling to analyze the relationship between blood pressure and the occurrence of preeclampsia, a risky health outcome, among pregnant women attending delivery services at Bishoftu General Hospital in Central Oromia region, Ethiopia. Out of a total of 549 pregnant women who attended maternal delivery at Bishoftu Hospital, 10.9% were diagnosed with preeclampsia. Among those diagnosed with preeclampsia, 93.3% did not result in death, while 6.7% unfortunately resulted in fatalities. The individual profile plots of the blood pressure (BP) measurements indicate variability within and between individuals, while the exploratory analysis suggests a linear pattern of increase in BP over time on average. The linear mixed model (LMM), identified that follow up time, weight, gravidity, age, family history of blood pressure and previous abortion were found to be significant at 5% significance level. Our findings are similar to the study by [45], [46]. Specifically, our study revealed that BP tends to increase with higher baseline age. This observation is consistent with the findings of a study conducted by [47] and [46], which also reported age at baseline as a significant predictor for BP in pregnant women. The results of this study reveal a correlation of 0.71 between the two blood pressure measurements, diastolic blood pressure (DBP) and systolic blood pressure (SBP). This finding provides direct evidence that both SBP and DBP tend to decrease at the beginning of pregnancy and gradually increase throughout the course of pregnancy. These findings align with previous studies, such as the study conducted by [46] and [47], which also supported the observation that SBP and DBP decrease early in pregnancy and show an upward trend as pregnancy progresses. The findings from your Cox regression model indicate that several risk factors are associated with the development of preeclampsia. These risk factors include the age of pregnant women, gravidity, family history of blood pressure, and previous history of abortion. Specifically, the study found a significant association between maternal age and the risk of preeclampsia. Older women were found to have a higher risk of developing preeclampsia compared to younger women. This aligns with previous researches conducted by [48] and [49] have consistently demonstrated a link between maternal age and the risk of preeclampsia. Furthermore, a woman with a family history of blood pressure was found to have a significantly higher risk and hazard of developing preeclampsia during the follow-up period. This suggests that having a family history of blood pressure of pregnancy increases the likelihood of developing preeclampsia as compared those women who haven’t. This finding is aligns with researches conducted in Brazil [50], Sudan [51], Pakistan [52] and Uganda [53]. The findings of our study indicate that women in their first pregnancy status (primi gravida) were more likely to develop preeclampsia as multigravida. The odds of developing preeclampsia in primi gravida women were found to be 0.52 times higher compared to multigravida women (women who have had multiple pregnancies). This finding is consistent with studies conducted by [54] in Gaza Strip, [55] in Ethiopia, and [56] in Egypt. These studies also reported that primi gravida is a risk factor for preeclampsia. This study indicated that a history of abortion perceived (prior induced abortions) was positively associated with development of preeclampsia. Women who had a history of abortion perceived were found to be more likely to develop preeclampsia compared to those without history of abortion. This finding is consistent with studies conducted in India by [57] and Iran by [58]. These studies also reported a positive association between a history of abortion perceived and the risk of developing preeclampsia. Furthermore in our joint modeling analysis, we observed significant association parameters (α) between two outcomes. The estimated association parameters were found to be 0.06 and 0.07, indicating a positive association between the true values of systolic blood pressure (SBP) and diastolic blood pressure (DBP) with the risk of preeclampsia over a follow-up period. These findings are consistent with a studies conducted by [46] and [59]. 4. Conclusion This study highlights the critical importance of proactive screening for preeclampsia in pregnant women, as 10.9% of them are affected by preeclampsia. The high mortality rate (6.7%) among women with preeclampsia, the occurrence of additional complications (40%), and the incidence of stillbirths (13.3%) among pregnant women with preeclampsia are significant findings. In this study, a comprehensive analysis was conducted to examine the relationship between longitudinal biomarker measurements and the occurrence of preeclampsia. By considering both longitudinal outcome and time-to-event data, we identified several factors associated with the risk of developing preeclampsia, including a history of previous abortion, a family history of blood pressure problems, maternal age of 30 years or older, and gravidity. The study reveals important insights into the risk of developing preeclampsia over time. Multigravida pregnancies exhibit a higher survival probability compared to prim gravida pregnancies, indicating a relatively lower risk of preeclampsia in individuals with multiple pregnancies. Pregnant women below the age of 30 have a higher survival probability, while those aged 30 years or older face an increased risk of preeclampsia. Additionally, pregnant women without a family history of blood pressure problems have a higher survival probability, whereas those with a family history face a heightened risk of preeclampsia. Pregnant women who had repeated measurements of systolic blood pressure (SBP) and diastolic blood pressure (DBP) over time during their pregnancy were found to have an association with the risk of developing preeclampsia. The findings of this study suggest that blood pressure measurements, specifically SBP and DBP, taken repeatedly during pregnancy, can serve as important indicators of the risk of developing preeclampsia. Higher baseline blood pressure measurements may indicate an increased predisposition to preeclampsia, while lower baseline blood pressure measurements may indicate a lower risk. Monitoring blood pressure, specifically SBP and DBP, throughout pregnancy can provide important indicators for assessing the risk of developing preeclampsia. Declarations Consent for publication Not applicable. Data Availability Statement: The datasets used and analyzed during the study are available from the corresponding author upon reasonable request. Funding This research received no funding. Conflict of Interest: The authors declare that they have no conflict of interest. Acknowledgements The authors express their gratitude to the Bishoftu General Hospital for their support and cooperation in this study. Special thanks to the staff at Bishoftu General Hospital for facilitating access to the maternal delivery follow-up data. Authors' Contributions MAA conceived the study, designed the methodology, and also contributed to the development of the data collection protocol, performed the statistical analysis, and drafted the manuscript. ATG assisted with the literature review, data interpretation, and critical revision of the manuscript for intellectual content. All authors (MAA and ATG) reviewed and approved the final manuscript. References Zhou, B. et al. Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19· 1 million participants. Lancet 389 (10064), 37–55 (2017). Roberts, J. M., Pearson, G., Cutler, J. & Lindheimer, M. Summary of the NHLBI working group on research on hypertension during pregnancy. Hypertension 41 (3), 437–445 (2003). Khan, K. S., Wojdyla, D., Say, L., Gülmezoglu, A. M. & Van Look, P. F. A. WHO analysis of causes of maternal death: a systematic review. Lancet 367 (9516), 1066–1074 (2006). Ezzati, M., Lopez, A. D., Rodgers, A., Vander Hoorn, S. & Murray, C. J. L. Selected major risk factors and global and regional burden of disease. Lancet 360 (9343), 1347–1360 (2002). Kearney, P. M. et al. Global burden of hypertension: analysis of worldwide data. Lancet 365 (9455), 217–223 (2005). Ghulmiyyah, L. M. & Sibai, B. M. Gestational Hypertension, Preeclampsia, and Eclampsia, Queenan’s Manag. High-Risk Pregnancy An Evidence‐Based Approach , pp. 280–288, (2012). Choudhury, M. & Friedman, J. E. Epigenetics and microRNAs in preeclampsia. Clin. Exp. Hypertens. 34 (5), 334–341 (2012). Organization, W. H. WHO recommendations: policy of interventionist versus expectant management of severe pre-eclampsia before term (World Health Organization, 2018). Gaym, A. et al. Disease burden due to pre-eclampsia/eclampsia and the Ethiopian health system’s response. Int. J. Gynecol. Obstet. 115 (1), 112–116 (2011). Firoz, T., Sanghvi, H., Merialdi, M. & von Dadelszen, P. Pre-eclampsia in low and middle income countries. Best Pract. Res. Clin. Obstet. Gynaecol. 25 (4), 537–548 (2011). Tsigas, E. Advocacy is essential to supporting women with pre-eclampsia. Obstet. Med. 10 (1), 33–35 (2017). Pauli, J. M. & Repke, J. T. Preeclampsia: short-term and long-term implications. Obstet. Gynecol. Clin. 42 (2), 299–313 (2015). Phipps, E., Prasanna, D., Brima, W. & Jim, B. Preeclampsia: updates in pathogenesis, definitions, and guidelines. Clin. J. Am. Soc. Nephrol. 11 (6), 1102–1113 (2016). Chung, S. S. et al. Demographic and Health Survey ethiopia . (2016). 10.1109/ipfa.2004.1345625 Berhe, A. K., Kassa, G. M., Fekadu, G. A. & Muche, A. A. Prevalence of hypertensive disorders of pregnancy in Ethiopia: a systemic review and meta-analysis. BMC Pregnancy Childbirth . 18 , 1–11 (2018). Ngwenya, S. Severe preeclampsia and eclampsia: incidence, complications, and perinatal outcomes at a low-resource setting, Mpilo Central Hospital, Bulawayo, Zimbabwe. Int. J. Womens Health , pp. 353–357, (2017). Kahsay, H. B., Gashe, F. E. & Ayele, W. M. Patterns of hypertensive disorders of pregnancy in selected hospitals of Tigray, Ethiopia. World Health . 1 , 4–6 (2018). Kongwattanakul, K., Saksiriwuttho, P., Chaiyarach, S. & Thepsuthammarat, K. Incidence, characteristics, maternal complications, and perinatal outcomes associated with preeclampsia with severe features and HELLP syndrome. Int. J. Womens Health , pp. 371–377, (2018). Creswell, J. W. Research Design: Qualitative, QuCreswell, JW. Research design Qualitative quantitative and mixed methods approaches. 2014. (2014). Riley, R. D. et al. Minimum sample size for developing a multivariable prediction model: PART II-binary and time‐to‐event outcomes. Stat. Med. 38 (7), 1276–1296 (2019). Basharat, N. Joint analysis of longitudinal and time to event data using accelerated failure time models (A Bayesian approach. University of Saskatchewan, 2019). Lawless, J. F. Statistical models and methods for lifetime data (Wiley, 2011). Aalen, O., Borgan, O. & Gjessing, H. Survival and event history analysis: a process point of view (Springer Science & Business Media, 2008). Klein, J. P. & Moeschberger, M. L. Survival analysis: techniques for censored and truncated data (Springer Science & Business Media, 2006). Kaplan, E. L. & Meier, P. Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53 (282), 457–481 (1958). Collett, D. Modelling survival data in medical research (Chapman and Hall/CRC, 2023). Cox, D. R. Regression models and life-tables. J. R Stat. Soc. Ser. B . 34 (2), 187–202 (1972). Collett, D. Modelling Survival Data in Medical Research . in Chapman & Hall/CRC Texts in Statistical Science. CRC Press, [Online]. (2015). Available: https://books.google.com.et/books?id=Okf7CAAAQBAJ Wei, L. J. The accelerated failure time model: a useful alternative to the Cox regression model in survival analysis. Stat. Med. 11 , 14–15 (1992). Erango, M. A., Goshu, A. T., Buta, G. B. & Dessisoa, A. H. Bayesian joint modelling of survival of HIV/AIDS patients using accelerated failure time data and longitudinal CD4 cell counts. Br. J. Med. Med. Res. 20 (6), 1–12 (2017). Rizopoulos, D. Joint models for longitudinal and time-to-event data: With applications in R (CRC, 2012). Buta, G. B., Goshu, A. T. & Worku, H. M. Bayesian joint modelling of disease progression marker and time to death event of HIV/AIDS patients under ART follow-up. Br. J. Med. Med. Res. 5 (8), 1034–1043 (2014). Guo, X. & Carlin, B. P. Separate and joint modeling of longitudinal and event time data using standard computer packages. Am. Stat. 58 (1), 16–24 (2004). McCrink, L. M., Marshall, A. H. & Cairns, K. J. Advances in joint modelling: a review of recent developments with application to the survival of end stage renal disease patients. Int. Stat. Rev. 81 (2), 249–269 (2013). Schluchter, M. D. Methods for the analysis of informatively censored longitudinal data. Stat. Med. 11 , 14–15 (1992). Dempster, A. P., Laird, N. M. & Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. J. R Stat. Soc. Ser. B , 39 , 1, pp. 1–22, 1977. Bolstad, W. M. & Curran, J. M. Introduction to Bayesian statistics (Wiley, 2016). Boldgiv, B. & Bolstad, M. Introduction to Bayesian Statistics by William 354 pages, ISBN 0-471-27020-2, A John Wiley and Sons, Inc., hardcover, US $ 84.95., Mong. J. Biol. Sci. , vol. 2, no. 2, pp. 77–78, 2004. (2004). Blasco, A. The Bayesian Choice . (2017). 10.1007/978-3-319-54274-4_2 Van de Schoot, R. et al. A gentle introduction to Bayesian analysis: Applications to developmental research. Child. Dev. 85 (3), 842–860 (2014). Diebolt, J. & Robert, C. P. Estimation of finite mixture distributions through Bayesian sampling. J. R Stat. Soc. Ser. B . 56 (2), 363–375 (1994). Givens, G. H. & Hoeting, J. A. Computational Statistics . in Wiley Series in Probability and Statistics. Wiley, [Online]. (2005). Available: https://books.google.com.et/books?id=ByDvAAAAMAAJ Zhao, X., Yu, C. & Tong, H. A Bayesian approach to weibull survival model for clinical randomized censoring trial based on MCMC simulation, in 2nd International Conference on Bioinformatics and Biomedical Engineering , IEEE, 2008, pp. 1181–1184. (2008). Geman, S. & Geman, D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. , 6, pp. 721–741, (1984). Farias, D. R. et al. Total cholesterol and leptin concentrations are associated with prospective changes in systemic blood pressure in healthy pregnant women. J. Hypertens. 32 (1), 127–134 (2014). Haile, D. B., Aguade, A. E. & Fetene, M. Z. Joint modeling of hypertension measurements and time-to-onset of preeclampsia among pregnant women attending antenatal care service at Arerti Primary Hospital, North Shoa, Ethiopia. Cogent Public. Heal . 9 (1), 2022846 (2022). Mo, X. B., Lei, S. F., Zhang, Y. H. & Zhang, H. Examination of the associations between m6A-associated single-nucleotide polymorphisms and blood pressure. Hypertens. Res. 42 (10), 1582–1589 (2019). Kashanian, M., Baradaran, H. R., Bahasadri, S. & Alimohammadi, R. Risk factors for pre-eclampsia: a study in Iran. Arch. Iran. Med. 14 (6), 412–415 (2011). Lamminpää, R., Vehviläinen-Julkunen, K., Gissler, M. & Heinonen, S. Preeclampsia complicated by advanced maternal age: a registry-based study on primiparous women in Finland 1997–2008. BMC Pregnancy Childbirth . 12 , 1–5 (2012). Dalmáz, C. A., dos Santos, K. G., Botton, M. R. & Roisenberg, I. Risk factors for hypertensive disorders of pregnancy in southern Brazil. Rev. Assoc. Med. Bras. 57 , 692–696 (2011). Adam, I., Elhassan, E. M., Mohmmed, A. A., Salih, M. M. & Elbashir, M. I. Malaria and pre-eclampsia in an area with unstable malaria transmission in Central Sudan. Malar. J. 10 , 1–4 (2011). Shamsi, U. et al. A multicentre matched case control study of risk factors for preeclampsia in healthy women in Pakistan. BMC Womens Health . 10 , 1–7 (2010). Kiondo, P. et al. Risk factors for pre‐eclampsia in Mulago Hospital, Kampala, Uganda. Trop. Med. Int. Heal . 