Socioeconomic Status and Cardiovascular Disease (CVD) Risk Factors among Pregnant Women in the Cape Coast Metropolitan Assembly – Central Region, Ghana

Socioeconomic Status and Cardiovascular Disease (CVD) Risk Factors among Pregnant Women in the Cape Coast Metropolitan Assembly – Central Region, Ghana | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Socioeconomic Status and Cardiovascular Disease (CVD) Risk Factors among Pregnant Women in the Cape Coast Metropolitan Assembly – Central Region, Ghana Abu Tia Dimongso, Peter Akrugu, Burhanatu Hafiz, Mate Siakwa, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7200664/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Introduction Noncommunicable diseases (NCDs) account for 74% of global mortalities, with cardiovascular diseases (CVDs) being the leading cause. The objective of this study was to investigate the associations between socioeconomic status (SES) and CVD risk factors among pregnant women in the Cape Coast Metropolis, Ghana. Methodology: This quantitative cross-sectional study was conducted in three health facilities in the Cape Coast Metropolis. Systematic random sampling was used to select 160 pregnant women attending the three antenatal clinics. Data were collected via a structured questionnaire and analysed via IBM SPSS Statistics 27 and R 4.3.1. Sociodemographic data, anthropometric data, maternal characteristics, dietary patterns, and blood pressure data were collected and analysed. Results and findings: The findings revealed that 25 (15.6%), 51 (31.9%), 64 (40%) and 20 (12.5%) patients had no risk factor, one risk factor, two risk factors, and three risk factors, respectively. Approximately 84.4% of the participants had at least one risk factor. Higher SES was associated with increased CVD risk factors. Approximately 56.9% of the participants were either overweight or obese, 7.5% had high blood pressure, 51.9% had a poor dietary pattern, and 37.5% were physically inactive. Age (estimate = 0.695, p < 0.001) and marital status (estimate = 4.091, p = 0.010) had positive and significant influences on blood pressure. SES (estimate = -0.002, p = 0.957), employment (estimate = 0.737, p = 0.547), and educational level (estimate = 1.198, p = 0.1405) had no significant effects on BP. Age (estimate = 0.408, p < 0.001) and marital status (estimate = 4.318, p < 0.001) had substantial positive influences on body mass index (BMI). In contrast, SES (estimate = 0.037, p = 0.109), parity (estimate = 0.559, p = 0.174), and job status (estimate = -0.619, p = 0.370) had a lower, nonsignificant influence on BMI. Conclusion This study highlights the significant impacts of age and marital status on blood pressure and body mass index among pregnant women, whereas socioeconomic status had no meaningful influence on cardiovascular disease risk factors. Employment status demonstrated a notable negative association with dietary patterns, underscoring the complex interplay between sociodemographic factors and health outcomes in this population. Socioeconomic status cardiovascular diseases pregnant woman and Risk factors overweight obese blood pressure dietary pattern Figures Figure 1 Figure 2 Background of the Study Globally, noninfectious diseases are responsible for approximately 41 million mortalities annually, accounting for approximately 74% of all mortalities. Seventeen million individuals die as a result of NCDs before reaching age 70 each year, and developing countries are responsible for approximately 86% of these hasty deaths, most of which are from CVD (17.9 million) (Shand et al., 2023). Approximately 80% of mortalities related to CVDs are a result of stroke and heart attack, and for individuals aged 70 years and younger, 25% of the deaths occur without warning signs (Robinson, 2021). Cardiovascular disease risk factors occur in all individuals; however, because some unavoidable physiological changes that are bound to occur in some special populations, their cases seem different: pregnant women (Franjic, 2019; Gangakhedkar & Kulkarni, 2021; Vinturache & Khalil, 2021). Higher rates of CVD have been reported in reproductive-aged women, with the occurrence of CVD during pregnancy posing a problem for both the attending physician and the unborn child. (Ludwig-Walz et al., 2022). The maternal mortality ratio (MMR) in Ghana has significantly decreased over the past three decades, dropping from 760 per 100,000 live births in 1990 to 310 in 2017, according to the 2017 Ghana Maternal Health Survey (T. K. Boafor et al., 2021). Despite a significant drop, Ghana still fears meeting the SDG 3.1 of 70 deaths per 100,000 live births by the year 2030. CVD risk factors occurring during pregnancy are responsible for the countless number of CVDs in the expectant mothers (Mikkola & Ylikorkala, 2024) These conditions include previous hypertension, hypertension during pregnancy, preeclampsia, DM, obesity/overweight, tobacco use, high cholesterol, advanced age, multiple pregnancies, lack of prenatal care, and maternal features such as early menarche and polycystic ovarian syndrome (Mitra & Ghosh, 2019). The prevalence of hypertensive disorders in pregnant persons has significantly increased globally, from 10.9% in 1990 to 18.0% in 2019 (Mathew et al., 2023). However, the incidence rate based on age was reduced, with a projected yearly percentage variation of -0.68. In 2019, the number of pregnancy-related fatalities caused by high BP was approximately 27,830, which is a 30% decrease compared with that in 1990 (Wang et al., 2021). Approximately 10% of pregnancies are affected by hypertension, a significant contributor to both mortality and morbidity. (Yan et al., 2021). The global prevalence of HDP increased from 16.3 million to 18.08 million between 1990 and 2019, representing an increase of 10.92% (Wang et al., 2021b). Additionally, the proportion of pregnant women with hypertensive disorders facing challenges rose significantly from 28.1% in 2012 to 83.7% in 2019. Hypertension-related disorders are the primary reasons for maternal death during pregnancy, accounting for 14.0% of worldwide maternal fatalities. This is especially true in low- and middle-income countries, where it is a significant basis of death among pregnant women (Agbeno et al., 2022). Pregnancy-related hypertensive diseases disproportionately impact Black, American Indian, and Alaskan Native women (Khedagi & Bello, 2021). Moreover, Black women have greater rates of severe morbidity and death associated with preeclampsia, whereas Hispanic women tend to have better pregnancy outcomes than Black women do. However, pregnancy outcomes for Hispanic women seem to be more favourable than those for black or Caucasian women, who have equal risk factors (Garovic et al., 2022). Similarly, in India and China, HDP has been linked to polycystic ovarian syndrome, BMI > 30 kg/m 2 , hyperglycemia during pregnancy, and advanced age (Mathew et al., 2023; Zhou et al, 2o22b). HDP is responsible for obstetric reasons for the deaths of pregnant women in developed nations ( Boafor, 2023). Maternal deaths remain a worldwide challenge, with a projected rate of 223 deaths per 100,000 live births, 70% of which emanate from sub-Saharan Africa. Cardiovascular diseases (CVDs) significantly contribute to maternal mortality by exacerbating conditions such as preeclampsia, cardiomyopathy, and thromboembolism during pregnancy (Easter, 2024; Keepanasseril et al., 2021; Sahu et al., 2021). Approximately 50.9% of gravid women present one or more cardiovascular risk factors (Garanet et al., 2023). Gestational hypertension, in particular, is on the rise. In Ghana, hypertension during pregnancy is the leading cause of maternal death, accounting for approximately 13.2% of all deaths among women (Hermes et al., 2013; WHO, 2021, 2023). Despite global efforts to reduce maternal mortality, Central Region continues to record a higher-than-average maternal mortality ratio (MMR), estimated at 319 deaths per 100,000 live births, than the national average of 301 in 2021 (GHANA STATISTICAL SERVICE, 2024). CVD risk factors are a significant contributor to these high mortality rates (Kotit & Yacoub, 2021). The development of risk factors for CVD during pregnancy is influenced by socioeconomic disparities, cultural norms, pre-existing maternal conditions, metabolic disorders, limited healthcare access, maternal age, urbanisation, unemployment and other lifestyle factors, such as physical inactivity and poor dietary patterns (O’Kelly et al., 2022; Yarney, 2019). However, insufficient research examining the effects of sociodemographic and maternal factors on the risk factors for CVD among pregnant women within the Central Region's unique socioeconomic and demographic landscape is a major concern. Given this gap, it is essential to investigate the factors contributing to the development of hypertension, overweight/obesity and poor dietary patterns among pregnant women in the Central Region. Methodology 3.1. Research Design This quantitative cross-sectional descriptive analytic study aimed to assess the prevalence of overweight/obesity, BP and poor dietary patterns among pregnant women while simultaneously determining the correlations among SES, maternal characteristics and CVD risk factors. 3.2. Study Area The Cape Coast metropolis is geographically situated along the Gulf of Guinea to the south. It is bordered by the Komenda Edina Eguafo Abrem Municipality to the west (starting at the Infrastructure bridge), Abura Asebu Kwamankese District to the east, and Twifu Heman Lower Denkyira district to the north. Covering an area of approximately 122 square, the metropolis stretches to Brabadze, which lies approximately 17 km from the capital of both the metropolis and the central region. Cape Coast's population has been steadily growing, reflecting its increasing urbanisation and the migration of people from rural areas in search of employment and better living conditions. According to the most recent census data, the population of Cape Coast is estimated to range between 250,000 and 300,000 people. The population is diverse, with a mix of students, traders, civil servants, and individuals working in sectors such as tourism and fishing. Population growth has also led to an increase in demand for housing, education, and healthcare services, placing pressure on existing infrastructure and local government services. This growing population underscores the need for sustainable urban planning and improved public services to ensure that development in the city benefits all residents. For this study, three hospitals within the Cape Coast Metropolitan area were randomly selected: Cape Coast Metropolitan Hospital (CCMH), University of Cape Coast Hospital (UCCH) and Adisadel Urban Health Centre. 3.3. Study Population The research focused on expectant mothers who received antenatal care services from the three health facilities mentioned above. This included all expectant mothers who carried a live foetus who attended antenatal clinics at the selected study areas either for their first time or not. All women who met the inclusion criteria were interviewed. 3.4. Inclusion and exclusion The study was conducted among pregnant women receiving antenatal care at three selected hospitals. The participants included those with confirmed pregnancies who were receiving antenatal care and who provided informed consent to participate in the study within the randomly selected hospitals. Pregnant women with a history of inevitable abortion; pre-existing medical conditions requiring ongoing treatment; and those diagnosed with hypertension, diabetes, or heart disease were excluded from the study. Additionally, women with multiple gestations were excluded. The determination of multiple pregnancies was based on findings from ultrasound scans. 3.5. Sampling Procedure Approximately 86% of pregnant women attend antenatal clinics annually in Ghana (GSS, 2007). The Leslie–Kish formula was used to determine the population size (Baligeh et al., 2023). . At 95% (1.96) confidence and a 5% margin of error, a sample size of 150 was reached, with 10% added to accommodate unreturned questionnaires. Data collection took approximately 3 months, starting in May 2024 and ending in August 2024. Three health institutions were randomly selected from the five major health facilities. The annual attendance from each health facility was used to determine the system of random systematic methods to be used in each institution over three months. 