17 (4), 480–487 (2012). El-Nakhal, S. Case-control study of risk factors associated with preeclampsia in the Gaza strip. J. Med. Med. Sci. 6 (9), 229–233 (2015). Grum, T., Seifu, A., Abay, M., Angesom, T. & Tsegay, L. Determinants of pre-eclampsia/Eclampsia among women attending delivery Services in Selected Public Hospitals of Addis Ababa, Ethiopia: a case control study. BMC Pregnancy Childbirth . 17 , 1–7 (2017). El-Moselhy, E. A., Khalifa, H. O., Amer, S. M., Mohammad, K. I. & Abd-El-Aal, H. M. Risk factors and impacts of pre-eclampsia: an epidemiologicl study among pregnant mothers in Cairo, Egypt., (2011). Agrawal, S., Walia, G. K., Staines-Urias, E., Casas, J. P. & Millett, C. Prevalence of and risk factors for eclampsia in pregnant women in India. Fam Med. Community Heal . 5 (4), 225–244 (2017). Ajah, L. O. et al. The feto-maternal outcome of preeclampsia with severe features and eclampsia in Abakaliki, South-East Nigeria. J. Clin. Diagn. Res. JCDR . 10 (9), QC18 (2016). Baschat, A. A. et al. Maternal blood‐pressure trends throughout pregnancy and development of pre‐eclampsia in women receiving first‐trimester aspirin prophylaxis. Ultrasound Obstet. Gynecol. 52 (6), 728–733 (2018). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6405478","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":491517194,"identity":"614988ee-e8e0-43af-9417-d58151b4596d","order_by":0,"name":"Merga Abdissa Aga","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYLACxgZmHgYG9oMPgGwePhK08CQbgLSwEasFRJlJgEiCWnTbex+/+LnDWka+/UBa5dccOxk2BuaHj27g0WJ25riZZe+ZdB6DM4nHbstuSwY6jM3YOAeflhtpbAa8bYd5DBgS0m5LbmMGauFhk8ar5f4zNsO/QC3y/Q/MiiW31ROh5QYb82OQLQw3EswYP247TISWM2lszLIgv9x4kyzNuO04DxszIb8cP8b88e0Oa3v5/vSDH39uq7bnZ29++BifFiBgk4CxQBEKJPErByv5AGMx/iCsehSMglEwCkYgAAAnJkbJQOlfGAAAAABJRU5ErkJggg==","orcid":"","institution":"Kotebe University of Education","correspondingAuthor":true,"prefix":"","firstName":"Merga","middleName":"Abdissa","lastName":"Aga","suffix":""},{"id":491517195,"identity":"9eec6893-a281-4d6d-9631-02f39f8fc763","order_by":1,"name":"Ayele Taye Goshu","email":"","orcid":"","institution":"Kotebe University of Education","correspondingAuthor":false,"prefix":"","firstName":"Ayele","middleName":"Taye","lastName":"Goshu","suffix":""}],"badges":[],"createdAt":"2025-04-08 17:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6405478/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6405478/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87825155,"identity":"ff3b84eb-e2f9-4bc1-be14-c88d8005783e","added_by":"auto","created_at":"2025-07-29 11:40:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60048,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan Meier plot by the history blood pressure, women age group and gravidity.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6405478/v1/dd6bcd9ada4f13354160e57d.jpg"},{"id":87825159,"identity":"7c9434ac-1f53-4d74-b255-07ebb11dd4f9","added_by":"auto","created_at":"2025-07-29 11:40:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":150111,"visible":true,"origin":"","legend":"\u003cp\u003ea longitudinal profile plot for SBP and DBP\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6405478/v1/5068b6f3786202c79dff7006.jpg"},{"id":87825742,"identity":"2069f1d1-b2df-48e8-90aa-5fe0faf112af","added_by":"auto","created_at":"2025-07-29 11:48:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38677,"visible":true,"origin":"","legend":"\u003cp\u003eQ-Q plot for residuals in both models for SBP and DBP\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6405478/v1/76075c9d7da7becc5600be8b.jpg"},{"id":87827400,"identity":"1f51c8ed-c1f5-4cec-8d29-93ba6e858375","added_by":"auto","created_at":"2025-07-29 11:56:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":25296,"visible":true,"origin":"","legend":"\u003cp\u003eLog-Minus-Log survival curves\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6405478/v1/874e7ed6398bde48572301cc.jpg"},{"id":87825162,"identity":"bd8b2211-fce1-4287-8e38-749ed52c09f0","added_by":"auto","created_at":"2025-07-29 11:40:23","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":24540,"visible":true,"origin":"","legend":"\u003cp\u003eSchoenfeld Residuals plot\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6405478/v1/15badf6fac6a38a691ebfd63.jpg"},{"id":87825165,"identity":"2478829d-4dd3-4c10-809c-bc71a36717e7","added_by":"auto","created_at":"2025-07-29 11:40:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":223896,"visible":true,"origin":"","legend":"\u003cp\u003eDensity and trace plots for associate parameters (α) with three chains of 20000 iterations.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6405478/v1/a9c989a6d9b35bb67502ade0.png"},{"id":92936096,"identity":"3d535351-7ed9-4ad1-84e1-65f58022c1e0","added_by":"auto","created_at":"2025-10-07 10:17:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1900147,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6405478/v1/67c4c2c0-9d9c-4004-bb9d-c4e2a80360ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bayesian Modelling of the Dynamics of Preeclampsia and Blood Pressure Data of Pregnant Women","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBlood pressure refers to the force exerted by blood against wall of blood vessel as it circulates throughout the human body. Blood pressure is measured as two levels: systolic blood pressure (SBP), which represents the maximum pressure reached during each heartbeat, and diastolic blood pressure (DBP), which reflects the minimum pressure between two heartbeats. In adults, a normal resting blood pressure is approximately 120 mmHg systolic over 80 mmHg diastolic, commonly expressed as 120/80 mmHg [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Blood pressure readings ranging from 120/70 to 140/90 mmHg are generally considered to indicate an increased risk of high blood pressure.\u003c/p\u003e\u003cp\u003eHypertensive disorders of pregnancy pose a significant risk to maternal health, leading to high rates of maternal mortality and severe morbidity. Hypertension refers to high blood pressure, and globally, it complicates around 3\u0026ndash;10% of all pregnancies, contributing to significant maternal and perinatal complications [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Approximately 18% of maternal deaths worldwide can be attributed to hypertensive disorders of pregnancy, resulting in an estimated 62,000\u0026ndash;77,000 deaths annually [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This problem is particularly pronounced in low and middle-income countries, where access to healthcare services is limited, and the quality of maternal and neonatal care is inadequate. Hypertension significantly increases the risk of various cardiovascular conditions, including coronary heart disease, congestive heart failure, stroke, renal failure, and other arterial diseases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The global burden of cardiovascular diseases has been largely influenced by lifestyle factors such as changes in diet and physical activity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLiterature shows that preeclampsia has already affected 2\u0026ndash;8% of pregnant women worldwide. It is characterized by elevated blood pressure and the presence of proteinuria in the urine during the second or third trimester of pregnancy [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Preeclampsia typically develops in the later stages of pregnancy but can occur at any point during the second half of gestation, during labor, or up to six weeks postpartum. It is a serious and not fully understood pregnancy complication that can result in adverse health outcomes for both the mother and baby [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAccording to the World Health Organization [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] report global, preeclampsia and related conditions such as gestational hypertension are cause for the deaths of approximately 76,000 mothers and 500,000 babies worldwide each year. The majority of perinatal deaths resulting from complications of pregnancy-related hypertension occur in low and middle-income countries. A WHO report from 2015 highlights that out of the 830 daily maternal deaths, 550 occur in Sub-Saharan Africa, 180 in Southern Asia, and only 5 in developed countries. The risk of a woman in a developing country dying from maternal-related causes during their lifetime is approximately 33 times higher compared to a woman living in a developed country.\u003c/p\u003e\u003cp\u003eHypertension ranks as the second most common direct cause of maternal death globally, contributing to an estimated 14% of all maternal deaths, which amounts to approximately 303,000 deaths. It is worth noting that in developed countries, maternal mortality resulting from hypertension is relatively rare, accounting for 12.9% of maternal deaths. However, in developing regions, where the vast majority (99%) of maternal deaths occur, hypertension accounts for 14% of these deaths. In specific regions such as sub-Saharan Africa, including Ethiopia, the burden of maternal mortality related to hypertension is even higher, accounting for 16% of maternal deaths [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGlobally, preeclampsia affects approximately 8\u0026ndash;10% of pregnancies, making it a relatively common condition during pregnancy. It is a leading cause of preterm delivery, which carries its own set of risks and complications for the newborn. In fact, preeclampsia accounts for around 20% of all neonatal intensive care admissions, further highlighting its impact on newborn health. In Africa and Asia, preeclampsia accounts for approximately 10% of maternal deaths [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In Ethiopia specifically, it has been identified as the second most common cause of maternal morbidity and the third leading cause of maternal mortality (FMOH). Preeclampsia affects around 5.47% of pregnancies in Ethiopia, indicating a significant burden within the country [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Severe preeclampsia or eclampsia, which represents the severe form of the condition, contributes to approximately 10% of maternal mortality cases in Ethiopia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAccording to the Ethiopian Demographic Health Survey conducted in 2016, the maternal mortality rate in Ethiopia is estimated at 412 deaths per 100,000 live births, equating to approximately 4 maternal deaths for every 1,000 live births [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The Ethiopian National Emergency Obstetric and Newborn Care (EMONC) data indicates that preeclampsia contributes to complications in approximately 1 out of every 5 pregnancies and 1 out of every 16 deliveries. The preeclampsia or its complications account for 16% of direct maternal deaths in Ethiopia [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. According to the research, the trend of maternal mortality due to preeclampsia in the country reveals an increasing over time.\u003c/p\u003e\u003cp\u003eIndeed, the prevalence of preeclampsia can vary within and between countries, including developing nations. In developing countries, the reported prevalence of preeclampsia ranges from 1.8\u0026ndash;16.7% [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]). Similarly, in Ethiopia, the prevalence of preeclampsia varies from 1.2\u0026ndash;19.1% [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Preeclampsia accounts for 16% of maternal deaths in Sub-Saharan Africa and 16.9% in Ethiopia [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The Ethiopian government has recognized the importance of reducing maternal and newborn morbidity and mortality and has implemented various measures to improve access to and quality of facility-based maternal and newborn services. These efforts aim to enhance the provision of emergency obstetric care, including the management of pregnancy-induced hypertension and its complications.\u003c/p\u003e\u003cp\u003ePreeclampsia is a significant health concern during pregnancy that can have adverse effects on both the mother and the fetus in Ethiopia, and so requires detailed investigations. The aim of this study is to address the gap in research and understanding by employing the Bayesian joint modelling approach to analyze the relationship among blood pressure (SBP and DBP), and the development of preeclampsia disease for the pregnant woman.\u003c/p\u003e"},{"header":"2. Methodology of the Study","content":"\u003cp\u003eThis study was used the different methodology to address the study gap and achieve the objectives of the study.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Study Population and Area\u003c/h2\u003e\u003cp\u003eThe study was conducted in Bishoftu\u0026rsquo;s General Hospital located in Oromia Region, Ethiopia. The study population covers of pregnant women who attended maternal delivery during the study period at Bishoftu\u0026rsquo;s General Hospital in the year 2023. Bishoftu General Hospital is located in Bishoftu town. With a population of about 2.4\u0026nbsp;million people living in three towns and five districts within the woreda, Bishoftu General Hospital serves approximately 800 to 900 patients every day. The facility has around 24 service delivery departments and approximately 203 operational patient beds.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e2. 3 Research Philosophy\u003c/h3\u003e\n\u003cp\u003eThe intellectual underpinnings of this study are grounded in post positivism. In post-positivist research, credible knowledge claims emerge as competing interpretations and possible courses of action was discussed and negotiated among the members of a community. In this situation, it would seem appropriate to share our thoughts and potential applications of the ideas with our responders rather than asking them to affirm or refute them. Post positivism is the philosophical aspect of the research dealing with the theory of verification and adopts the quantitative research approach [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Research Approach\u003c/h2\u003e\u003cp\u003eThe quantitative research approaches was conducted in this study in order to achieve the objectives of the study that is to model blood pressure data of pregnant women who are under prenatal follow up. On the other hand a path analysis model is finding to be suitable of explaining the association or relationship blood pressure and time to event (develop preeclampsia).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Study Design\u003c/h2\u003e\u003cp\u003eA retrospective longitudinal study was carried out in 2023, utilizing follow-up or repeated measurements of maternal delivery data at Bishoftu General Hospital. Both longitudinal and survival data were collected from the follow-up cards of pregnant women, which included relevant information for all women who attended maternal delivery during the study period, from January to December 2023.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Inclusion and Exclusion Criteria\u003c/h2\u003e\u003cp\u003e\u003cb\u003eInclusion Criteria\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePregnant women who attended maternal delivery service and have at least two follow up at the Bishoftu\u0026rsquo;s General Hospital were included in the study or sampling procedure.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExclusion Criteria\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe women who have incomplete inf\u003c/p\u003e\u003cp\u003eormation regarding to the study variables on the registration card were not eligible for the study. Pregnant women who discontinued the ANC services were not included in this study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Sampling Procedure\u003c/h2\u003e\u003cp\u003eThe researchers adopted a systematic random sampling technique to select a representative sample from medical charts containing the names and identification numbers of pregnant women. During the study period, a total of 5989 women attended ANC (Antenatal Care) follow-ups. The sampling interval (K) was determined by dividing the total number of women receiving ANC follow-up at the hospital during the study period by the required sample size. As a result, every 11th pregnant woman attending antenatal care follow-up was included in the evaluation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Sample Size Determination\u003c/h2\u003e\u003cp\u003eThe sample size formula presented in this section is based on the three criteria, which minimize overfitting whilst ensuring precise estimates of overall outcome risk [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The first criterion, aims to ensure the optimism of predictor effect estimates is small, as defined by a global shrinkage factor of \u0026ge;\u0026thinsp;0.9. The second criterion extends this idea to ensure the optimism is small on the R\u003csup\u003e2\u003c/sup\u003e Nagelkerke scale, such that there is a difference of \u0026le;\u0026thinsp;5% in the apparent and adjusted percentage of variation explained by the model. Lastly, the third criterion ensures the sample size will precisely estimate the overall outcome risk, which is fundamental. Hence, the minimum sample size required for this study is calculated as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:n=\\frac{p}{{(S}_{VH}-1)\\text{log}(1-\\frac{{{\\:R}_{cs\\:adj}}^{2}}{{S}_{VH}})}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e is the numbers of candidate predictor\u0026rsquo;s variables, S\u003csub\u003eVH\u003c/sub\u003e is Van Houwelingen\u0026rsquo;s global shrinkage factor (at least 0.9) and R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eCS_adj\u003c/sub\u003e is the adjusted Cox-Snell R\u003csup\u003e2\u003c/sup\u003e estimate of the model's (at least 0.1) based on the previous studies in the same setting and population [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHence, in this study we have 10 numbers of candidate predictor\u0026rsquo;s variables and no existing studies or information to identify a sensible value of the expected Cox-Snell R\u003csup\u003e2\u003c/sup\u003e. According to [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], in the absence of any other information, we suggest that sample sizes be derived assuming the value of R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eCS_adj\u003c/sub\u003e corresponds to an R\u003csup\u003e2\u003c/sup\u003e Nagelkerke of 0.15. Therefore, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003e is computed as:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\:\\:n=\\frac{p}{{(S}_{VH}-1)\\text{log}(1-\\frac{{{\\:R}_{cs\\:adj}}^{2}}{{S}_{VH}})}=\\frac{10}{(0.9-1)\\text{log}(1-\\frac{0.15}{0.9})}=\\frac{10}{\\left(0.01823\\right)}=548.5463\\:=\\:549$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe data were collected using specific checklist from Bishoftu\u0026rsquo;s General Hospital during January to December 2023.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVariables of the Study\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eResponse variables\u003c/b\u003e: This study considered three outcome variables: longitudinal measurements of Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) in millimeters of mercury (mmHg), as well as the survival outcome, defined as the time to onset preeclampsia from antenatal care follow-up among pregnant women at the hospital.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003cp\u003eThe study considered factors such as maternal age, Weight in kg, parity (nulliparous, multipara), gravidity(prim gravida, multigravida), family history blood pressure (yes, no), previous history of preeclampsia (yes, no), previous abortion (yes, no), gestational age, number of antenatal care visit (less than 4, 4 and more times), and pregnant multiplicity (single, twin).\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Statistical Models and Analyses\u003c/h2\u003e\u003cp\u003eTo achieve the research objectives, joint models integrating longitudinal and survival data were employed. The analysis included exploratory data analysis, linear mixed-effects models (LME) for longitudinal data, Cox proportional hazards models for survival data, and joint models combining both.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLinear Mixed Effects Models\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLME models account for within-subject correlations and between-subject variability, making them suitable for longitudinal data. The model is expressed as [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{ij}\\left(t\\right)\\:=\\:{{x}^{{\\prime\\:}}}_{ij}\\left(t\\right){\\beta\\:}_{j}\\:+\\:{{z}^{{\\prime\\:}}\\:}_{ij}\\left(t\\right){b}_{ij}\\:+\\:{\\epsilon\\:}_{i}\\left(t\\right),$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{ij}\\left(t\\right)\\sim\\:\\:N\\left(0,\\:{\\sigma\\:}^{2}\\right),\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{b}_{ij}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e\u0026sim; N\u003c/em\u003e(0,\u003cem\u003eD\u003c/em\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eConsider a study with \u0026#119899; randomly selected and independent subjects, and m\u003csub\u003ei\u003c/sub\u003e represents the number of observations for individual i, i\u0026thinsp;=\u0026thinsp;1, 2 \u0026hellip;\u0026hellip;.n. Let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{ij}\\)\u003c/span\u003e\u003c/span\u003e be the mi vector of responses for individual i for jth response variable [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Where β is a vector of fixed effects of X\u003csub\u003eij\u003c/sub\u003e(t) time-varying covariate matrix, b\u003csub\u003eij\u003c/sub\u003e is a vector of random slope effects of Z\u003csub\u003eij\u003c/sub\u003e(t) time-varying covariate matrix. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e is normally distributed with variance \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{\\epsilon\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSurvival Models\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSurvival analysis is a statistical approach used to analyze time-to-event data, where the primary focus is on the time until a specific event of interest occurs. This response variable often termed failure time, survival time, or event time is typically continuous but may be incompletely observed for some individuals. Such incomplete observations, known as censored data, occur when the exact event time is not recorded but is known to exceed a specific value \u0026#119905;. Let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:T\\)\u003c/span\u003e\u003c/span\u003e be a non-negative random variable representing survival times. Lifetime distributions can be defined using any of the following key measures[\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 observed data are denoted by (T, δ), where T\u0026thinsp;=\u0026thinsp;min (X, C) is the follow-up time, and\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\delta\\:\\:=\\:I(X\\le\\:C)\\:is\\:an\\:indicator\\:for\\:status\\:at\\:the\\:end\\:of\\:follow-up,$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\delta\\:\\:=\\:I\\left(X\\le\\:C\\right)\\:=\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:0\\:if\\:\\:\\:X\\:\u0026gt;\\:C\\:\\left(observed\\:censoring\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:1\\:if\\:\\:\\:X\\:\\le\\:\\:C\\:\\left(observed\\:failure\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRight censoring arises due to various factors, including the absence of the event of interest before the study concludes, participants being lost to follow-up during the study, or individuals withdrawing for other reasons, which may involve competing risks. In such scenarios, the observed survival time under right censoring is shorter than the true survival time [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe Kaplan-Meier Estimates of Survival Function\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Kaplan-Meier (K-M) estimator is widely used for individual-level survival data analysis. Unlike the life table method, which is applied to grouped data, the K-M estimator utilizes individual event times, providing greater precision. Here, we will focus on describing the K-M estimator. Assume that \u0026#119903; individuals experience failures in a group of individuals. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Let\\:0\\:\u0026lt;\\:t\\left(1\\right)\\:\u0026lt;\\:\\dots\\:\\dots\\:\u0026lt;\\:\\:t\\left(r\\right)\\:\u0026lt;\\:1\\)\u003c/span\u003e\u003c/span\u003e represents, the observed ordered death times. Let r\u003csub\u003ej\u003c/sub\u003e denote the size of the risk set at time t\u003csub\u003e(j),\u003c/sub\u003e where the risk set refers to the collection of individuals who are alive and uncensored just before t\u003csub\u003e(j)\u003c/sub\u003e. Let d\u003csub\u003ej\u003c/sub\u003e represent the number of observed deaths at time t\u003csub\u003e(j),\u003c/sub\u003e j = 1\u0026hellip;. r. The Kaplan-Meier (K-M) estimator of the survival function S (t) is defined by:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\widehat{s\\left(t\\right)}=\\:\\prod\\:_{j:t{t}_{\\left(j\\right)\u0026lt;t}}(1-\\frac{{d}_{j}}{{r}_{j}})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis estimator is a step function, meaning that it only changes values at the time of each death [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e][\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eParametric Distributions for Survival Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor modeling the time until an event occurs, survival analysis frequently utilizes probability density functions (PDFs) such as Weibull, Exponential, Log-Logistic, Log-Normal, Gamma, and Gompertz distributions [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These distributions are frequently employed to model the time until an event occurs, each having distinct characteristics that make them suitable for different types of survival data [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e][\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eCox Regression Model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Cox Proportional Hazards (PH) model is widely used to assess the effects of covariates on hazard rates without requiring a specific form for the baseline hazard function [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e][\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:h\\left(t|\\varvec{x}\\right)={h}_{0}\\left(t\\right)\\:\\text{e}\\text{x}\\text{p}\\left({\\varvec{w}}^{{\\prime\\:}}\\varvec{\\gamma\\:}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn this equation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{0}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the baseline hazard function, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:w\\)\u003c/span\u003e\u003c/span\u003e is a vector of covariates, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\gamma\\:}\\)\u003c/span\u003e\u003c/span\u003e is the corresponding vector of regression coefficients. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{0}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e, can either have a specified parametric form or be left as an arbitrary nonnegative function. The semi-parametric Cox PH model is assumes an arbitrary (unspecified) nonnegative function, while parametric PH models\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{h}_{0}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e whereas parametric PH models [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] assume a parametric form for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{0}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e such as Weibull or exponential[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It is important to note that the assumption of proportional hazards is strong, and this assumption should be thoroughly checked [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. When this assumption is violated, the Accelerated Failure Time (AFT) family offers an attractive alternative to PH models [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e][\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.9.5 Joint Model\u003c/h2\u003e\u003cp\u003eThe standard approach in joint modeling involves two sub-models: a longitudinal model that handles measurement errors and missing data to estimate the true values of the time-dependent covariate, and a time-to-event model that uses these estimated values to quantify the relationship between the covariate and the time until the event occurs [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e][\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The key idea of joint modeling is to link the time-to-event model with the longitudinal model. In this approach, a linear mixed-effects model is typically used for the time-dependent covariate, representing the longitudinal response. This model accounts for random effects that capture subject-specific variations and incorporates measurement errors and missing data [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. On the other hand, a proportional hazards (PH) model is employed to analyze the association between the covariate and the time to the event [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{h}_{i}\\left(t\\:\\right|{M}_{i}\\left(t\\right))\\:={\\:h}_{0}(t\\left)\\:exp\\right\\{{\\gamma\\:{\\prime\\:}w}_{i}\\:+\\:\\sum\\:_{j=1}^{J}{{\\alpha\\:}_{j}m}_{ij}\\left(t\\right)\\},$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:{y}_{i}\\left(t\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:=\\:{m}_{i}\\left(t\\right)+\\:{\\epsilon\\:}_{i}\\left(t\\right)\\:\\:=\\:{x{\\prime\\:}}_{i}\\left(t\\right)\\beta\\:\\:+\\:{z{\\prime\\:}}_{i}\\:\\left(t\\right){b}_{i}\\:+\\:{\\epsilon\\:}_{i}\\left(t\\right),$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\:\\:{M}_{i}\\left(t\\right)\\:=\\:\\left\\{{m}_{i}\\right(s),\\:0\\:\\le\\:\\:s\\:\u0026lt;\\:t\\}\\)\u003c/span\u003e\u003c/span\u003e longitudinal history\u003c/p\u003e\u003cp\u003eα -quantifies the association between the time-varying covariate and the risk of an event and w\u003csub\u003ei\u003c/sub\u003e baseline covariates.\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\gamma\\:\\)\u003c/span\u003e\u003c/span\u003e -represents the vector of coefficients associated with covariates \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{w}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003e in the survival sub model.\u003c/p\u003e\u003cp\u003eJ -represents the total number of longitudinal measurements included in the model.\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{ϵ\\:}_{i}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e-represents the error term or random effects associated with the longitudinal sub model.\u003c/p\u003e\u003cp\u003eThis joint modeling framework combines these two components to capture the association between the longitudinal measurements and the survival time of outcome.