3.6. Data collection instruments The study used a structured questionnaire to gather sociodemographic, maternal, and anthropometric data for calculating participants' BMI. Section A, included age, education, employment, marital status, and religious affiliation. Section B included gestational age, family history of CVD, oral contraceptive use, breastfeeding, high blood pressure, gestational diabetes, PCOS, and fertility treatments. Participants' blood pressure was measured twice via an Omron digital machine at 15-minute intervals. They rested for 30 minutes before the BP was taken. The ideal BP was recorded via an adult cuff. Mean arterial pressure (MAP) was used for analysis, which is best for estimating the impact of BP on the cardiovascular system (DeMers & Wachs, 2019). MAP is calculated on the basis of the overall cardiac cycle and has a stronger correlation with certain CVD risk factors, such as body weight. Weight and height were used to calculate the BMI of pregnant women (PWs), and the guidelines from the Institute of Medicine (IOM) were used as reference points (Rasmussen et al., 2009). The IOM average standard weight during pregnancy was subtracted from the current weight, taking into account the trimester. The standard weight gain in a normal woman with normal dietary practices is between 0.1 kg and 2 kg. The average weight gain in each trimester was used as a benchmark to determine the actual weight of the pregnant women. The International Wealth Index (IWI) (Smits & Steendijk, 2015) was used to determine the SES of participants. The variables assessed included; household assets such as television, refrigerators, phones, cars, bicycles, and utensils. The quality of toilet facilities, floor materials, and water supplies were also examined. The number of sleeping rooms in the house was also counted. The assets were assigned weights on the basis of their respective components. Housing features, such as floor materials, toilet facilities, and sleeping rooms, were classified as low, medium, or high quality. Individual scores were categorised as poorer, poor, rich, or very rich. The Mediterranean Diet tool (Trichopoulou et al., 2003) was used to assess participants' dietary patterns, with a score of 9 or above indicating adherence. The data were pretested at Anamabo health centre, and any unclear variables were revised. The tool was culturally appropriate and well suited to the research objectives, considering culturally specific activities within the Ghanaian context. 3.7. Sampling technique and sampling size A multistage sampling technique was employed. Three out of the five health facilities were randomly selected and used. Systematic random sampling was used to recruit participants who met the inclusion criteria. A sample size of 165 participants was determined via Cochran’s formula, ensuring adequate power for statistical analysis and reliability of the study findings. One hundred and sixty questionnaires were properly completed and subsequently used for the final analysis. Three qualified midwives were recruited and trained as research assistants over three days on proper questionnaire administration, ethical considerations, and confidentiality protocols. Each day after data collection, the completed questionnaires were reviewed for completeness and securely stored. Participation was entirely voluntary, and informed consent was obtained from each participant, who was also ifinformed of their right to withdraw from the study at any time without penalty. 3.8. Data Processing and Data Analysis. The data were analysed via IBM SPSS Statistics version 27.0 for descriptive analysis and R 4.4.1 for inferential statistics. The data were meticulously entered into SPSS, and a visual inspection was conducted by two independent individuals. Means were computed to identify outliers and missing values. The results are presented in tables and graphs, and associations were identified using multivariate structural equation modelling (SEM). Cross-tabulation was used to assess SES and risk factors among participants. 3.9. Ethical consideration Ethical clearance of the research was sought from the Review Committee of the Research Department of the Ghana Health Service after the approval of the topic from the School of Nursing and Midwifery at the University of Cape Coast. Participants who were randomly selected were taken to a noise-free office for privacy and confidentiality after they agreed to take part in the study. Their names were not needed. After each day's activity, the researcher met with research assistants to collect all the completed questionnaires. They were kept under key and lock Results 1.2. Demographic characteristics of the respondents Table 1 below shows the distributions of the various demographic characteristics. Table 1 Demographic characteristics of the participants Measure Frequency Percent Facility CCMH 45 28.1 UCCH 67 41.9 ADISADEL 48 30.0 Religion Muslim 20 12.5 Christian 140 87.5 Educational level None 3 1.9 Primary 9 5.6 JHS 49 30.6 SHS 53 33.1 Tertiary 46 28.8 Marital status Married 85 53.1 Single 75 46.9 Trimester 1st 21 13.1 2nd 74 46.3 3rd 65 40.6 Socioeconomic status Lower 0 0 Lower middle 18 11.3 Upper middle 70 43.8 High 72 45.0 Author’s field data 2024 Table 2 Age, parity and menarche of participants Variable N Minimum Maximum Mean Std. Deviation Deliveries (parity) 160 0.00 6.00 1.4063 1.51012 Age of menarche 160 6.00 20.00 13.8625 1.89832 Age of participants 160 16.00 42.00 29.0250 5.69779 The study participants concerning the three hospitals are as follows: UCCH, Adisadel Urban Health Centre, and CCHM, representing 41.9%, 30.0%, and 28.1% of the sample, respectively. The majority of participants were Christians (87.5%), with the remaining 12.5% being Muslims. Nineteen percent had no formal education, 5.6% had completed primary school, 30.6% had completed junior high school (JHS), 33.1% had completed senior high school (SHS), and 28.8% had completed tertiary education. In terms of marital status, 53.1% were married, and 46.9% were not. Among them, 13.1%, 46.3%, and 40.6% were in their first, second, and third trimesters, respectively. The obstetric history revealed a mean parity order of 1.41 deliveries, with some participants reporting as many as six deliveries and others reporting none. The age of menarche ranged from 6–20 years, with a mean onset age of 13.86 years. The age of the participants ranged from 16–42 years, with an average age of 29.03 years. 1.3. Past medical and obstetric history of the participants A small proportion (12.5%) reported a family history of CVD, and 18.3% had previously used oral contraceptives. In terms of obstetric history, more than half of the participants (51.2%) had breastfed their children for over one year. Approximately 3.8% reported a history of hypertension during pregnancy, another 3.8% had experienced gestational diabetes, 10.6% had a history of PCOS, and 6.9% had undergone fertility treatments. 1.4. Risk Factors for Cardiovascular Diseases Table 3 Mean arterial pressure, BMI, and dietary pattern of the participants Variable Frequency Percent Mean Arterial Pressure Optimal (MAP < 93) 129 80.6 Normal (MAP b/n 93 and 105) 19 11.9 High normal 8 5.0 Grade 1 HPT 3 1.9 Grade 2 HPT 1 0.6 Body Mass Index Underweight 4 2.5 Normal weight 65 40.6 Overweight 43 26.9 Obesity 48 30.0 Dietary pattern Good 77 48.1 Poor 83 51.9 Total 160 100.0 Author’s field data 2024 The majority of participants (80.6%) had an optimal range, whereas 11.9% had normal MAP levels. A smaller proportion had elevated MAP values, with 5.0% classified as high-normal, 1.9% diagnosed with Grade 1 hypertension, and 0.6% with Grade 2 hypertension. In terms of BMI, 40.6% of the participants were normal but a significant proportion had higher-than-recommended body weights, with 26.9% classified as overweight and 30.0% categorised as obese. Only 2.5% of the participants were underweight. The dietary patterns revealed that 48.1% of the participants reported good dietary practices, whereas the remainder reported suboptimal dietary habits. 1.2. Relationships between SES and CVD risk factors among PWs This study explored the associations between SES and risk factors for CVD. The table below presents varying levels of risk across SES categories. Table 4 Socioeconomic status and CVD risk factors SES Classification MAP (Mean arterial pressure) Dietary pattern Body Mass Index No risk Risk No risk Risk No risk Risk Lower middle 17 1 9 9 7 11 Upper middle 69 1 27 43 35 35 High 70 2 41 31 26 46 Author’s field data 2024 Table 6 above presents the pictorial analysis of the mothers-to-be wealth with CVD risk factors. The respondents with high BP were few across all SES groupings. One out of 18 (5.6%) had high MAP in the lower-middle SES group, with 1 (1.4%) and 2 (2.8%) had high MAP among the upper middle and high groups respectively. Mean arterial pressure is not a significant concern for the pregnant women in this study, regardless of their SES classification. The data indicates that women in the upper-middle SES group exhibited the highest prevalence of poor dietary patterns, 43 out of 70 (61.4%), followed by the lower-middle SES group, 9 out of 18 (50%), and the high SES group, 31 out of 72 (43.1%). In contrast, women in the lower-middle SES group may benefit from more home-prepared meals, despite financial limitations. These patterns highlight the complex relationship between SES and dietary behaviour, reinforcing existing evidence that education, income, and health acce ss significantly shape nutritional choices. The highest proportions of overweight or obesity were found in the high SES group, 46 out of 72 (63.9%) women and the lower-middle SES group, 11 out of 18 (61.1%), while the upper-middle SES group showed a relatively lower rate, 35 out of 70 (50%). These results indicate that BMI-related cardiovascular risks are significant at both ends of the socioeconomic spectrum. 1.3. Prevalence of risk factors among participants Table 7 below provides an overview of CVD risk factors among respondents. Among the 160 participants, 15.6% (25) had no CVD risk factors. About 31.9% (51), 40% (64), and 12.5% (20) have 1, 2 and 3 CVD risk factor(s), respectively. Overall, the majority (84.4%) of the gravidae have at least one CVD risk factor, with a notable proportion displaying multiple risk factors. Table 5 Number of risk factors per participant Number of risk factors Frequency Percent (%) None 25 15.6 One 51 31.9 Two 64 40.0 Three 20 12.5 Author’s field data 2024 1.2. Correlation Matrix A correlation study revealed associations between SES and CVD risk variables, including blood pressure, BMI, dietary habits, and age, among pregnant individuals. These results are explained in full below. The correlation between SES and MAP was positive but modest (r = 0.101), indicating a minor increase in blood pressure with increasing SES. This association implies that socioeconomic gains may be related to increase BP. A small positive association was also identified between SES and BMI (r = 0.170), indicating that greater SES is associated with slightly higher BMI levels. This shows that women from higher socioeconomic backgrounds can suffer greater risk owing to increased BMI. SES is slightly related to quality dietary habit (r = 0.077). Increase in SES, increases quality dietary habit marginally. While this link is modest, it does suggest that women of higher SES may have somewhat greater access to healthful meals. Nevertheless, the small association shows that good dietary habit alone may not be a substantial modulator of CVD risk in this cohort. Finally, age revealed a slight positive correlation with SES (r = 0.199), indicating that older pregnant women in this sample tended to have higher SES. While age itself is a nonmodifiable characteristic, its link with SES might suggest that older women could be more financially secure or more educated, which can affect health-seeking behaviours and access to resources. Overall, our data emphasise that among pregnant women, greater SES is marginally linked with specific CVD risk variables, such as BMI and blood pressure. Although the relationships are minor, the patterns identified here illustrate the subtle role that SES may have in impacting cardiovascular health risk during pregnancy. Further investigations are necessary to investigate these connections in more detail, especially to elucidate possible mediators and moderators of SES and CVD risk factors. Table 6 Correlation Matrix MAP BMI Diet PA Age SES MAP 1.000 0.386 0.026 -0.044 0.183 0.101 BMI 0.386 1.000 -0.020 -0.145 0.343 0.170 DIET 0.026 -0.020 1.000 0.087 0.056 0.077 Age 0.183 0.343 0.056 -0.005 1.000 0.199 SES 0.101 0.170 0.077 -0.041 0.199 1.000 Author’s field data 2024 1.3. Structural equation model (SEM) results This research examined the correlation between SES and cardiovascular disease (CVD) risk variables, including MAP, BMI, and dietary habits, in pregnant women. Structural equation modelling (SEM) was used to evaluate the direct impacts of SES on each risk factor while controlling for pertinent confounders such as age, educational attainment, job status, marital status, prenatal visits, parity, cardiovascular disease history, contraceptive usage, and breastfeeding. The following is a comprehensive overview of the results for each outcome variable. Age had a positive and substantial influence on MAP (estimate = 0.695, p < 0.001). This relationship highlights the significance of age as a risk factor for hypertension. Marital