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMaximum Likelihood Estimation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe maximum likelihood method is a commonly used approach for statistical inference [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e][\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Maximum likelihood estimation (MLE) has been the dominant method for parameter estimation in joint modeling, as it enables the simultaneous estimation of parameters from both the longitudinal and survival models. Due to the complexity of joint models, computational methods, such as Expectation-Maximization (EM) and Gauss-Hermite integration, have been developed to estimate parameters in the presence of unobserved data [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eBayesian Inference\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Bayesian approach differs from traditional frequentist approaches primarily in how probability is explained [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Bayesian methods offer an effective approach for dealing with limited data and model uncertainty. In Bayesian analysis, prior distributions represent prior knowledge about parameters before observing data. These priors are updated to produce posterior distributions using Bayes' theorem. Sampling techniques, such as Markov Chain Monte Carlo (MCMC), are used to approximate the posterior distribution and provide estimates of the parameters and their uncertainties. This method is particularly useful in complex models where traditional estimation methods may be inadequate [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e][\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e][\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo work with the posterior distribution, one common approach is to employ sampling techniques such as Markov chain Monte Carlo (MCMC) methods. By sampling values from the posterior distribution, we can approximate its behavior and compute sample statistics. The posterior density provides information about the parameter's behavior across a range of values within the parameter space. By generating a sample from the posterior distribution, we obtain a set of parameter values that reflect its uncertainty and variation. With this sample of values, we can estimate various quantities of interest, such as the posterior mean or median, standard deviation, and credible intervals. These estimates provide insights into the central tendency, dispersion, and uncertainty bounds of the parameter values based on the observed data.\u003c/p\u003e\u003cp\u003eIn Bayesian inference, Monte Carlo samples (generated by MCMC) drawn from the posterior distribution are used. MCMC algorithms such as the Gibbs sampler [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] are employed to obtain these samples[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Gibbs sampling has emerged as a valuable tool in Bayesian inference, especially when direct sampling from the joint distribution is challenging. Its versatility in handling complex models and providing approximate samples from the posterior distribution has made it widely embraced in diverse domains such as statistics, machine learning, and image analysis [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was granted by the Kotebe University of Education Ethical Review Committee. A formal approval letter was provided to Bishoftu General Hospital, where the hospital\u0026apos;s review committee granted permission to access maternal delivery follow-up data. As this study involved retrospective data collection, the requirement for informed consent was waived by the ethical review committee. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e"},{"header":"3.\tResults and Discussion","content":"\u003cp\u003eIn this section, descriptive and inferential statistics results are presented as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Descriptive Results\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 549 pregnant women who attended maternal delivery during the study period were included in the study. All participants were followed for one year, from January 1, 2023, to December 31, 2023, with each woman having at least two follow-up visits. A total of 2,760 systolic blood pressure (SBP) and diastolic blood pressure (DBP) measurements were recorded. Antenatal care was provided to the women throughout their pregnancies. The dataset collected is unbalanced, as not all subjects have the same number of observations. In addition to the longitudinal outcomes, several baseline covariates were recorded, and survival data is also available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eCross tabulation of preeclampsia status and other variables\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 160px;\"\u003e\n \u003cp\u003eVariable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 460px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cstrong\u003ePreeclampsia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eOutcome\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eCensored (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eEvent (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eTotal (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 160px;\"\u003e\n \u003cp\u003eMortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e484 (88.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e56 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e540 (98.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e5 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e4 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e9 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e489 (89.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e60 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e549 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 160px;\"\u003e\n \u003cp\u003eTermination of pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e20 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;33 (6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e53 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e469 (85.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e27 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e496 (90.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e489 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e60 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e549 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 160px;\"\u003e\n \u003cp\u003eBirth Outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eStill Birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e9 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e8 (1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e17 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAlive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e480 (87.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e52 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e532 (96.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e489 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e60 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e549 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 160px;\"\u003e\n \u003cp\u003eDevelop complications\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e467 (85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e36 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e503 (91.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e22 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e24 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;46 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e489 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e60 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e549 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 160px;\"\u003e\n \u003cp\u003eHistory preeclampsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;472 (86.0) \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e40 (7.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e512 (93.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e17 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e20 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e37 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e489 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e60 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e549 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 160px;\"\u003e\n \u003cp\u003ePrevious abortion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e475 (86.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e39 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e514 (93.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e14 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e21 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e35 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e489 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e60 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e549 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 160px;\"\u003e\n \u003cp\u003eFamily history of BP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e472 (85.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e32 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;504 (91.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e17 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e28 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e45 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e489 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e60 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e549 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 160px;\"\u003e\n \u003cp\u003eParity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNullipara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e189 (34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e45 (8.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e234 (42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cimg width=\"12\" height=\"21\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABIAAAAgCAMAAAAootjDAAAAAXNSR0IArs4c6QAAAEtQTFRFAAAAAAAAAAA6ADo6OgAAOjoAOjpmOmaQOma2ZjoAZjo6ZpC2ZrbbkNv/tmY6ttvbttv/27Zm27aQ29u229v/2////7Zm/9vb///bNpzaxgAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAT0lEQVQoU2NgGEJAjIlDBN25EoKszDyi6KLifCxs/Bg+E+bCYQA3hgFCnEy8yPpB1qCoAprFLoCkAt1GTHdhcz1RgS/GiAxQ3UWUAQOoCADBuwL4ESbo2wAAAABJRU5ErkJggg==\" alt=\"image\"\u003e1 para\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e300 (54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e15 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e315 (57.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e489 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e60 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e549 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 160px;\"\u003e\n \u003cp\u003eGravidity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eprim gravida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e174 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e38 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e212 (38.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eMultigravida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e315 (57.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e22 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e337 (61.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e489 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e60 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e549 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 160px;\"\u003e\n \u003cp\u003eAge grouped\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026lt; 30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e6 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e8 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cimg width=\"12\" height=\"21\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABIAAAAgCAMAAAAootjDAAAAAXNSR0IArs4c6QAAAEtQTFRFAAAAAAAAAAA6ADo6OgAAOjoAOjpmOmaQOma2ZjoAZjo6ZpC2ZrbbkNv/tmY6ttvbttv/27Zm27aQ29u229v/2////7Zm/9vb///bNpzaxgAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAT0lEQVQoU2NgGEJAjIlDBN25EoKszDyi6KLifCxs/Bg+E+bCYQA3hgFCnEy8yPpB1qCoAprFLoCkAt1GTHdhcz1RgS/GiAxQ3UWUAQOoCADBuwL4ESbo2wAAAABJRU5ErkJggg==\" alt=\"image\"\u003e\u0026nbsp;30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e483 (87.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e58 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e541 (98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e489 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e60 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e549 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 160px;\"\u003e\n \u003cp\u003ePregnant multiplicity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eSingleton\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e479 (87.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e57 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e536 (97.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTwin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e10 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e3 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e13 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e489 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e60 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e549 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 160px;\"\u003e\n \u003cp\u003eNumber of ANC visit\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026lt;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e6 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e8 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cimg width=\"12\" height=\"21\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABIAAAAgCAMAAAAootjDAAAAAXNSR0IArs4c6QAAAEtQTFRFAAAAAAAAAAA6ADo6OgAAOjoAOjpmOmaQOma2ZjoAZjo6ZpC2ZrbbkNv/tmY6ttvbttv/27Zm27aQ29u229v/2////7Zm/9vb///bNpzaxgAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAAAT0lEQVQoU2NgGEJAjIlDBN25EoKszDyi6KLifCxs/Bg+E+bCYQA3hgFCnEy8yPpB1qCoAprFLoCkAt1GTHdhcz1RgS/GiAxQ3UWUAQOoCADBuwL4ESbo2wAAAABJRU5ErkJggg==\" alt=\"image\"\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e483 (87.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;58 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e541 ( 98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e489 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e60 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e549 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 160px;\"\u003e\n \u003cp\u003eMode of Delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNormal delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e380 (69.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e46 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e426 (77.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eVacuum /Forceps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e12 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e3 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e15 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eC-section\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e97 (17.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e11 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e108 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e489 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e60 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e549 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 160px;\"\u003e\n \u003cp\u003eMode of onset labour\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eSpontaneous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e283 (51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;13 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e296 (53.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eInduced\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e206 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e47 (8.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e253 (46.