status shows a significant positive correlation with BP (estimate = 4.091, p = 0.010). SES had no significant effect on BP (estimate = -0.002, p = 0.957), indicating that economic resources alone may not directly affect MAP in this demographic. Other variables, such as employment (estimate = 0.737, p = 0.547), educational level (estimate = 1.198, p = 0.145), and contraceptive use (estimate = 1.231, p = 0.517), also did not reach statistical significance, indicating that these factors may not be primary determinants of MAP among expectant mothers. BMI is strongly associated with age and marital status. Age had a considerable positive influence on BMI (estimate = 0.408, p < 0.001), revealing a pattern of higher BMI with advancing age. Marital status was similarly associated with BMI (estimate = 4.318, p < 0.001), suggesting that married women or those in stable relationships might experience lifestyle factors that contribute to a higher BMI. SES had a lower, nonsignificant influence on BMI (estimate = 0.037, p = 0.109), indicating that income or wealth alone may not predict BMI. The absence of a substantial link with SES shows that variables beyond economic resources, possibly including access to health education or community support, may mitigate this correlation. Other factors, such as job status (estimate = -0.619, p = 0.370) and parity (estimate = 0.559, p = 0.174), were not significantly linked with BMI. Dietary Pattern, a crucial component in reducing CVD risk, revealed significant relationships with job status and contraceptive usage, but SES and age did not indicate relevant impacts. Employment status had a significant negative relationship with dietary patterns (estimate = -0.687, p = 0.018). Contraceptive usage positively affects food patterns (estimate = 1.034, p = 0.021). SES had a minimal, nonsignificant effect on eating habit (estimate = 0.009, p = 0.371). Similarly, education level (estimate = 0.014, p = 0.944) and age (estimate = -0.075, p = 0.101) did not significantly affect food patterns. The findings underline that age and marital status are key drivers of BMI and BP among pregnant women. Table 7 Regression Results for Cardiovascular Disease Risk Factors among Pregnant Women Outcome Predictor Estimate Std. Error z value p value MAP SES -0.002 0.041 -0.054 0.957 Age 0.695 0.194 3.580 < 0.001 Educational Level 1.198 0.822 1.457 0.145 Employment Status 0.737 1.224 0.602 0.547 Marital Status 4.091 1.596 2.563 0.010 No. of Antenatal Visits 0.049 0.326 0.149 0.881 Parity -1.093 0.730 -1.498 0.134 History of CVD -0.183 2.167 -0.085 0.933 Contraceptive Use 1.231 1.897 0.649 0.517 Breastfeeding -0.800 1.631 -0.490 0.624 BMI SES 0.037 0.023 1.602 0.109 Age 0.408 0.109 3.727 < 0.001 Educational Level 0.582 0.463 1.256 0.209 Employment Status -0.619 0.690 -0.897 0.370 Marital Status 4.318 0.899 4.801 < 0.001 No. of Antenatal Visits -0.184 0.183 -1.005 0.315 Parity 0.559 0.411 1.359 0.174 History of CVD -0.277 1.221 -0.227 0.820 Contraceptive Use -1.234 1.069 -1.154 0.248 Breastfeeding -0.791 0.919 -0.861 0.389 DIET SES 0.009 0.010 0.894 0.371 Age -0.075 0.046 -1.639 0.101 Educational Level 0.014 0.195 0.070 0.944 Employment Status -0.687 0.290 -2.371 0.018 Marital Status -0.131 0.378 -0.346 0.729 No. of Antenatal Visits 0.053 0.077 0.690 0.490 Parity 0.329 0.173 1.905 0.057 History of CVD -0.197 0.513 -0.384 0.701 Contraceptive Use 1.034 0.449 2.304 0.021 Breastfeeding -0.221 0.386 -0.574 0.566 Author’s field data 2024 The figure below illustrates the structural relationships among socio-economic status, dietary patterns, and cardiovascular risk factors.” Source: Field data, 2024 Model Summary and Fit Measures The structural equation model (SEM) analysis provided the following model summary and fit measurements, offering insight into the quality of the model fit and the estimation process. The model uses a maximum likelihood (ML) estimator, optimised via the NLMINB approach. With a total of 160 data points, the model has 50 parameters. The user model test result is 0.000 with 0 degrees of freedom, indicating that the model is completely saturated and fits the observed data exactly. The baseline model test statistic, by comparison, is 133.712 with 46 degrees of freedom and a very significant p value (p < 0.001), demonstrating that the baseline model poorly matches the data compared with the user model. The comparative fit index (CFI) and Tucker‒Lewis index (TLI) both obtained values of 1.000, denoting optimal model fit, with values close to 1 indicating outstanding fit in both indices. The root mean square error of approximation (RMSEA) was 0.000, with a 90% confidence range spanning from 0.000–0.000, showing no disagreement between the model and observed data in the population. This is further reinforced by the standardised root mean square residual (SRMR) of 0.000, indicating a good match since SRMR values closer to 0 imply a better fit. The model’s loglikelihood is -1985.729, with corresponding Akaike information criterion (AIC) and Bayesian information criterion (BIC) values of 4071.459 and 4225.218, respectively. The sample-size-adjusted BIC (SABIC) is 4066.936. These information requirements aid in analysing model parsimony, with lower values typically suggesting better model fit than alternative models do. The findings reveal a good model fit with 0% residual error and strong fit indices. These results demonstrate the robustness of the model in capturing the associations between socioeconomic status and cardiovascular risk variables among pregnant women. Table 8 Model Summary and Fit Measures Measure Value Estimator ML Optimisation Method NLMINB Number of Model Parameters 50 Number of Observations 160 Test Statistic (User Model) 0.000 Degrees of Freedom (User Model) 0 Test Statistic (Baseline Model) 133.712 Degrees of Freedom (Baseline Model) 46 P value (Baseline Model) 0.000 CFI 1.000 TLI 1.000 Loglikelihood (User Model) -1985.729 AIC 4071.459 BIC 4225.218 SABIC 4066.936 RMSEA 0.000 90% CI Lower (RMSEA) 0.000 90% CI Upper (RMSEA) 0.000 SRMR 0.000 Source: field data, 2024 4.10. Covariances The covariance estimates among CVD risk factors and socioeconomic indicators, specifically MAP, BMI, and DIET, offer insights into the interrelationships between these variables. A substantial positive correlation was discovered between BP and BMI (estimate = 13.684, p < 0.001), with a normalised value of 0.313. These data demonstrate a substantial positive correlation between BP and BMI, indicating that when BMI increases, BP may also increase among pregnant women. Given that both increased BP and increased BMI are known risk factors for CVD, this association underscores the relevance of BMI in impacting cardiovascular health during pregnancy. The correlation between MAP and DIET, although positive (estimate = 0.552), was also nonsignificant (p = 0.704), with a normalised value of 0.030. This finding shows that food habits, as evaluated in this model, may have a minimal direct association with BP levels among the research participants. The negative correlation between BMI (estimate = -6.784, p = 0.082) approached significance, with a normalised value of -0.139. Similarly, the covariances between BMI and DIET (estimate = -0.521, p = 0.525) were also nonsignificant, with standardised values of -0.050. These data imply that dietary habits, as defined here, are not strongly linked with either BMI. In summary, whereas BMI and BP show a substantial positive link, the associations between other CVD risk variables and SES-related indicators are modest and largely nonsignificant. Table 9 Covariances Covariance Estimate Std. Err z value P values Std.lv Std.all MAPBMI 13.684 3.621 3.779 0.000 0.313 0.313 MAP DIET 0.552 1.451 0.380 0.704 0.030 0.030 BMIDIET -0.521 0.819 -0.636 0.525 -0.050 -0.050 Source: field data, 2024 Variances The variance estimates for the CVD risk factors and SES indicators provide insights into the degree of variability for each component among pregnant women in this research. For the parameters MAP, BMI, and DIET, the variance estimate was 0.000, with matching standard errors and z values similar to 0.000, showing that these variables did not contribute further unexplained variability to the model. MAP revealed a significant variance estimate of 77.560 (p < 0.001), with a normalised estimate of 0.888, indicating high unexplained variability in BP across the subjects. BMI also exhibited substantial variability, with an estimate of 24.631 (p < 0.001) and a standardised value of 0.750. This reveals large variability in BMI levels among PW, supporting BMI as a key CVD risk factor that varies between people, presumably related to socioeconomic or lifestyle disparities. Finally, the dietary pattern latent variable (diet) demonstrated a significant variance of 4.341 (p < 0.001) and a standardised variance of 0.880. These data suggest considerable diversity in food patterns among women, indicating that poor nutrition is a major component contributing to individual CVD risk profiles. In summary, BP, BMI, and diet all revealed considerable and noteworthy variability, highlighting their importance as critical determinants related to cardiovascular health among pregnant women. These differences underscore the relevance of addressing these modifiable risk variables in programs aimed at lowering CVD risk within this group. Table 10 Variances in the CVD risk factors for the SEM Variance Estimate Std. Err z value P values Std.lv Std.all MAP 77.560 8.672 8.944 0.000 0.888 0.888 BMI 24.631 2.754 8.944 0.000 0.750 0.750 DIET 4.341 0.485 8.944 0.000 0.880 0.880 Source: field data, 2024 Discussion This study examined the relationships between SES and CVD risk factors in pregnant women, with a focus on blood pressure (BP), body mass index (BMI), and dietary habits. Similar research globally aligns with or contrasts these findings in various ways. Socio-demographic and maternal characteristics of pregnant women at risk of CVD The socio-demographic and maternal characteristics of the participants revealed important patterns in the prevalence of CVD risk factors. Among the participants, 51.9% exhibited poor dietary patterns, 57.5% had abnormal body weights (overweight or obese), and only 2.5% had high mean arterial pressure (MAP). These findings align with studies conducted in different regions. The findings of this study are consistent with those of other studies showing a high prevalence of abnormal BMI among pregnant women. Chairat et al. ( 2023 ) reported that more than 50% of pregnant women were classified as overweight. Similarly, Deputy et al. ( 2015 ) reported that 47.2% of pregnant women had a BMI above normal. A recent systematic review of obesity and overweight among pregnant women showed a 43.8% pooled prevalence (Kent et al., 2024 ). The high prevalence of abnormal BMI in this study further emphasises the growing concern over abnormal weight gain during pregnancy, which is a well-known risk factor for CVD and other health complications. However, the prevalence in this study is significantly higher than that reported in Croatia, where only 29.2% of pregnant women were classified as overweight or obese (Vince et al., 2021 ). Cultural perceptions of body size may explain some of these variations, as larger body sizes in some African and African diaspora communities are often associated with wealth, beauty, and dignity (Appiah et al., 2016 ; Hoenink et al., 2021 ; Naigaga et al., 2018 ). These cultural factors may contribute to higher rates of obesity and overweight in these populations. In terms of dietary habits, 51.9% of the participants reported poor dietary patterns, which is consistent with findings from Garanet et al. ( 2023 ) and Franck ( 2021 ). Researchers have shown that unhealthy diets contribute significantly to hypertension and metabolic disorders in pregnancy worldwide and are directly correlated with future cardiovascular risk. These findings align with the results of the present study, highlighting poor dietary habits as a major risk factor for CVD among pregnant women. The prevalence of poor dietary patterns in this study contrasts with the lower percentages observed in some developing countries, where limited access to affordable, nutritious food might contribute to poor dietary habits, further exacerbating cardiovascular risks. Socio-demographic Predictors of Pregnant Women at Risk of CVD The analysis identified several socio-demographic factors, such as age and marital status, as significant predictors of CVD risk among pregnant women. Specifically, older maternal age was significantly correlated with increased BMI (estimate = 0.408, p < 0.001), which is a well-established risk factor for CVD. Age-related increases in BMI are documented in studies by Gozuyesil et al. ( 2025 ), which suggest that older women tend to have higher BMIs due to metabolic changes and lifestyle factors that become more pronounced with age. Similarly, marital status was found to influence BMI, with married women being more likely to have a higher BMI (estimate = 4.318, p < 0.001), likely due to lifestyle factors, shared resources, and social support mechanisms. This finding is consistent with research by (Corrêa et al., 2024 ; Silva et al., 2008 ), who reported that marital status was positively correlated with BMI in pregnant women (Lee et al., 2020 ). However, SES did not have a significant effect on BMI in this study, which contrasts with findings from studies in high-income countries, such as (Kominiarek et al., 2018 ), which highlighted that lower SES was linked to elevated BMI due to limited access to nutritious food and healthcare services. The lack of a significant SES impact in this study could be due to factors such as greater access to healthcare resources or more homogenous access to nutrition and exercise in the sample population. Influence of Socio-demographic Factors on Cardiovascular Risk among Diverse Maternal Groups The findings from this study revealed that socio-demographic factors, including age, marital status, and employment, influence CVD risk factors such as BMI and dietary habits. For example, studies in high-income countries often emphasise SES as a key predictor of BMI, with lower SES being linked to higher obesity rates due to limited access to healthcare and nutritious food (Kominiarek et al., 2018 ). In contrast, the present study revealed that sociodemographic factors such as age and marital status had more significant effects on CVD risk factors than SES alone. The lack of a strong SES effect may be due to various factors, including access to healthcare resources or community-based health interventions that mitigate the negative impacts of lower SES. Employment status and contraceptive use were significant predictors of dietary patterns, with employed women reporting more time constraints that negatively impacted their dietary habits, which aligns with findings from Bangladesh (Islam et al., 2016 ). Contraceptive use was positively associated with better dietary patterns, similar to findings from (Barker et al., 2008 ). The overall findings suggest that while sociodemographic factors such as age and marital status significantly influence CVD risk factors among pregnant women, the role of SES is complex and may vary on the basis of the local context, healthcare infrastructure, and cultural norms. This study underscores the importance of considering both demographic and lifestyle factors when designing interventions to reduce CVD risk during pregnancy, especially in diverse socioeconomic contexts. 5.1. Summary This study aimed to explore the relationship between socioeconomic status (SES) and the presence of cardiovascular risk factors among pregnant women. The results provide valuable insights into the distribution and severity of key cardiovascular risk factors, including mean arterial pressure (MAP), body mass index (BMI), and dietary patterns. The findings revealed that cardiovascular risk is prevalent across all SES groups, albeit with variations in the number and severity of risk factors. Prevalence of Cardiovascular Risk Factors The prevalence of cardiovascular risk factors among pregnant women in this study was significant across all SES categories. The MAP risk was notably low, with only 2.5% of women exhibiting elevated MAP. These findings suggest that MAP is not a major concern in this study population. The relatively low MAP risk across SES groups is encouraging, as effective monitoring and control of blood pressure could be contributing factors. However, this finding does not discount the need for continued vigilance regarding hypertension during pregnancy, particularly in women with multiple risk factors. In contrast, BMI has emerged as a key area of concern. A significant proportion of women (57.5%) were classified as being at risk due to being overweight or obese. This finding is particularly concerning, as high BMI is an established risk factor for both cardiovascular diseases and complications during pregnancy, such as gestational diabetes and preeclampsia. The prevalence of overweight and obesity underscores the importance of interventions aimed at promoting healthy weight and preventing excessive weight gain during pregnancy. Dietary patterns were also a significant concern, with 51.9% of participants exhibiting poor eating habits. A poor diet, characterised by low fruit and vegetable intake and high consumption of processed foods, is a known contributor to CVD. The high proportion of women with poor dietary patterns suggests that nutritional interventions should be prioritised as part of prenatal care. The role of dietary education in managing and reducing CVD cannot be overstated. SES and CVD risk factors The relationship between socioeconomic status (SES) and cardiovascular risk factors was an important aspect of the analysis. The study revealed that lower-middle SES women were particularly vulnerable to the accumulation of multiple risk factors, with 50% of participants in this group having two risk factors. This finding suggests that lower SES is associated with a greater likelihood of experiencing multiple cardiovascular risks. The increased risk in this group could be attributed to several factors, including limited access to healthcare, poor diet, and reduced opportunities for physical activity, all of which are common in lower SES populations. Interestingly, the upper-middle and high SES groups also presented notable levels of CVD risk. While lower-SES women accounted for a greater proportion of women with two risk factors, upper-middle- and high-SES women presented a significant incidence of two and three risk factors, especially in the high-SES group (44.4%). This means that higher SES does not offer complete protection against CVD risk. Even in these groups, factors such as sedentary lifestyles, stress, and poor dietary habits seem to contribute to the accumulation of CVD. These findings challenge the common assumption that higher SES automatically correlates with better health outcomes and underscore the need for targeted interventions that address lifestyle factors common to all SES groups. Socioeconomic status and its impact on individual CVD risk factors The correlation between SES and BP, although modest (r = 0.101), suggests that higher SES might be linked with slightly elevated BP among pregnant women. While the association was small, it is in line with findings from studies that suggest that individuals from higher SES backgrounds often experience increased stress or engage in less healthy lifestyle practices, which may contribute to higher blood pressure (Kraft & Kraft, 2021 ). However, this result contrasts with those of some studies, which have shown a more substantial impact of SES on BP, likely due to variations in lifestyle factors, access to healthcare, and diet (Qin et al., 2022 ). In this cohort, the small effect size may be attributable to other dominant factors, such as age or marital status, which may overshadow SES in influencing BP levels. The finding of a modest positive correlation between SES and BMI (r = 0.170) also mirrors the literature, which often links higher SES with increased BMI due to better access to food, sedentary lifestyles, or stress-induced eating. However, the correlation was still relatively weak, indicating that while SES plays a role in BMI, other factors, such as physical activity and health education, might mitigate its effect. This aligns with studies suggesting that higher SES alone does not guarantee a higher BMI but is often compounded by other variables, such as dietary habits and exercise patterns. In terms of dietary habits, the weak positive correlation between SES and diet quality (r = 0.077) suggests that women with higher SES may have slightly better dietary patterns. While this finding is consistent with the notion that wealthier individuals typically have access to healthier food, the modest association indicates that SES alone may not significantly influence diet. Research has shown that access to quality food is a key determinant of dietary habits, but factors such as cultural preferences, time constraints, and education may play a more significant role in shaping food choices (Larson et al., 2006). Therefore, while SES is a contributing factor, its impact on diet quality appears to be limited in this cohort. Severity of Risk Factor Accumulation While the study revealed that no participant had all four cardiovascular risk factors, the accumulation of two or three risk factors was prevalent in many participants. This is particularly significant, as the presence of multiple risk factors can exacerbate the overall risk of developing CVD during pregnancy and later in life. The upper-middle SES group had the highest proportion of women with three risk factors, suggesting that even women with higher SES are at risk for severe CVD complications. The lowest proportion of women with a lower-middle SES had three risk factors, yet a high proportion of women still had two risk factors. This highlights the compounding effects of multiple CVD risks and the importance of early intervention in these women to prevent the escalation of risk factors. Marital Status and Age as Predictors of CVD Risk The study revealed that age was a significant predictor of both BP (estimate = 0.695, p < 0.001) and BMI (estimate = 0.408, p < 0.001), which is consistent with many studies linking advanced age with increased CVD risk. Older pregnant women in this study presented higher BP and BMI values, reinforcing the idea that age is a critical determinant of CVD risk factors. This finding underscores the importance of age as a nonmodifiable risk factor in pregnancy, which warrants increased monitoring and early intervention for older pregnant women. Similarly, marital status was found to have a significant positive association with both BP (estimate = 4.091, p = 0.010) and BMI (estimate = 4.318, p < 0.001), suggesting that married or cohabiting women may experience increased CVD risk compared with their unmarried counterparts. This aligns with studies that indicate that marital status can impact health outcomes, possibly due to stress, lifestyle habits, or shared resources within a marriage (Umberson et al., 2010). Married women may face additional stressors or engage in less healthy behaviours, which could contribute to higher BP and BMI. These findings further highlight the need for targeted prenatal care that considers marital status a risk factor. Interactions among SES, lifestyle factors, and CVD risk Dietary habits were found to be significantly influenced by employment status (estimate = -0.687, p = 0.018) and contraceptive use (estimate = 1.034, p = 0.021), whereas SES had no significant effect. The employed women were found to have poorer dietary patterns, possibly due to time constraints, stress, or limited access to nutritious food due to busy schedules. This finding is consistent with studies that show that employed individuals may struggle to maintain healthy eating habits due to a lack of time or resources. On the other hand, contraceptive use was positively associated with better dietary habits, which could be reflective of health-conscious behaviours among women using contraception. These findings suggest that factors beyond SES, such as employment status and reproductive health choices, may play a more significant role in influencing dietary habits. The findings of this study demonstrate some alignment with existing research on the relationship between sociodemographic factors and CVD risk, particularly the significant influence of age and marital status. However, the study also presents contrasting findings in terms of the role of SES. While SES is typically viewed as a significant predictor of health outcomes, its impact on CVD risk in this cohort was modest, suggesting that SES may not be as influential as other sociodemographic factors, such as age or marital status. This could be due to the specific context of the study, where other unmeasured variables (e.g., access to healthcare, local health policies, or community support systems) might have played a more significant role in shaping maternal health. Correlation between SES and risk factors The analysis of correlations between SES and CVD risk factors revealed modest relationships. A slight positive correlation between SES and BMI indicated that women with higher SES are more likely to have higher BMIs, potentially due to dietary patterns and lifestyle factors. A slight positive correlation between SES and dietary patterns was observed, although the relationship was not strong. This finding indicates that women in higher SES groups may, on average, exhibit slightly better dietary habits than those in lower SES groups, but the correlation is not robust enough to suggest a definitive trend. The findings highlight the prevalence of CVD risk factors among pregnant women across all SES groups, with BMI and dietary patterns emerging as key risk factors. While the MAP risk was low, multiple risk factors were common, particularly in the lower-middle SES group, where women were more likely to experience two or more risks. However, the upper-middle and high SES groups also presented considerable risk, particularly in terms of BMI and physical inactivity. These findings suggest that CVD risk during pregnancy is prevalent across all SES categories and that interventions should focus on managing modifiable risk factors, such as diet and weight management, in pregnant women. Further research is needed to explore the underlying causes of these associations and to develop tailored strategies to reduce cardiovascular risk in pregnant women from different socioeconomic backgrounds. 