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e489 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e60 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e549 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAs it shown in Table 1, among a total of 549 cases, 10.9% (60 cases) were diagnosed with preeclampsia. Out of those diagnosed with preeclampsia, 93.3% (56 cases) did not result in death, while 6.7% (4 cases) unfortunately resulted in fatalities. Regarding the termination of pregnancy, 10% (53 cases) underwent this procedure. Among the cases with preeclampsia, 55% (33 cases) opted for termination, while 45% (27 cases) did not pursue it. There were 17 cases (3.1%) of stillbirths among the total cases. Among those diagnosed with preeclampsia, 86.7% (52 cases) resulted in live births, while 13.3% (8 cases) unfortunately ended in stillbirths, indicating that preeclampsia accounted for 13.3% of the recorded stillbirths. Furthermore, 8.38% (46 cases) of pregnant women experienced complications. Among the cases with preeclampsia, 60% (36 cases) did not develop complications, while 40% (24 cases) did face additional complications.\u003c/p\u003e\n\u003cp\u003eThe result in Table 1 shows that History of Preeclampsia: Among the cases, 6.74% (37 cases) had a history of preeclampsia. Out of those with preeclampsia, 66.7% (40 cases) did not have a previous history of preeclampsia, while 33.3% (20 cases) had a history of preeclampsia. Previous Abortion: Among all the cases, 6.37% (35 cases) had a history of previous abortion. Within the preeclampsia cases, 65% (39 cases) did not have a history of previous abortion, while 35% (21 cases) had experienced previous abortions. Family History of High Blood Pressure: A total of 8.2% (45 cases) had a family history of high blood pressure. Among the cases with preeclampsia, 53.3% (32 cases) did not have a family history of high blood pressure, while 46.7% (28 cases) had a family history of high blood pressure. Parity (Number of Pregnancies): Among all the women, 42.6% (234 cases) were nulliparous (had no previous pregnancies), while 57.4% (315 cases) were multiparous (had previous pregnancies). In cases with preeclampsia, 75% (45 cases) were nulliparous, while 25% (15 cases) were multiparous. Gravidity (Number of Times Pregnant): Among all the women, 38.6% (212 cases) were prim-gravida (first-time pregnant), while 61.4% (337 cases) were multigravida (had been pregnant before). Among the cases with preeclampsia, 63.3% (38 cases) were prim-gravida, while 36.7% (22 cases) were multigravida. Regarding age: Among the cases with preeclampsia, 3.3% (2 women) were below 30 years of age, while 96.7% (58 women) were 30 years or older. Singleton vs. Twin Pregnancies: The majority of women, 97.6% (536 cases), had singleton pregnancies, while 2.4% (13 cases) had twin pregnancies. Among the cases with preeclampsia, 95% (57 cases) had singleton pregnancies, while 5% (3 cases) had twin pregnancies.\u003c/p\u003e\n\u003cp\u003eAs it shown in Table 1 antenatal care (ANC) follow-up: The majority of women, 98.5% (541 cases), had four or more ANC follow-ups, indicating regular prenatal care. Only 1.5% (8 cases) had fewer than four ANC follow-ups. \u0026nbsp;Among the cases with preeclampsia, 96.7% (58 cases) had four or more ANC follow-ups, while 3.3% (2 cases) had fewer than four ANC follow-ups. Mode of Delivery: The mode of delivery for pregnant women varied. Spontaneous normal deliveries accounted for the majority, with 77.6% (426 cases). Instrumental deliveries (vacuum/forceps) were relatively less common, representing 2.7% (15 cases), while cesarean section deliveries accounted for 19.67% (108 cases). Among the cases with preeclampsia, the distribution of delivery modes was as follows: 76.7% (46 cases) had spontaneous normal deliveries, 5% (3 cases) had vacuum-assisted deliveries, and 18.3% (11 cases) underwent Cesarean sections.\u003c/p\u003e\n\u003cp\u003eRegarding the mode of onset of labor: \u0026nbsp;Among all the women, \u0026nbsp;53.9% (296 cases) experienced spontaneous onset of labor, where labor began naturally without any external interventions. A majority of women, 46.7% (253 cases), had labor induced, indicating that medical interventions were employed to initiate labor. \u0026nbsp;Among the cases with preeclampsia, the distribution of labor onset was as follows: 21.7% (13 cases) experienced spontaneous labor onset. The majority, 78.3% (47 cases), had labor induced, indicating the need for medical intervention to initiate labor. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e\u0026nbsp; Complications developed during pregnancy\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eVariable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eCategory\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003ePreeclampsia \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eDeveloped complication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eLiver failure\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eRenal failure\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eDIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eHELLP syndrome\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAll above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eLiver and renal failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 among women who developed complication (46 cases), 8 individuals (8 cases) developed liver failure due to preeclampsia, while none of the cases without preeclampsia experienced liver failure. This suggests that liver failure is a complication specifically associated with preeclampsia. \u0026nbsp;One of the cases with preeclampsia had renal failure, while 2 cases without preeclampsia developed renal failure. \u0026nbsp;Disseminated Intravascular Coagulation (DIC): Among the cases with preeclampsia, 11 individuals (11 cases) developed DIC, while 13 cases without preeclampsia experienced DIC. Both groups showed instances of DIC, indicating that this complication can occur in both preeclampsia and non-preeclampsia cases. HELLP Syndrome: Two cases with preeclampsia developed HELLP syndrome, while none of the cases without preeclampsia had this complication. This suggests that HELLP syndrome is specifically associated with preeclampsia. Combined Complications: Among all the cases, 3 individuals (3 cases) experienced all the complications mentioned above (liver, renal failure, DIC and HELPP syndrome). \u0026nbsp;One case occurred in the group without preeclampsia, while two cases were observed in the preeclampsia group. Liver and Renal Failure: Six cases in total (4 cases with preeclampsia and 2 cases without preeclampsia) developed both liver and renal failure. This indicates that the occurrence of both liver and renal failure is associated to preeclampsia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e: Summary of continuous variables\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003evariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eMaternal Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e27.68 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;5.44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e17 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;45 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eWeight in Kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e27.68 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eGA at admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e38.81 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2.32\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e28 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e42 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eTime Visit \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e26.39 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;8.28 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e4 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;42 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e114.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;13.40 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e94\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e190 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e68.54\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e12.03 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e124\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResult in Table 3 shows that the average age is approximately 27.68 with a standard deviation of approximately 5.44. The median age is 28, which means that half of the values are greater than or equal to 28, and half are less than or equal to 28. \u0026nbsp;The average weight is approximately 27.68 with a standard deviation of approximately 5.44. The average gestational age is approximately 38.81 with a standard deviation of approximately 2.32. The average time of visit is approximately 26.39 with a standard deviation of approximately 8.28.\u003c/p\u003e\n\u003cp\u003eThe average systolic blood pressure is approximately 114.04 with a standard deviation of approximately 13.40. \u0026nbsp;The median systolic blood pressure is 111, indicating that half of the values are greater than or equal to 111, and half are less than or equal to 111. The average diastolic blood pressure is approximately 68.54 with a standard deviation of approximately 12.03. The median diastolic blood pressure is 67, indicating that half of the values are greater than or equal to 67, and half are less than or equal to 67. \u0026nbsp; \u0026nbsp; Next, survival probability of developing preeclampsia is estimated using Kaplan Meier plot.\u003c/p\u003e\n\u003cp\u003eKaplan Meier plot in Figure 1 shows that probability of developing preeclampsia over time in weeks, indicating the risk of developing preeclampsia is increase as time increase. It appears that multigravida pregnancies have a higher survival probability in terms of developing preeclampsia compared to prim-gravida pregnancies. This suggests that individuals with multiple pregnancies (multigravida) have a relatively lower risk of developing preeclampsia compared to those with a single pregnancy (prim-gravida). \u0026nbsp;The Kaplan Meier plot showed that pregnant women who are less than 30 years old have a higher survival probability in terms of developing preeclampsia. On the other hand, pregnant women aged 30 years or more seem to have a higher risk of developing preeclampsia compared to those who are younger than 30 years old. Figure 1 revealed that pregnant women who have no family history of blood pressure have a higher survival probability. \u0026nbsp;Conversely, pregnant women who have a family history of blood pressure problems are at a higher risk of developing preeclampsia compared to those without a family history of the condition (Figure 1).\u003c/p\u003e\n\u003cp\u003eFigure 2 shows the observed longitudinal measures plotted against time in weeks for the 549 pregnant women included in the analysis. The heterogeneity of the pregnant women systolic blood pressure (SBP) diastolic blood pressure (DBP) is apparent in this plot (see Figure 2). The smooth curve represents the average trajectory. This indicates that a random effect on the average score might be adequate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Inferential results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEnsuring the normality of residuals and random effects in linear mixed models is fundamental for achieving robust and reliable statistical inferences. The normality assumption was assessed using Q-Q plots, which demonstrated that the residuals and random effects follow a normal distribution. This adherence enhances the model\u0026apos;s ability to capture the underlying patterns in the data accurately. Consequently, it improves model fit, leading to more reliable predictions and well-calibrated prediction intervals, thereby strengthening the validity of the analysis (Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostics of the Cox PH model:\u0026nbsp;\u003c/strong\u003eThe assumptions of the cox PH model are checked by employing log-Minus-Log survival curves, Schoenfeld Residuals plot (see figure 4-5).\u003c/p\u003e\n\u003cp\u003eThe log-minus-log survival curve is parallel. Parallel curves indicate that the hazard ratios remain constant over time, supporting the assumption (Figure 4).\u003c/p\u003e\n\u003cp\u003eThe figure 5, showed the residuals display a random scatter around zero, it indicates that the effect of the predictor variables on the hazard rate remains constant over time. This is consistent with the proportional hazards assumption, which assumes that the hazard ratios for the predictor variables are constant over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariable Selection for Sub-models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVariable selection in joint models involves selecting the covariates to include in the sub-models for both the longitudinal and time-to-event components. Stepwise Selection: The stepwise selection methods, such as forward selection or backward elimination, to iteratively add or remove covariates from the sub-models. These methods evaluate the significance of covariates based on statistical criteria, such as Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC). \u0026nbsp;As candidate, Age, weight, gravidity, parity, family history of blood pressure, previous abortion, History of preeclampsia, number of ANC visit, pregnant multiplicity, and gestational age (GA) variables are considered for each models. Hence, Age, gravidity, \u0026nbsp;family history of blood pressure, previous abortion, number of ANC visit and pregnant multiplicity variables are selected for Cox Ph model using stepwise method. Besides, Age, Weight, family history of blood pressure, and previous abortion variables are selected for longitudinal sub-model. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostics of Bayesian Joint Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrace plots (Figure 6) for parameters of the longitudinal and survival sub-models show rapid oscillations without trends, indicating efficient mixing and swift convergence of the Markov chains. Density plots display smooth, unimodal distributions, confirming accurate sampling from the target distribution. The Gelman-Rubin statistic (\u003cimg width=\"44\" height=\"23\" src=\"data:image/png;base64,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\" alt=\"image\"\u003efor all parameters is close to 1, further validating convergence and reliability of the results (see Table 4). \u0026nbsp; These diagnostics confirm the robustness of the Bayesian joint model implemented using the JMbayes2 package in R software. \u0026nbsp;This confirms that the Markov chains exhibit strong convergence and good mixing, ensuring the reliability of the results.\u003c/p\u003e\n\u003cp\u003eWe provide the posterior summaries of all the parameters in the Bayesian joint model of interest in Table 4. The results of the Bayesian joint Cox model for the longitudinal SBP process provide valuable insights into the factors influencing SBP: The estimated overall mean SBP is 101.5 mmHg, with a 95% credible interval of (97.06, 105.90), indicating a statistically significant baseline measurement. For each additional week of follow-up, SBP increases by an average of 0.56 mmHg (95% credible interval: 0.51, 0.63), demonstrating a significant and consistent upward trend in SBP over time. Participants\u0026apos; age shows a significant positive effect on SBP, with a mean increase of 0.16 mmHg per year (95% credible interval: 0.05, 0.26). Older individuals are more likely to have higher SBP. For every additional kilogram of body weight, SBP increases by 0.10 mmHg (95% credible interval: 0.04, 0.17), suggesting that higher body weight is associated with elevated SBP. Participants with a family history of hypertension exhibit significantly higher SBP levels, with an average increase of 2.24 mmHg (95% credible interval: 0.16, 4.33) compared to those without a family history. A history of previous abortion is associated with a mean increase in SBP of 2.92 mmHg (95% credible interval: 0.51, 5.29), indicating a noteworthy impact on SBP levels. All covariates have credible intervals that exclude 0, underscoring their robust effects on SBP dynamics, as shown in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e Bayesian joint multivariate longitudinal and survival time model\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"21\" valign=\"top\" style=\"width: 643px;\"\u003e\n \u003cp\u003eRandom-effects covariance matrix: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"20\" valign=\"top\" style=\"width: 529px;\"\u003e\n \u003cp\u003e\u0026nbsp;StdDev \u0026nbsp; \u0026nbsp;Corr \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e(Intr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e10.