5.2. Conclusion This study underscores the complex interplay between sociodemographic and lifestyle factors in influencing cardiovascular risk among pregnant women. While age and marital status emerged as strong predictors of CVD risk factors, SES had a more modest impact. The findings suggest that future interventions should focus on the sociodemographic factors most strongly associated with CVD risk, such as age and marital status, while also addressing lifestyle factors such as diet, which can be influenced by employment and reproductive health choices. Further research is needed to explore the nuanced relationships between SES, lifestyle behaviours, and cardiovascular health, particularly in different cultural and geographical contexts. 5.3. Recommendations On the basis of the insights from this study on the impact of socioeconomic status, age, marital status, and other variables on CVD risk factors among pregnant women, some targeted recommendations for parents, spouses, healthcare professionals, policymakers, and hospital staff exist. First, spouses and family members should e ncourage pregnant women to stay active by offering support, whether by taking walks together, helping with household responsibilities, or facilitating time for exercise. Family support is essential for maintaining healthy activity levels, especially in the face of daily stressors. They should also ensure access to nutritious food choices at home by cooking healthy meals and maintaining a supportive environment for balanced eating habits. Marital support, particularly from spouses, can positively influence dietary habits by fostering a culture of healthy eating in the household. The management of stress levels should consider vulnerable people. Pregnant women may experience additional stress, particularly if they are balancing work and pregnancy. Providing emotional support and helping alleviate daily pressures can help reduce stress-related impacts on blood pressure. Second, nurses and prenatal healthcare providers monitor age and marital status-related risks of CVD. Given the positive correlation between age, BP and BMI, healthcare providers should monitor these variables closely. Nurses should assess older PW more frequently for hypertension and weight gain, providing tailored advice for managing these risk factors. Since contraceptive use is associated with better dietary practices, nurses could promote contraceptive counselling as part of general health education, as it may correlate with health-conscious behaviour. Implement Dietary Education for Working Pregnant Women. Since employment is associated with less healthy dietary habits, policymakers should advocate for nutritional support programs tailored to employed pregnant women, including resources for quick, healthy meal planning. Flexible work policies could be encouraged to provide breaks for meal preparation and promote work‒life balance. Hospital workers and maternal health clinicians should integrate routine screening for high blood pressure and high BMI, especially among older and married pregnant women, as these groups may be at greater risk of CVD. Maternal health clinics can establish protocols for closer monitoring of these populations. Offer onsite or referral-based nutritional counselling for pregnant women, especially those who are employed, to guide them in making healthy dietary choices. This service should emphasise easy-to-prepare, nutritious meals that align with busy schedules. Frequent prenatal visits should be promoted to monitor dietary habits and overall health, especially among high-risk groups. Regular check-ins with healthcare professionals can provide opportunities to reinforce healthy lifestyle behaviours. Stakeholders should take responsibility for educating the community on the impact of age on pregnancy health, emphasising the need for proactive health management for older pregnant women. Awareness campaigns could focus on the importance of regular blood pressure checks, healthy weight maintenance, and proper nutrition during pregnancy. Stakeholders should implement programs and initiatives that reach pregnant women of all socioeconomic backgrounds, emphasising that health behaviours such as poor diet are crucial for maternal and foetal health. Educational materials should be widely accessible and cover cost-effective ways to adopt healthy lifestyles. These recommendations are intended to address the different CVD risk factors identified in the study, emphasising lifestyle interventions, comprehensive support, and education across various demographics to promote the health of pregnant women and reduce risk factors for adverse pregnancy outcomes. Declarations Ethics approval and consent to participate The School of Nursing and Midwifery, college of Health and allied health, University of Cape Coast gave approval for the study. Consent for publication All the authors consented Competing interests There is no competing interest Funding Not applicable Availability of data and materials The data for the research is available on request. Acknowledgements I’m deeply grateful to my supervisor, Dr. Siakwa Mate , whose guidance, patience, and encouragement have been invaluable throughout this research journey. I would also like to thank Mr . Albert Opoku , the Principal of my school, for his constant support, motivation, and belief in me—his presence has always been a source of strength. Authors' information My name is Abu Tia Dimongso , and I work as a tutor at the Nursing and Midwifery Training College in Tepa . I’m a Registered Mental Health Nurse with over eight years of experience working at the bedside before transitioning into the classroom. I hold a Certificate and Diploma in Mental Health Nursing , a Bachelor of Science in Nursing , and a Master of Nursing degree. My background in clinical care continues to shape my approach to teaching and research. Authors' contributions I took the lead in writing this manuscript and managing the entire submission process. I coordinated the team of research assistants who supported data collection, personally handled data entry, and conducted the analysis myself. This work represents a culmination of personal commitment, practical experience, and academic effort. 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M., Coolman, M., Steegers, E. A., Jaddoe, V. W., Moll, H. A., Hofman, A., Mackenbach, J. P., & Raat, H. (2008). Low socioeconomic status is a risk factor for preeclampsia: the Generation R Study. Journal of Hypertension , 26 (6), 1200–1208. https://doi.org/10.1097/HJH.0b013e3282fcc36e Smits, J., & Steendijk, R. (2015). The International Wealth Index (IWI). In Social Indicators Research (Vol. 122, Issue 1, pp. 65–85). https://doi.org/10.1007/s11205-014-0683-x Trichopoulou, A., Costacou, T., Bamia, C., & Trichopoulos, D. (2003). Adherence to a Mediterranean Diet and Survival in a Greek Population. New England Journal of Medicine , 348 (26), 2599–2608. https://doi.org/10.1056/nejmoa025039 Vince, K., Brkic, M., Poljicanin, T., & Matijevic, R. (2021). Prevalence and impact of prepregnancy body mass index on pregnancy outcome: a cross-sectional study in Croatia. Journal of Obstetrics and Gynaecology , 41 (1), 55–59. https://doi.org/10.1080/01443615.2019.1706157 Vinturache, A., & Khalil, A. (2021). Maternal Physiological Changes in Pregnancy. The Global Library of Women’s Medicine . https://doi.org/10.3843/GLOWM.411323 Wang, W., Xie, X., Yuan, T., Wang, Y., Zhao, F., Zhou, Z., & Zhang, H. (2021a). Epidemiological trends of maternal hypertensive disorders of pregnancy at the global, regional, and national levels: a population-based study. BMC Pregnancy and Childbirth , 21 (1), 1–10. https://doi.org/10.1186/S12884-021-03809-2/FIGURES/3 Wang, W., Xie, X., Yuan, T., Wang, Y., Zhao, F., Zhou, Z., & Zhang, H. (2021b). Epidemiological trends of maternal hypertensive disorders of pregnancy at the global, regional, and national levels: a population-based study. BMC Pregnancy and Childbirth , 21 (1), 364. https://doi.org/10.1186/s12884-021-03809-2 WHO. (2021). Cardiovascular diseases (CVDs) . https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) WHO. (2023). Trends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division: executive summary . https://www.who.int/publications/i/item/9789240069251 Yan, X., Kong, F., Wang, A., Li, F., & Chen, L. (2021). Prevalence and the influencing factors for critical situation of 6 579 pregnant women with hypertensive disorders complicating pregnancy. Zhong Nan Da Xue Xue Bao. Yi Xue Ban = Journal of Central South University. Medical Sciences , 46 (8), 814–821. https://doi.org/10.11817/J.ISSN.1672-7347.2021.200601 Yarney, L. (2019). Does knowledge on sociocultural factors associated with maternal mortality affect maternal health decisions? A cross-sectional study of the Greater Accra region of Ghana. BMC Pregnancy and Childbirth , 19 (1), 47. https://doi.org/10.1186/s12884-019-2197-7 Additional Declarations No competing interests reported. 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06:58:34","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":250625,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7200664/v1/e7adc809fcca2389d6655286.html"},{"id":91953582,"identity":"d26d6507-25cc-4024-b65d-191160b5f0b3","added_by":"auto","created_at":"2025-09-23 06:58:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9296,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePast Medical and obstetric history of the participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: Field data 2024\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7200664/v1/9d598247828e3d0079ca18df.png"},{"id":91952196,"identity":"baaf1079-db40-4226-834c-59a5f297e262","added_by":"auto","created_at":"2025-09-23 06:50:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":104727,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSEM path diagram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: Field data, 2024\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7200664/v1/35748c526a5b7dcd4c485ea6.png"},{"id":91954222,"identity":"1afca17b-f264-4e20-a08f-437a9cee614a","added_by":"auto","created_at":"2025-09-23 07:06:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1914565,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7200664/v1/cbceb5f7-8d39-4d7f-9e96-e53f0dd9baf4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Socioeconomic Status and Cardiovascular Disease (CVD) Risk Factors among Pregnant Women in the Cape Coast Metropolitan Assembly – Central Region, Ghana","fulltext":[{"header":"Background of the Study","content":"\u003cp\u003eGlobally, noninfectious diseases are responsible for approximately 41 million mortalities annually, accounting for approximately 74% of all mortalities. Seventeen million individuals die as a result of NCDs before reaching age 70 each year, and developing countries are responsible for approximately 86% of these hasty deaths, most of which are from CVD (17.9 million) (Shand et al., 2023). Approximately 80% of mortalities related to CVDs are a result of stroke and heart attack, and for individuals aged 70 years and younger, 25% of the deaths occur without warning signs (Robinson, 2021). Cardiovascular disease risk factors occur in all individuals; however, because some unavoidable physiological changes that are bound to occur in some special populations, their cases seem different: pregnant women (Franjic, 2019; Gangakhedkar \u0026amp; Kulkarni, 2021; Vinturache \u0026amp; Khalil, 2021).\u003c/p\u003e\n\u003cp\u003eHigher rates of CVD have been reported in reproductive-aged women, with the occurrence of CVD during pregnancy posing a problem for both the attending physician and the unborn child. (Ludwig-Walz et al., 2022).\u003c/p\u003e\n\u003cp\u003eThe maternal mortality ratio (MMR) in Ghana has significantly decreased over the past three decades, dropping from 760 per 100,000 live births in 1990 to 310 in 2017, according to the 2017 Ghana Maternal Health Survey\u0026nbsp;(T. K. Boafor et al., 2021). Despite a significant drop, Ghana still\u0026nbsp;fears\u0026nbsp;meeting the SDG 3.1 of 70 deaths per 100,000 live\u0026nbsp;births\u0026nbsp;by the year 2030.\u0026nbsp;CVD\u0026nbsp;risk factors occurring during pregnancy are responsible for the countless number of CVDs in the expectant mothers\u0026nbsp;(Mikkola \u0026amp; Ylikorkala, 2024)\u0026nbsp;These conditions include previous hypertension, hypertension during pregnancy, preeclampsia, DM, obesity/overweight, tobacco use, high cholesterol, advanced age, multiple pregnancies, lack of prenatal care, and maternal features such as early menarche and polycystic ovarian syndrome\u0026nbsp;(Mitra \u0026amp; Ghosh, 2019).\u0026nbsp;The prevalence of hypertensive disorders in pregnant persons has significantly increased globally, from 10.9% in 1990 to 18.0% in 2019\u0026nbsp;(Mathew et al., 2023).\u0026nbsp;However, the incidence rate based on age was reduced, with a projected yearly percentage variation of -0.68. In 2019, the number of pregnancy-related fatalities caused by high BP was approximately 27,830, which is a 30% decrease compared with that in 1990\u0026nbsp;(Wang et al., 2021).\u0026nbsp;Approximately 10% of pregnancies are affected by hypertension, a significant contributor to both mortality and morbidity.\u0026nbsp;(Yan et al., 2021). The global prevalence of HDP\u0026nbsp;increased\u0026nbsp;from 16.3 million to 18.08 million between 1990 and 2019, representing an increase of 10.92%\u0026nbsp;(Wang et al., 2021b). Additionally, the proportion of pregnant women with hypertensive disorders facing challenges rose significantly from 28.1% in 2012 to 83.7% in 2019. Hypertension-related disorders are the primary reasons for maternal death during pregnancy, accounting for 14.0% of worldwide maternal fatalities. This is especially true in low- and middle-income countries, where it is a significant basis of death among pregnant women\u0026nbsp;(Agbeno et al., 2022).\u003c/p\u003e\n\u003cp\u003ePregnancy-related hypertensive diseases disproportionately impact Black, American Indian, and Alaskan Native women (Khedagi \u0026amp; Bello, 2021). Moreover, Black women have greater rates of severe morbidity and death associated with preeclampsia, whereas Hispanic women tend to have better pregnancy outcomes than Black women do. However, pregnancy outcomes for Hispanic women seem to be more favourable than those for black or Caucasian women, who have equal risk factors (Garovic et al., 2022).\u003c/p\u003e\n\u003cp\u003eSimilarly, in India and China, HDP has been linked to polycystic ovarian syndrome, BMI \u0026gt; 30 kg/m\u003csup\u003e2\u003c/sup\u003e, hyperglycemia during pregnancy, and advanced age (Mathew et al., 2023; Zhou et al, 2o22b). HDP is responsible for obstetric reasons for the deaths of pregnant women in developed nations ( Boafor, 2023).\u003c/p\u003e\n\u003cp\u003eMaternal deaths remain a worldwide challenge, with a projected rate of 223 deaths per 100,000 live births, 70% of which emanate from sub-Saharan Africa. Cardiovascular diseases (CVDs) significantly contribute to maternal mortality by exacerbating conditions such as preeclampsia, cardiomyopathy, and thromboembolism during pregnancy (Easter, 2024; Keepanasseril et al., 2021; Sahu et al., 2021). Approximately 50.9% of gravid women present one or more cardiovascular risk factors (Garanet et al., 2023). Gestational hypertension, in particular, is on the rise. In Ghana, hypertension during pregnancy is the leading cause of maternal death, accounting for approximately 13.2% of all deaths among women (Hermes et al., 2013; WHO, 2021, 2023).\u003c/p\u003e\n\u003cp\u003eDespite global efforts to reduce maternal mortality, Central Region continues to record a higher-than-average maternal mortality ratio (MMR), estimated at 319 deaths per 100,000 live births, than the national average of 301 in 2021 (GHANA STATISTICAL SERVICE, 2024). CVD risk factors are a significant contributor to these high mortality rates (Kotit \u0026amp; Yacoub, 2021). The development of risk factors for CVD during pregnancy is influenced by socioeconomic disparities, cultural norms, pre-existing maternal conditions, metabolic disorders, limited healthcare access, maternal age, urbanisation, unemployment and other lifestyle factors, such as physical inactivity and poor dietary patterns (O\u0026rsquo;Kelly et al., 2022; Yarney, 2019). However, insufficient research examining the effects of sociodemographic and maternal factors on the risk factors for CVD among pregnant women within the Central Region\u0026apos;s unique socioeconomic and demographic landscape is a major concern.\u003c/p\u003e\n\u003cp\u003eGiven this gap, it is essential to investigate the factors contributing to the development of hypertension, overweight/obesity and poor dietary patterns among pregnant women in the Central Region.\u003c/p\u003e"},{"header":"Methodology","content":"\u003ch2\u003e3.1. \u0026nbsp; \u0026nbsp; \u0026nbsp; Research Design\u003c/h2\u003e\n\u003cp\u003eThis quantitative cross-sectional descriptive analytic study aimed to assess the prevalence of overweight/obesity, BP and poor dietary patterns among pregnant women while simultaneously determining the correlations among SES, maternal characteristics and CVD risk factors.\u003c/p\u003e\n\u003ch2 id=\"_Toc197446674\"\u003e3.2.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;Study\u0026nbsp;Area\u003c/h2\u003e\n\u003cp\u003eThe Cape Coast metropolis is geographically situated along the Gulf of Guinea to the south. It is bordered by the Komenda Edina Eguafo Abrem Municipality to the west (starting at the Infrastructure bridge), Abura Asebu Kwamankese District to the east, and Twifu Heman Lower Denkyira district to the north. Covering an area of approximately 122 square, the metropolis stretches to Brabadze, which lies approximately 17 km from the capital of both the metropolis and the central region.\u003c/p\u003e\n\u003cp\u003eCape Coast\u0026apos;s population has been steadily growing, reflecting its increasing urbanisation and the migration of people from rural areas in search of employment and better living conditions. According to the most recent census data, the population of Cape Coast is estimated to range between 250,000 and 300,000 people. The population is diverse, with a mix of students, traders, civil servants, and individuals working in sectors such as tourism and fishing.\u003c/p\u003e\n\u003cp\u003ePopulation growth has also led to an increase in demand for housing, education, and healthcare services, placing pressure on existing infrastructure and local government services. This growing population underscores the need for sustainable urban planning and improved public services to ensure that development in the city benefits all residents.\u003c/p\u003e\n\u003cp\u003eFor this study, three hospitals within the Cape Coast Metropolitan area were randomly selected: Cape Coast Metropolitan Hospital (CCMH), University of Cape Coast Hospital (UCCH) and Adisadel Urban Health Centre.\u003c/p\u003e\n\u003ch2 id=\"_Toc197446675\"\u003e3.3.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Study Population\u003c/h2\u003e\n\u003cp\u003eThe research focused on expectant mothers who received antenatal care services from the three health facilities mentioned above. This included all expectant mothers who carried a live foetus who attended antenatal clinics at the selected study areas either for their first time or not. All women who met the inclusion criteria were interviewed.\u003c/p\u003e\n\u003ch2 id=\"_Toc197446676\"\u003e3.4.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Inclusion\u0026nbsp;and exclusion\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe study was conducted among pregnant women receiving antenatal care at three selected hospitals. The participants included those with confirmed pregnancies who were receiving antenatal care and who provided informed consent to participate in the study within the randomly selected hospitals. \u0026nbsp;Pregnant women with a history of inevitable abortion; pre-existing medical conditions requiring ongoing treatment; and those diagnosed with hypertension, diabetes, or heart disease were excluded from the study. Additionally, women with multiple gestations were excluded. The determination of multiple pregnancies was based on findings from ultrasound scans.\u003c/p\u003e\n\u003ch2 id=\"_Toc197446679\"\u003e3.5.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Sampling Procedure\u003c/h2\u003e\n\u003cp\u003eApproximately 86% of pregnant women attend antenatal clinics annually in Ghana (GSS, 2007).\u0026nbsp;The\u0026nbsp;Leslie\u0026ndash;Kish\u0026nbsp;formula was used to determine the population size\u0026nbsp;(Baligeh et al., 2023).\u0026nbsp;\u003cimg width=\"96\" height=\"31\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e. At 95% (1.96) confidence and\u0026nbsp;a\u0026nbsp;5% margin of error, a sample size of 150 was reached,\u0026nbsp;with 10% added to\u0026nbsp;accommodate\u0026nbsp;unreturned questionnaires.\u003c/p\u003e\n\u003cp\u003eData collection took approximately 3 months, starting in May 2024 and ending in August 2024. Three health institutions were randomly selected from the five major health facilities. The annual attendance from each health facility was used to determine the system of random systematic methods to be used in each institution over three months.\u003c/p\u003e\n\u003ch2\u003e\u003cspan id=\"_Toc197446680\"\u003e3.6.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Data collection instruments\u003c/span\u003e\u003c/h2\u003e\n\u003cp\u003eThe study used a structured questionnaire to gather sociodemographic, maternal, and anthropometric data for calculating participants\u0026apos; BMI. Section A, included age, education, employment, marital status, and religious affiliation. Section B included gestational age, family history of CVD, oral contraceptive use, breastfeeding, high blood pressure, gestational diabetes, PCOS, and fertility treatments.\u003c/p\u003e\n\u003cp\u003eParticipants\u0026apos; blood pressure was measured twice via an Omron digital machine at 15-minute intervals. They rested for 30 minutes before the BP was taken. The ideal BP was recorded via an adult cuff. Mean arterial pressure (MAP) was used for analysis, which is best for estimating the impact of BP on the cardiovascular system (DeMers \u0026amp; Wachs, 2019). MAP is calculated on the basis of the overall cardiac cycle and has a stronger correlation with certain CVD risk factors, such as body weight.\u003c/p\u003e\n\u003cp\u003eWeight and height were used to calculate the BMI of pregnant women (PWs), and the guidelines from the Institute of Medicine (IOM) were used as reference points (Rasmussen et al., 2009). The IOM average standard weight during pregnancy was subtracted from the current weight, taking into account the trimester. The standard weight gain in a normal woman with normal dietary practices is between 0.1 kg and 2 kg. The average weight gain in each trimester was used as a benchmark to determine the actual weight of the pregnant women.\u003c/p\u003e\n\u003cp\u003eThe International Wealth Index (IWI) (Smits \u0026amp; Steendijk, 2015) was used to determine the SES of participants. The variables assessed included; household assets such as television, refrigerators, phones, cars, bicycles, and utensils. The quality of toilet facilities, floor materials, and water supplies were also examined. The number of sleeping rooms in the house was also counted. The assets were assigned weights on the basis of their respective components. Housing features, such as floor materials, toilet facilities, and sleeping rooms, were classified as low, medium, or high quality. Individual scores were categorised as poorer, poor, rich, or very rich.\u003c/p\u003e\n\u003cp\u003eThe Mediterranean Diet tool (Trichopoulou et al., 2003) was used to assess participants\u0026apos; dietary patterns, with a score of 9 or above indicating adherence. The data were pretested at Anamabo health centre, and any unclear variables were revised. The tool was culturally appropriate and well suited to the research objectives, considering culturally specific activities within the Ghanaian context.\u003c/p\u003e\n\u003ch2\u003e3.7.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Sampling technique and sampling size\u003c/h2\u003e\n\u003cp\u003eA multistage sampling technique was employed. Three out of the five health facilities were randomly selected and used. Systematic random sampling was used to recruit participants who met the inclusion criteria.\u0026nbsp;A\u0026nbsp;sample size of 165 participants was determined via Cochran\u0026rsquo;s formula, ensuring adequate power for statistical analysis and reliability of the study findings. One hundred and sixty questionnaires were properly completed and subsequently used for the final analysis.\u003c/p\u003e\n\u003cp\u003eThree qualified midwives were recruited and trained as research assistants over three days on proper questionnaire administration, ethical considerations, and confidentiality protocols. Each day after data collection, the completed questionnaires were reviewed for completeness and securely stored. Participation was entirely voluntary, and informed consent was obtained from each participant, who was also ifinformed of their right to withdraw from the study at any time without penalty.