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e(Intr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003e(Intr)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTime (in weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.5746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.9614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e(Intr) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e14.2183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.0127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.6057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.3166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.3473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 315px;\"\u003e\n \u003cp\u003e-0.9299\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"21\" valign=\"top\" style=\"width: 643px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvival Outcome: Time to develop preeclampsia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eStDev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e97.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eRhat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eAge (30 and more years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.7436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.4140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.6043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;\u0026lt; 30 years(ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eGravidity (Multigravida)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.6606\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.3264\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-1.3165\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.0276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.0062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;prim gravida (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eFamily history BP(Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.9740\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.3683 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.2686 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.6935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0056\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.0261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cstrong\u003e\u0026nbsp;NO(ref)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003ePrevious abortion(yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.0776\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.3744 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.3383 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.8207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0034\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.0206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cstrong\u003eNo (ref.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eNumber ANC (less than 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.7430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.8916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.8062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2 .6709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.4033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; 4 and more times(ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003ePregnant Multiplicity (single)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.6960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-1.2383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.5067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.6700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.0017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Twin(ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eSBP\u0026nbsp;(\u0026alpha;\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0635\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0277 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0073 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.1196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.1247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eDBP\u0026nbsp;(\u0026alpha;\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0739\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0294 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0183 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.1366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0071\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.0269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"21\" valign=\"top\" style=\"width: 643px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLongitudinal Outcome: Systolic blood pressure (SBP)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eStDev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e97.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eRhat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e101.5061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2.2586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e97.0620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e105.9024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.0106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eTime (in weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.5693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.0314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.5080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.6307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.1294\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eWomen\u0026rsquo;s Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.1604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.0540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.2641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.0033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.0227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eWomen\u0026rsquo;s Weight in kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.1067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.0311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.1666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.0153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eFamily history of BP (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.2436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1.0711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.1592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e4.3321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.0373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.0023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;No (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003ePrevious abortion (yes)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.9202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1.2316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.5164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e5.2882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.0180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cstrong\u003eNo (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eSigma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e9.5841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.1499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e9.2953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e9.8741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.0255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"21\" valign=\"top\" style=\"width: 643px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLongitudinal Outcome: Diastolic blood pressure (DBP)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eStDev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e2.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e97.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eRhat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e52.6364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2.1275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e48.4890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e56.8711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.0180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eTime (in weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.5148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.0323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.4518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.5774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.0236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eAge (in weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.0415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.0480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.0524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.1354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.3813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.0316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eWeight in kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.0128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.0290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.0430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.6583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.0284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eFamily history\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eof BP (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e2.1873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.9380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.3677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3.9944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.0190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.0050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePrevious\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eabortion(yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1.0785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1.0709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-1.0540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3.1635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.3207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo (ref.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSigma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e7.9163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.1271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e7.6670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e8.1683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.0066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe analysis was performed on a dataset with 549 groups and 60 events (10.9% event rate). The longitudinal outcomes systolic blood pressure (SBP) and diastolic blood pressure (DBP) have 2760 observations each. The estimated standard deviations of the random effect component indicate the amount of variability between individuals for the intercept and time random effects. The intercept random effect has a standard deviation of 10.1, suggesting substantial variability in the initial values among individuals. The time random effect has a standard deviation of 0.57, indicating relatively less variability in the rates of change over time in the systolic blood pressure outcome model. \u0026nbsp; \u0026nbsp;Relationship between intercept and time effects: The correlation between the intercept and time random effects are estimated to be -0.96. This negative correlation suggests that individuals with higher baseline values (intercept) tend to have slower rates of change over time. In other words, individuals who start with higher values have a tendency to experience smaller changes over the observed time period. The correlation between the intercept random effects is estimated to be 0.034, indicating a weak correlation between the initial values (intercepts) of different individuals. This suggests that the initial values of individuals are relatively independent of each other (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The intercept random effect of DBP outcome has a standard deviation of 14.2, suggesting substantial variability in the initial values among individuals. The time random effect has a standard deviation of 0.60 indicating relatively less variability in the rates of change over time in diastolic blood pressure outcome model. Relationship between time effects: The correlation between time random effects is estimated to be -0.93. This negative correlation indicates a relationship between the rates of change over time among individuals. \u0026nbsp;Individuals with faster rates of change tend to have slower rates of change over time, and vice versa. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Moreover, like joint model, maternal with family history of blood pressure\u0026quot; indicates that pregnant women with a family history of blood pressure issues tend to have higher systolic blood pressure measurements compared to pregnant women without a family history of blood pressure issues. The odds ratio would be approximately 9.39 (exp (2.24)). This means that pregnant women with a family history of blood pressure have approximately 9.39 times higher odds of having higher systolic blood pressure compared to pregnant women without a family history of blood pressure. This suggests that family history plays a role in influencing the systolic blood pressure values of pregnant women (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe systolic blood pressure (SBP) is measured in millimeters of mercury (mmHg), a coefficient of 2.92 would suggest that pregnant women with a previous abortion have, on average, a 2.92 mmHg higher SBP compared to pregnant women\u0026rsquo;s without a previous abortion, adjusting for other factors in the model. The coefficient of 0.56 indicates the change in the outcome variable (SBP) associated with a one-unit increase in the predictor variable (maternal age in years) while holding other variables constant. Similarly, the coefficient of 0.16 indicates the change in the outcome variable (SBP) associated with a one-unit increase in the predictor variable (maternal weight in kg) while holding other variables constant.\u003c/p\u003e\n\u003cp\u003eThe findings from the diastolic blood pressure (DBP) longitudinal analysis using the Bayesian joint log-normal Accelerated Failure Time (AFT) model reveal that the overall mean for the longitudinal process is statistically significant, with an estimate of 52.63 and a 95% credible interval ranging from 48.49 to 56.87. Among the covariates examined, follow-up time in weeks significantly influenced DBP, with an estimate of 0.51 and a 95% credible interval of (0.45, 0.57), excluding 0. Additionally, a family history of blood pressure emerged as a significant factor, with an estimate of 2.18 and a 95% credible interval of (0.36, 3.99), also excluding 0. The analysis suggests that pregnant women with a family history of blood pressure issues tend to experience higher diastolic blood pressure compared to those without such a history. The odds ratio for this association is approximately 8.9 (exp(2.18)), indicating that women with a family history of blood pressure are about 8.9 times more likely to have elevated DBP than those without a family history. This highlights the significant impact of family history on the diastolic blood pressure levels of pregnant women (Table 4).\u003c/p\u003e\n\u003cp\u003eThe results from the survival analysis indicate several significant factors influencing the occurrence of preeclampsia onset. Maternal age, specifically being under 30 years, shows a significant effect, with an estimate of 0.77 and a 95% credible interval of (0.05, 1.60), excluding 0. This suggests that pregnant women under 30 years have lower odds of developing preeclampsia, with an odds ratio of approximately 2.16 (exp(0.77)). In other words, younger women are 2.16 times less likely to develop preeclampsia compared to women aged 30 years or older, controlling for other covariates.\u003c/p\u003e\n\u003cp\u003eA family history of blood pressure also plays a crucial role, with an estimate of 1.01 and a 95% credible interval of (0.26, 1.78), excluding 0. The odds ratio for this factor is approximately 2.73 (exp(1.01)), indicating that pregnant women with a family history of blood pressure issues are 2.73 times more likely to develop preeclampsia compared to those without such a family history. The presence of a previous abortion is another significant factor, with an estimate of 1.1 and a 95% credible interval of (0.33, 1.88), excluding 0. This corresponds to an odds ratio of approximately 3.01 (exp(1.1)), suggesting that women with a previous abortion have 3.01 times higher odds of developing preeclampsia compared to those without a prior abortion. Additionally, the analysis shows that primigravida women (those pregnant for the first time) have a higher relative risk of developing preeclampsia compared to multigravida women (those pregnant more than once). The odds ratio for this factor is 0.52 (exp(-0.64)), meaning that multigravida women have about 0.52 times the odds of developing preeclampsia compared to primigravida women after controlling for other variables. This suggests a lower likelihood of developing preeclampsia among women with previous pregnancies.\u003c/p\u003e\n\u003cp\u003eThese results indicate that maternal age, gravidity, previous abortion and family history of blood pressure are associated with the occurrence of onset preeclampsia. However, the effects of multiplicity of the pregnant and number of antenatal care (ANC) do not reach statistical significance in predicting the occurrence of onset preeclampsia in this analysis.\u003c/p\u003e\n\u003cp\u003eThe joint Cox proportional hazards (Cox PH) model analysis of time-to-event and a longitudinal process reveals a strong and statistically significant association between both systolic blood pressure (SBP) and diastolic blood pressure (DBP) as longitudinal processes, and the survival process (the relative risk of developing preeclampsia). The 95% credible interval for the association between SBP and the risk of developing preeclampsia does not include zero, with a credible interval of (0.007, 0.119), indicating that this relationship is statistically significant. Furthermore, the association parameter (\u0026alpha;) for SBP suggests that each one-unit increase in SBP is associated with a 6.5% increase in the relative risk of developing preeclampsia (exp (0.0635) = 1.065), after controlling for other covariates. Similarly, the association between DBP and preeclampsia onset is also statistically significant, as indicated by the 95% credible interval of (0.018, 0.136), which excludes zero. The corresponding association parameter (\u0026alpha;) for DBP indicates that each one-unit increase in DBP is associated with an 7.7% increase in the relative risk of preeclampsia (exp (0.074) = 1.077), controlling for other factors (Table 4).\u003c/p\u003e\n\u003cp\u003eThese findings underline a clear positive association between both SBP and DBP with the relative risk of developing preeclampsia. Specifically, higher measurements of both systolic and diastolic blood pressure are linked to a significantly increased risk of preeclampsia, emphasizing the importance of monitoring blood pressure levels in pregnant women for early identification of those at higher risk for this condition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Discussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe aim of this study is to employ Bayesian joint modeling to analyze the relationship between blood pressure and the occurrence of preeclampsia, a risky health outcome, among pregnant women attending delivery services at Bishoftu General Hospital in Central Oromia region, Ethiopia. Out of a total of 549 pregnant women who attended maternal delivery at Bishoftu Hospital, 10.9% were diagnosed with preeclampsia. Among those diagnosed with preeclampsia, 93.3% did not result in death, while 6.7% unfortunately resulted in fatalities. \u0026nbsp;The individual profile plots of the blood pressure (BP) measurements indicate variability within and between individuals, while the exploratory analysis suggests a linear pattern of increase in BP over time on average.\u003c/p\u003e\n\u003cp\u003eThe linear mixed model (LMM), identified that follow up time, weight, gravidity, age, family history of blood pressure and previous abortion were found to be significant at 5% significance level. Our findings are similar to the study by [45], [46]. Specifically, our study revealed that BP tends to increase with higher baseline age. This observation is consistent with the findings of a study conducted by [47] and [46], which also reported age at baseline as a significant predictor for BP in pregnant women. The results of this study reveal a correlation of 0.71 between the two blood pressure measurements, diastolic blood pressure (DBP) and systolic blood pressure (SBP). This finding provides direct evidence that both SBP and DBP tend to decrease at the beginning of pregnancy and gradually increase throughout the course of pregnancy. These findings align with previous studies, such as the study conducted by\u0026nbsp;[46]\u0026nbsp;and\u0026nbsp;[47], which also supported the observation that SBP and DBP decrease early in pregnancy and show an upward trend as pregnancy progresses.\u003c/p\u003e\n\u003cp\u003eThe findings from your Cox regression model indicate that several risk factors are associated with the development of preeclampsia. These risk factors include the age of pregnant women, gravidity, family history of blood pressure, and previous history of abortion. Specifically, the study found a significant association between maternal age and the risk of preeclampsia. Older women were found to have a higher risk of developing preeclampsia compared to younger women. This aligns with previous researches conducted by [48] and \u0026nbsp;[49] have consistently demonstrated a link between maternal age and the risk of preeclampsia. Furthermore, a woman with a family history of blood pressure was found to have a significantly higher risk and hazard of developing preeclampsia during the follow-up period. This suggests that having a family history of blood pressure of pregnancy increases the likelihood of developing preeclampsia as compared those women who haven\u0026rsquo;t. This finding is aligns with researches conducted in Brazil [50], Sudan [51], Pakistan [52] and Uganda [53].\u003c/p\u003e\n\u003cp\u003eThe findings of our study indicate that women in their first pregnancy status (primi gravida) were more likely to develop preeclampsia as multigravida. The odds of developing preeclampsia in primi gravida women were found to be 0.52 times higher compared to multigravida women (women who have had multiple pregnancies). \u0026nbsp;This finding is consistent with studies conducted by [54] in Gaza Strip, [55] in Ethiopia, and [56] in Egypt. \u0026nbsp;These studies also reported that primi gravida is a risk factor for preeclampsia. \u0026nbsp;This study indicated that a history of abortion perceived (prior induced abortions) was positively associated with development of preeclampsia. Women who had a history of abortion perceived were found to be more likely to develop preeclampsia compared to those without history of abortion. This finding is consistent with studies conducted in India by [57] and Iran by [58]. These studies also reported a positive association between a history of abortion perceived and the risk of developing preeclampsia.\u003c/p\u003e\n\u003cp\u003eFurthermore in our joint modeling analysis, we observed significant association parameters (\u0026alpha;) between two outcomes. The estimated association parameters were found to be 0.06 and 0.07, indicating a positive association between the true values of systolic blood pressure (SBP) and diastolic blood pressure (DBP) with the risk of preeclampsia over a follow-up period. These findings are consistent with a studies conducted by [46] and [59].\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study highlights the critical importance of proactive screening for preeclampsia in pregnant women, as 10.9% of them are affected by preeclampsia. The high mortality rate (6.7%) among women with preeclampsia, the occurrence of additional complications (40%), and the incidence of stillbirths (13.3%) among pregnant women with preeclampsia are significant findings.\u003c/p\u003e\n\u003cp\u003eIn this study, a comprehensive analysis was conducted to examine the relationship between longitudinal biomarker measurements and the occurrence of preeclampsia. By considering both longitudinal outcome and time-to-event data, we identified several factors associated with the risk of developing preeclampsia, including a history of previous abortion, a family history of blood pressure problems, maternal age of 30 years or older, and gravidity. The study reveals important insights into the risk of developing preeclampsia over time. Multigravida pregnancies exhibit a higher survival probability compared to prim gravida pregnancies, indicating a relatively lower risk of preeclampsia in individuals with multiple pregnancies. \u0026nbsp;Pregnant women below the age of 30 have a higher survival probability, while those aged 30 years or older face an increased risk of preeclampsia. Additionally, pregnant women without a family history of blood pressure problems have a higher survival probability, whereas those with a family history face a heightened risk of preeclampsia.\u003c/p\u003e\n\u003cp\u003ePregnant women who had repeated measurements of systolic blood pressure (SBP) and diastolic blood pressure (DBP) over time during their pregnancy were found to have an association with the risk of developing preeclampsia. The findings of this study suggest that blood pressure measurements, specifically SBP and DBP, taken repeatedly during pregnancy, can serve as important indicators of the risk of developing preeclampsia. Higher baseline blood pressure measurements may indicate an increased predisposition to preeclampsia, while lower baseline blood pressure measurements may indicate a lower risk. Monitoring blood pressure, specifically SBP and DBP, throughout pregnancy can provide important indicators for assessing the risk of developing preeclampsia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to the Bishoftu General Hospital for their support and cooperation in this study. Special thanks to the staff at Bishoftu General Hospital for facilitating access to the maternal delivery follow-up data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMAA conceived the study, designed the methodology, and also contributed to the development of the data collection protocol, performed the statistical analysis, and drafted the manuscript. ATG assisted with the literature review, data interpretation, and critical revision of the manuscript for intellectual content. All authors (MAA and ATG) reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhou, B. et al. Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19\u0026middot; 1 million participants. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e389\u003c/b\u003e (10064), 37\u0026ndash;55 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoberts, J. M., Pearson, G., Cutler, J. \u0026amp; Lindheimer, M. Summary of the NHLBI working group on research on hypertension during pregnancy. \u003cem\u003eHypertension\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e (3), 437\u0026ndash;445 (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhan, K. S., Wojdyla, D., Say, L., G\u0026uuml;lmezoglu, A. M. \u0026amp; Van Look, P. F. A. WHO analysis of causes of maternal death: a systematic review. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e367\u003c/b\u003e (9516), 1066\u0026ndash;1074 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEzzati, M., Lopez, A. D., Rodgers, A., Vander Hoorn, S. \u0026amp; Murray, C. J. L. Selected major risk factors and global and regional burden of disease. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e360\u003c/b\u003e (9343), 1347\u0026ndash;1360 (2002).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKearney, P. M. et al. Global burden of hypertension: analysis of worldwide data. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e365\u003c/b\u003e (9455), 217\u0026ndash;223 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhulmiyyah, L. M. \u0026amp; Sibai, B. M. Gestational Hypertension, Preeclampsia, and Eclampsia, \u003cem\u003eQueenan\u0026rsquo;s Manag. High-Risk Pregnancy An Evidence‐Based Approach\u003c/em\u003e, pp. 280\u0026ndash;288, (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChoudhury, M. \u0026amp; Friedman, J. E. Epigenetics and microRNAs in preeclampsia. \u003cem\u003eClin. Exp. Hypertens.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e (5), 334\u0026ndash;341 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrganization, W. H. \u003cem\u003eWHO recommendations: policy of interventionist versus expectant management of severe pre-eclampsia before term\u003c/em\u003e (World Health Organization, 2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGaym, A. et al. Disease burden due to pre-eclampsia/eclampsia and the Ethiopian health system\u0026rsquo;s response. \u003cem\u003eInt. J. Gynecol. Obstet.\u003c/em\u003e \u003cb\u003e115\u003c/b\u003e (1), 112\u0026ndash;116 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFiroz, T., Sanghvi, H., Merialdi, M. \u0026amp; von Dadelszen, P. Pre-eclampsia in low and middle income countries. \u003cem\u003eBest Pract. Res. Clin. Obstet. Gynaecol.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e (4), 537\u0026ndash;548 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsigas, E. Advocacy is essential to supporting women with pre-eclampsia. \u003cem\u003eObstet. Med.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (1), 33\u0026ndash;35 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePauli, J. M. \u0026amp; Repke, J. T. Preeclampsia: short-term and long-term implications. \u003cem\u003eObstet. Gynecol. Clin.