\u003c/p\u003e\n\u003ch2 id=\"_Toc160817933\"\u003e3.8.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Data\u0026nbsp;Processing and Data Analysis.\u003c/h2\u003e\n\u003cp\u003eThe data were analysed via IBM SPSS Statistics version 27.0 for descriptive analysis and R 4.4.1 for inferential statistics. The data were meticulously entered into SPSS, and a visual inspection was conducted by two independent individuals. Means were computed to identify outliers and missing values. The results are presented in tables and graphs, and associations were identified using multivariate structural equation modelling (SEM). Cross-tabulation was used to assess SES and risk factors among participants.\u003c/p\u003e\n\u003ch2 id=\"_Toc197446683\"\u003e3.9.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Ethical consideration\u003c/h2\u003e\n\u003cp\u003eEthical clearance of the research was sought from the Review Committee of the Research Department of the Ghana Health Service after the approval of the topic from the School of Nursing and Midwifery at the University of Cape Coast. Participants who were randomly selected were taken to a noise-free office for privacy and confidentiality after they agreed to take part in the study. Their names were not needed. After each day\u0026apos;s activity, the researcher met with research assistants to collect all the completed questionnaires. They were kept under key and lock\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.2. Demographic characteristics of the respondents\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below shows the distributions of the various demographic characteristics.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic characteristics of the participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eFacility\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCMH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUCCH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADISADEL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMuslim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChristian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJHS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTrimester\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1st\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2nd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3rd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocioeconomic status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower middle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper middle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eAuthor\u0026rsquo;s field data 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAge, parity and menarche of participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStd. Deviation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeliveries (parity)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.4063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.51012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of menarche\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.8625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.89832\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of participants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29.0250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.69779\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e The study participants concerning the three hospitals are as follows: UCCH, Adisadel Urban Health Centre, and CCHM, representing 41.9%, 30.0%, and 28.1% of the sample, respectively. The majority of participants were Christians (87.5%), with the remaining 12.5% being Muslims. Nineteen percent had no formal education, 5.6% had completed primary school, 30.6% had completed junior high school (JHS), 33.1% had completed senior high school (SHS), and 28.8% had completed tertiary education. In terms of marital status, 53.1% were married, and 46.9% were not. Among them, 13.1%, 46.3%, and 40.6% were in their first, second, and third trimesters, respectively. The obstetric history revealed a mean parity order of 1.41 deliveries, with some participants reporting as many as six deliveries and others reporting none. The age of menarche ranged from 6\u0026ndash;20 years, with a mean onset age of 13.86 years. The age of the participants ranged from 16\u0026ndash;42 years, with an average age of 29.03 years.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.3. Past medical and obstetric history of the participants\u003c/h2\u003e\u003cp\u003eA small proportion (12.5%) reported a family history of CVD, and 18.3% had previously used oral contraceptives. In terms of obstetric history, more than half of the participants (51.2%) had breastfed their children for over one year. Approximately 3.8% reported a history of hypertension during pregnancy, another 3.8% had experienced gestational diabetes, 10.6% had a history of PCOS, and 6.9% had undergone fertility treatments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.4. Risk Factors for Cardiovascular Diseases\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean arterial pressure, BMI, and dietary pattern of the participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eMean Arterial Pressure\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOptimal (MAP\u0026thinsp;\u0026lt;\u0026thinsp;93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal (MAP b/n 93 and 105)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh normal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 1 HPT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 2 HPT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBody Mass Index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDietary pattern\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e160\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eAuthor\u0026rsquo;s field data 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe majority of participants (80.6%) had an optimal range, whereas 11.9% had normal MAP levels. A smaller proportion had elevated MAP values, with 5.0% classified as high-normal, 1.9% diagnosed with Grade 1 hypertension, and 0.6% with Grade 2 hypertension. In terms of BMI, 40.6% of the participants were normal but a significant proportion had higher-than-recommended body weights, with 26.9% classified as overweight and 30.0% categorised as obese. Only 2.5% of the participants were underweight. The dietary patterns revealed that 48.1% of the participants reported good dietary practices, whereas the remainder reported suboptimal dietary habits.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e1.2. Relationships between SES and CVD risk factors among PWs\u003c/h2\u003e\u003cp\u003eThis study explored the associations between SES and risk factors for CVD. The table below presents varying levels of risk across SES categories.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSocioeconomic status and CVD risk factors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSES Classification\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMAP (Mean arterial pressure)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eDietary pattern\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo risk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRisk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo risk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRisk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo risk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRisk\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower middle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper middle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eAuthor\u0026rsquo;s field data 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e above presents the pictorial analysis of the mothers-to-be wealth with CVD risk factors. The respondents with high BP were few across all SES groupings. One out of 18 (5.6%) had high MAP in the lower-middle SES group, with 1 (1.4%) and 2 (2.8%) had high MAP among the upper middle and high groups respectively. Mean arterial pressure is not a significant concern for the pregnant women in this study, regardless of their SES classification.\u003c/p\u003e\u003cp\u003eThe data indicates that women in the upper-middle SES group exhibited the highest prevalence of poor dietary patterns, 43 out of 70 (61.4%), followed by the lower-middle SES group, 9 out of 18 (50%), and the high SES group, 31 out of 72 (43.1%). In contrast, women in the lower-middle SES group may benefit from more home-prepared meals, despite financial limitations. These patterns highlight the complex relationship between SES and dietary behaviour, reinforcing existing evidence that education, income, and health acce\u003cb\u003ess\u003c/b\u003e significantly shape nutritional choices.\u003c/p\u003e\u003cp\u003eThe highest proportions of overweight or obesity were found in the high SES group, 46 out of 72 (63.9%) women and the lower-middle SES group, 11 out of 18 (61.1%), while the upper-middle SES group showed a relatively lower rate, 35 out of 70 (50%). These results indicate that BMI-related cardiovascular risks are significant at both ends of the socioeconomic spectrum.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e1.3. Prevalence of risk factors among participants\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e below provides an overview of CVD risk factors among respondents. Among the 160 participants, 15.6% (25) had no CVD risk factors. About 31.9% (51), 40% (64), and 12.5% (20) have 1, 2 and 3 CVD risk factor(s), respectively.\u003c/p\u003e\u003cp\u003eOverall, the majority (84.4%) of the gravidae have at least one CVD risk factor, with a notable proportion displaying multiple risk factors.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumber of risk factors per participant\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of risk factors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercent (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTwo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eAuthor\u0026rsquo;s field data 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e1.2. Correlation Matrix\u003c/h2\u003e\u003cp\u003eA correlation study revealed associations between SES and CVD risk variables, including blood pressure, BMI, dietary habits, and age, among pregnant individuals. These results are explained in full below.\u003c/p\u003e\u003cp\u003eThe correlation between SES and MAP was positive but modest (r\u0026thinsp;=\u0026thinsp;0.101), indicating a minor increase in blood pressure with increasing SES. This association implies that socioeconomic gains may be related to increase BP. A small positive association was also identified between SES and BMI (r\u0026thinsp;=\u0026thinsp;0.170), indicating that greater SES is associated with slightly higher BMI levels. This shows that women from higher socioeconomic backgrounds can suffer greater risk owing to increased BMI.\u003c/p\u003e\u003cp\u003eSES is slightly related to quality dietary habit (r\u0026thinsp;=\u0026thinsp;0.077). Increase in SES, increases quality dietary habit marginally. While this link is modest, it does suggest that women of higher SES may have somewhat greater access to healthful meals. Nevertheless, the small association shows that good dietary habit alone may not be a substantial modulator of CVD risk in this cohort.\u003c/p\u003e\u003cp\u003eFinally, age revealed a slight positive correlation with SES (r\u0026thinsp;=\u0026thinsp;0.199), indicating that older pregnant women in this sample tended to have higher SES. While age itself is a nonmodifiable characteristic, its link with SES might suggest that older women could be more financially secure or more educated, which can affect health-seeking behaviours and access to resources.\u003c/p\u003e\u003cp\u003eOverall, our data emphasise that among pregnant women, greater SES is marginally linked with specific CVD risk variables, such as BMI and blood pressure. Although the relationships are minor, the patterns identified here illustrate the subtle role that SES may have in impacting cardiovascular health risk during pregnancy. Further investigations are necessary to investigate these connections in more detail, especially to elucidate possible mediators and moderators of SES and CVD risk factors.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation Matrix\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDiet\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSES\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eAuthor\u0026rsquo;s field data 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e1.3. Structural equation model (SEM) results\u003c/h2\u003e\u003cp\u003eThis research examined the correlation between SES and cardiovascular disease (CVD) risk variables, including MAP, BMI, and dietary habits, in pregnant women. Structural equation modelling (SEM) was used to evaluate the direct impacts of SES on each risk factor while controlling for pertinent confounders such as age, educational attainment, job status, marital status, prenatal visits, parity, cardiovascular disease history, contraceptive usage, and breastfeeding.