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (2), 299\u0026ndash;313 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePhipps, E., Prasanna, D., Brima, W. \u0026amp; Jim, B. Preeclampsia: updates in pathogenesis, definitions, and guidelines. \u003cem\u003eClin. J. Am. Soc. Nephrol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (6), 1102\u0026ndash;1113 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChung, S. S. et al. \u003cem\u003eDemographic and Health Survey ethiopia\u003c/em\u003e. (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/ipfa.2004.1345625\u003c/span\u003e\u003cspan address=\"10.1109/ipfa.2004.1345625\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerhe, A. K., Kassa, G. M., Fekadu, G. A. \u0026amp; Muche, A. A. Prevalence of hypertensive disorders of pregnancy in Ethiopia: a systemic review and meta-analysis. \u003cem\u003eBMC Pregnancy Childbirth\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e, 1\u0026ndash;11 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNgwenya, S. Severe preeclampsia and eclampsia: incidence, complications, and perinatal outcomes at a low-resource setting, Mpilo Central Hospital, Bulawayo, Zimbabwe. \u003cem\u003eInt. J. Womens Health\u003c/em\u003e, pp. 353\u0026ndash;357, (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKahsay, H. B., Gashe, F. E. \u0026amp; Ayele, W. M. Patterns of hypertensive disorders of pregnancy in selected hospitals of Tigray, Ethiopia. \u003cem\u003eWorld Health\u003c/em\u003e. \u003cb\u003e1\u003c/b\u003e, 4\u0026ndash;6 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKongwattanakul, K., Saksiriwuttho, P., Chaiyarach, S. \u0026amp; Thepsuthammarat, K. Incidence, characteristics, maternal complications, and perinatal outcomes associated with preeclampsia with severe features and HELLP syndrome. \u003cem\u003eInt. J. Womens Health\u003c/em\u003e, pp. 371\u0026ndash;377, (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCreswell, J. W. Research Design: Qualitative, QuCreswell, JW. Research design Qualitative quantitative and mixed methods approaches. 2014. (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRiley, R. D. et al. Minimum sample size for developing a multivariable prediction model: PART II-binary and time‐to‐event outcomes. \u003cem\u003eStat. Med.\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e (7), 1276\u0026ndash;1296 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBasharat, N. \u003cem\u003eJoint analysis of longitudinal and time to event data using accelerated failure time models\u003c/em\u003e (A Bayesian approach. University of Saskatchewan, 2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLawless, J. F. \u003cem\u003eStatistical models and methods for lifetime data\u003c/em\u003e (Wiley, 2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAalen, O., Borgan, O. \u0026amp; Gjessing, H. \u003cem\u003eSurvival and event history analysis: a process point of view\u003c/em\u003e (Springer Science \u0026amp; Business Media, 2008).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKlein, J. P. \u0026amp; Moeschberger, M. L. \u003cem\u003eSurvival analysis: techniques for censored and truncated data\u003c/em\u003e (Springer Science \u0026amp; Business Media, 2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaplan, E. L. \u0026amp; Meier, P. Nonparametric estimation from incomplete observations. \u003cem\u003eJ. Am. Stat. Assoc.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e (282), 457\u0026ndash;481 (1958).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCollett, D. \u003cem\u003eModelling survival data in medical research\u003c/em\u003e (Chapman and Hall/CRC, 2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCox, D. R. Regression models and life-tables. \u003cem\u003eJ. R Stat. Soc. Ser. B\u003c/em\u003e. \u003cb\u003e34\u003c/b\u003e (2), 187\u0026ndash;202 (1972).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCollett, D. \u003cem\u003eModelling Survival Data in Medical Research\u003c/em\u003e. in Chapman \u0026amp; Hall/CRC Texts in Statistical Science. CRC Press, [Online]. (2015). Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://books.google.com.et/books?id=Okf7CAAAQBAJ\u003c/span\u003e\u003cspan address=\"https://books.google.com.et/books?id=Okf7CAAAQBAJ\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei, L. J. The accelerated failure time model: a useful alternative to the Cox regression model in survival analysis. \u003cem\u003eStat. Med.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 14\u0026ndash;15 (1992).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eErango, M. A., Goshu, A. T., Buta, G. B. \u0026amp; Dessisoa, A. H. Bayesian joint modelling of survival of HIV/AIDS patients using accelerated failure time data and longitudinal CD4 cell counts. \u003cem\u003eBr. J. Med. Med. Res.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (6), 1\u0026ndash;12 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRizopoulos, D. \u003cem\u003eJoint models for longitudinal and time-to-event data: With applications in R\u003c/em\u003e (CRC, 2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eButa, G. B., Goshu, A. T. \u0026amp; Worku, H. M. Bayesian joint modelling of disease progression marker and time to death event of HIV/AIDS patients under ART follow-up. \u003cem\u003eBr. J. Med. Med. Res.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (8), 1034\u0026ndash;1043 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo, X. \u0026amp; Carlin, B. P. Separate and joint modeling of longitudinal and event time data using standard computer packages. \u003cem\u003eAm. Stat.\u003c/em\u003e \u003cb\u003e58\u003c/b\u003e (1), 16\u0026ndash;24 (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcCrink, L. M., Marshall, A. H. \u0026amp; Cairns, K. J. Advances in joint modelling: a review of recent developments with application to the survival of end stage renal disease patients. \u003cem\u003eInt. Stat. Rev.\u003c/em\u003e \u003cb\u003e81\u003c/b\u003e (2), 249\u0026ndash;269 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchluchter, M. D. Methods for the analysis of informatively censored longitudinal data. \u003cem\u003eStat. Med.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 14\u0026ndash;15 (1992).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDempster, A. P., Laird, N. M. \u0026amp; Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. \u003cem\u003eJ. R Stat. Soc. Ser. B\u003c/em\u003e, \u003cb\u003e39\u003c/b\u003e, 1, pp. 1\u0026ndash;22, 1977.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBolstad, W. M. \u0026amp; Curran, J. M. \u003cem\u003eIntroduction to Bayesian statistics\u003c/em\u003e (Wiley, 2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoldgiv, B. \u0026amp; Bolstad, M. Introduction to Bayesian Statistics by William 354 pages, ISBN 0-471-27020-2, A John Wiley and Sons, Inc., hardcover, US \u003cspan\u003e$\u003c/span\u003e84.95., \u003cem\u003eMong. J. Biol. Sci.\u003c/em\u003e, vol. 2, no. 2, pp. 77\u0026ndash;78, 2004. (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlasco, A. \u003cem\u003eThe Bayesian Choice\u003c/em\u003e. (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-319-54274-4_2\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-54274-4_2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan de Schoot, R. et al. A gentle introduction to Bayesian analysis: Applications to developmental research. \u003cem\u003eChild. Dev.\u003c/em\u003e \u003cb\u003e85\u003c/b\u003e (3), 842\u0026ndash;860 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiebolt, J. \u0026amp; Robert, C. P. Estimation of finite mixture distributions through Bayesian sampling. \u003cem\u003eJ. R Stat. Soc. Ser. B\u003c/em\u003e. \u003cb\u003e56\u003c/b\u003e (2), 363\u0026ndash;375 (1994).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGivens, G. H. \u0026amp; Hoeting, J. A. \u003cem\u003eComputational Statistics\u003c/em\u003e. in Wiley Series in Probability and Statistics. Wiley, [Online]. (2005). Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://books.google.com.et/books?id=ByDvAAAAMAAJ\u003c/span\u003e\u003cspan address=\"https://books.google.com.et/books?id=ByDvAAAAMAAJ\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao, X., Yu, C. \u0026amp; Tong, H. A Bayesian approach to weibull survival model for clinical randomized censoring trial based on MCMC simulation, in \u003cem\u003e2nd International Conference on Bioinformatics and Biomedical Engineering\u003c/em\u003e, IEEE, 2008, pp. 1181\u0026ndash;1184. (2008).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeman, S. \u0026amp; Geman, D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. \u003cem\u003eIEEE Trans. Pattern Anal. Mach. Intell.\u003c/em\u003e, 6, pp. 721\u0026ndash;741, (1984).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFarias, D. R. et al. Total cholesterol and leptin concentrations are associated with prospective changes in systemic blood pressure in healthy pregnant women. \u003cem\u003eJ. Hypertens.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e (1), 127\u0026ndash;134 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaile, D. B., Aguade, A. E. \u0026amp; Fetene, M. Z. Joint modeling of hypertension measurements and time-to-onset of preeclampsia among pregnant women attending antenatal care service at Arerti Primary Hospital, North Shoa, Ethiopia. \u003cem\u003eCogent Public. Heal\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e (1), 2022846 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMo, X. B., Lei, S. F., Zhang, Y. H. \u0026amp; Zhang, H. Examination of the associations between m6A-associated single-nucleotide polymorphisms and blood pressure. \u003cem\u003eHypertens. Res.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (10), 1582\u0026ndash;1589 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKashanian, M., Baradaran, H. R., Bahasadri, S. \u0026amp; Alimohammadi, R. Risk factors for pre-eclampsia: a study in Iran. \u003cem\u003eArch. Iran. Med.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (6), 412\u0026ndash;415 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLamminp\u0026auml;\u0026auml;, R., Vehvil\u0026auml;inen-Julkunen, K., Gissler, M. \u0026amp; Heinonen, S. Preeclampsia complicated by advanced maternal age: a registry-based study on primiparous women in Finland 1997\u0026ndash;2008. \u003cem\u003eBMC Pregnancy Childbirth\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, 1\u0026ndash;5 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDalm\u0026aacute;z, C. A., dos Santos, K. G., Botton, M. R. \u0026amp; Roisenberg, I. Risk factors for hypertensive disorders of pregnancy in southern Brazil. \u003cem\u003eRev. Assoc. Med. Bras.\u003c/em\u003e \u003cb\u003e57\u003c/b\u003e, 692\u0026ndash;696 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdam, I., Elhassan, E. M., Mohmmed, A. A., Salih, M. M. \u0026amp; Elbashir, M. I. Malaria and pre-eclampsia in an area with unstable malaria transmission in Central Sudan. \u003cem\u003eMalar. J.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 1\u0026ndash;4 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShamsi, U. et al. A multicentre matched case control study of risk factors for preeclampsia in healthy women in Pakistan. \u003cem\u003eBMC Womens Health\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e, 1\u0026ndash;7 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKiondo, P. et al. Risk factors for pre‐eclampsia in Mulago Hospital, Kampala, Uganda. \u003cem\u003eTrop. Med. Int. Heal\u003c/em\u003e. \u003cb\u003e17\u003c/b\u003e (4), 480\u0026ndash;487 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEl-Nakhal, S. Case-control study of risk factors associated with preeclampsia in the Gaza strip. \u003cem\u003eJ. Med. Med. Sci.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e (9), 229\u0026ndash;233 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrum, T., Seifu, A., Abay, M., Angesom, T. \u0026amp; Tsegay, L. Determinants of pre-eclampsia/Eclampsia among women attending delivery Services in Selected Public Hospitals of Addis Ababa, Ethiopia: a case control study. \u003cem\u003eBMC Pregnancy Childbirth\u003c/em\u003e. \u003cb\u003e17\u003c/b\u003e, 1\u0026ndash;7 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEl-Moselhy, E. A., Khalifa, H. O., Amer, S. M., Mohammad, K. I. \u0026amp; Abd-El-Aal, H. M. Risk factors and impacts of pre-eclampsia: an epidemiologicl study among pregnant mothers in Cairo, Egypt., (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgrawal, S., Walia, G. K., Staines-Urias, E., Casas, J. P. \u0026amp; Millett, C. Prevalence of and risk factors for eclampsia in pregnant women in India. \u003cem\u003eFam Med. Community Heal\u003c/em\u003e. \u003cb\u003e5\u003c/b\u003e (4), 225\u0026ndash;244 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAjah, L. O. et al. The feto-maternal outcome of preeclampsia with severe features and eclampsia in Abakaliki, South-East Nigeria. \u003cem\u003eJ. Clin. Diagn. Res. JCDR\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e (9), QC18 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaschat, A. A. et al. Maternal blood‐pressure trends throughout pregnancy and development of pre‐eclampsia in women receiving first‐trimester aspirin prophylaxis. \u003cem\u003eUltrasound Obstet. Gynecol.\u003c/em\u003e \u003cb\u003e52\u003c/b\u003e (6), 728\u0026ndash;733 (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bayesian joint model, Cox model, Diastolic blood pressure, Linear mixed model, Preeclampsia, Pregnancy, Systolic blood pressure","lastPublishedDoi":"10.21203/rs.3.rs-6405478/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6405478/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHypertensive disorders of pregnancy pose a significant risk to maternal health, leading to high rates of maternal mortality and severe morbidity. This study aimed to jointly model the longitudinal changes in systolic and diastolic blood pressures and the time to develop preeclampsia among pregnant women. A retrospective study design was employed, collecting data from the medical charts of 549 pregnant women undergoing follow-up at Bishoftu General Hospital. Various methods, including summary statistics measures, individual profile plots, and Kaplan-Meier plots, were used to explore the data. Joint Bayesian multivariate models were employed to obtain inferences of progression of the disease. Among the 549 pregnant women, 10.9% of them were diagnosed with preeclampsia. Out of those diagnosed, unfortunately, 6.7% of women diagnosed with preeclampsia did not survive. Additionally, 10% of the pregnant women underwent pregnancy termination, and among them, 45% of the terminations were due to preeclampsia. There were 17 cases (3.1%) of stillbirths among the total cases, with 13.3% attributed to preeclampsia. Furthermore, 8.38% of pregnant women experienced complications, and among them, 40% of the complications were due to developing preeclampsia.\u003c/p\u003e\u003cp\u003eThe association parameter suggests that a one-unit increase in systolic blood pressure (SBP) raises the relative risk of developing preeclampsia by approximately 6%, while a one-unit increase in diastolic blood pressure (DBP) increases the relative risk by about 7%, after adjusting for other factors. The study findings indicate that systolic blood pressure and diastolic blood pressure are related to time to developing preeclampsia during pregnancy. Higher baseline blood pressure measurement may indicate an increased time to developing preeclampsia, while lower baseline blood pressure measurement may lead to a higher survival time from preeclampsia. The factors significantly affecting the survival time of developing preeclampsia are mother\u0026rsquo;s age, weight, family history of blood pressure, previous experience of abortion and gravidity. By monitoring blood pressure status, specifically SBP and DBP, throughout pregnancy period of the mother can minimize the risk of developing preeclampsia, and hence, enhance maternal and infant health.\u003c/p\u003e","manuscriptTitle":"Bayesian Modelling of the Dynamics of Preeclampsia and Blood Pressure Data of Pregnant Women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 11:40:19","doi":"10.21203/rs.3.rs-6405478/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a6668d2a-2d9a-404c-a25b-a38711b7f19a","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52184659,"name":"Biological sciences/Genetics"},{"id":52184660,"name":"Health sciences/Health care"},{"id":52184661,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-10-07T10:09:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-29 11:40:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6405478","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6405478","identity":"rs-6405478","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}