\u003c/p\u003e\u003cp\u003eThe following is a comprehensive overview of the results for each outcome variable.\u003c/p\u003e\u003cp\u003eAge had a positive and substantial influence on MAP (estimate\u0026thinsp;=\u0026thinsp;0.695, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This relationship highlights the significance of age as a risk factor for hypertension. Marital status shows a significant positive correlation with BP (estimate\u0026thinsp;=\u0026thinsp;4.091, p\u0026thinsp;=\u0026thinsp;0.010).\u003c/p\u003e\u003cp\u003eSES had no significant effect on BP (estimate = -0.002, p\u0026thinsp;=\u0026thinsp;0.957), indicating that economic resources alone may not directly affect MAP in this demographic. Other variables, such as employment (estimate\u0026thinsp;=\u0026thinsp;0.737, p\u0026thinsp;=\u0026thinsp;0.547), educational level (estimate\u0026thinsp;=\u0026thinsp;1.198, p\u0026thinsp;=\u0026thinsp;0.145), and contraceptive use (estimate\u0026thinsp;=\u0026thinsp;1.231, p\u0026thinsp;=\u0026thinsp;0.517), also did not reach statistical significance, indicating that these factors may not be primary determinants of MAP among expectant mothers.\u003c/p\u003e\u003cp\u003eBMI is strongly associated with age and marital status. Age had a considerable positive influence on BMI (estimate\u0026thinsp;=\u0026thinsp;0.408, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), revealing a pattern of higher BMI with advancing age. Marital status was similarly associated with BMI (estimate\u0026thinsp;=\u0026thinsp;4.318, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that married women or those in stable relationships might experience lifestyle factors that contribute to a higher BMI.\u003c/p\u003e\u003cp\u003eSES had a lower, nonsignificant influence on BMI (estimate\u0026thinsp;=\u0026thinsp;0.037, p\u0026thinsp;=\u0026thinsp;0.109), indicating that income or wealth alone may not predict BMI. The absence of a substantial link with SES shows that variables beyond economic resources, possibly including access to health education or community support, may mitigate this correlation. Other factors, such as job status (estimate = -0.619, p\u0026thinsp;=\u0026thinsp;0.370) and parity (estimate\u0026thinsp;=\u0026thinsp;0.559, p\u0026thinsp;=\u0026thinsp;0.174), were not significantly linked with BMI.\u003c/p\u003e\u003cp\u003eDietary Pattern, a crucial component in reducing CVD risk, revealed significant relationships with job status and contraceptive usage, but SES and age did not indicate relevant impacts. Employment status had a significant negative relationship with dietary patterns (estimate = -0.687, p\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e\u003cp\u003eContraceptive usage positively affects food patterns (estimate\u0026thinsp;=\u0026thinsp;1.034, p\u0026thinsp;=\u0026thinsp;0.021). SES had a minimal, nonsignificant effect on eating habit (estimate\u0026thinsp;=\u0026thinsp;0.009, p\u0026thinsp;=\u0026thinsp;0.371). Similarly, education level (estimate\u0026thinsp;=\u0026thinsp;0.014, p\u0026thinsp;=\u0026thinsp;0.944) and age (estimate = -0.075, p\u0026thinsp;=\u0026thinsp;0.101) did not significantly affect food patterns.\u003c/p\u003e\u003cp\u003eThe findings underline that age and marital status are key drivers of BMI and BP among pregnant women.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression Results for Cardiovascular Disease Risk Factors among Pregnant Women\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ez value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMAP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.957\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducational Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployment Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.547\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo. of Antenatal Visits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.881\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHistory of CVD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContraceptive Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.517\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBreastfeeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.624\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducational Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployment Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.370\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo. of Antenatal Visits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.315\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHistory of CVD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.820\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContraceptive Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.248\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBreastfeeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDIET\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducational Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.944\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployment Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.729\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo. of Antenatal Visits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.490\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHistory of CVD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.701\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContraceptive Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBreastfeeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.566\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAuthor\u0026rsquo;s field data 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe figure below illustrates the structural relationships among socio-economic status, dietary patterns, and cardiovascular risk factors.\u0026rdquo;\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSource: Field data, 2024\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel Summary and Fit Measures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe structural equation model (SEM) analysis provided the following model summary and fit measurements, offering insight into the quality of the model fit and the estimation process.\u003c/p\u003e\u003cp\u003eThe model uses a maximum likelihood (ML) estimator, optimised via the NLMINB approach. With a total of 160 data points, the model has 50 parameters. The user model test result is 0.000 with 0 degrees of freedom, indicating that the model is completely saturated and fits the observed data exactly. The baseline model test statistic, by comparison, is 133.712 with 46 degrees of freedom and a very significant p value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), demonstrating that the baseline model poorly matches the data compared with the user model.\u003c/p\u003e\u003cp\u003eThe comparative fit index (CFI) and Tucker‒Lewis index (TLI) both obtained values of 1.000, denoting optimal model fit, with values close to 1 indicating outstanding fit in both indices. The root mean square error of approximation (RMSEA) was 0.000, with a 90% confidence range spanning from 0.000\u0026ndash;0.000, showing no disagreement between the model and observed data in the population. This is further reinforced by the standardised root mean square residual (SRMR) of 0.000, indicating a good match since SRMR values closer to 0 imply a better fit.\u003c/p\u003e\u003cp\u003eThe model\u0026rsquo;s loglikelihood is -1985.729, with corresponding Akaike information criterion (AIC) and Bayesian information criterion (BIC) values of 4071.459 and 4225.218, respectively. The sample-size-adjusted BIC (SABIC) is 4066.936. These information requirements aid in analysing model parsimony, with lower values typically suggesting better model fit than alternative models do.\u003c/p\u003e\u003cp\u003eThe findings reveal a good model fit with 0% residual error and strong fit indices. These results demonstrate the robustness of the model in capturing the associations between socioeconomic status and cardiovascular risk variables among pregnant women.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel Summary and Fit Measures\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEstimator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eML\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOptimisation Method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNLMINB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Model Parameters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest Statistic (User Model)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDegrees of Freedom (User Model)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest Statistic (Baseline Model)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133.712\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDegrees of Freedom (Baseline Model)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP value (Baseline Model)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoglikelihood (User Model)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1985.729\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4071.459\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4225.218\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSABIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4066.936\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e90% CI Lower (RMSEA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e90% CI Upper (RMSEA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSRMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eSource: field data, 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.10. \u003cb\u003eCovariances\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eThe covariance estimates among CVD risk factors and socioeconomic indicators, specifically MAP, BMI, and DIET, offer insights into the interrelationships between these variables.\u003c/p\u003e\u003cp\u003eA substantial positive correlation was discovered between BP and BMI (estimate\u0026thinsp;=\u0026thinsp;13.684, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a normalised value of 0.313. These data demonstrate a substantial positive correlation between BP and BMI, indicating that when BMI increases, BP may also increase among pregnant women. Given that both increased BP and increased BMI are known risk factors for CVD, this association underscores the relevance of BMI in impacting cardiovascular health during pregnancy.\u003c/p\u003e\u003cp\u003eThe correlation between MAP and DIET, although positive (estimate\u0026thinsp;=\u0026thinsp;0.552), was also nonsignificant (p\u0026thinsp;=\u0026thinsp;0.704), with a normalised value of 0.030. This finding shows that food habits, as evaluated in this model, may have a minimal direct association with BP levels among the research participants.\u003c/p\u003e\u003cp\u003eThe negative correlation between BMI (estimate = -6.784, p\u0026thinsp;=\u0026thinsp;0.082) approached significance, with a normalised value of -0.139.\u003c/p\u003e\u003cp\u003eSimilarly, the covariances between BMI and DIET (estimate = -0.521, p\u0026thinsp;=\u0026thinsp;0.525) were also nonsignificant, with standardised values of -0.050. These data imply that dietary habits, as defined here, are not strongly linked with either BMI.\u003c/p\u003e\u003cp\u003eIn summary, whereas BMI and BP show a substantial positive link, the associations between other CVD risk variables and SES-related indicators are modest and largely nonsignificant.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCovariances\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCovariance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Err\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP values\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStd.lv\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStd.all\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAPBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.313\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAP DIET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMIDIET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eSource: field data, 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eVariances\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe variance estimates for the CVD risk factors and SES indicators provide insights into the degree of variability for each component among pregnant women in this research.\u003c/p\u003e\u003cp\u003eFor the parameters MAP, BMI, and DIET, the variance estimate was 0.000, with matching standard errors and z values similar to 0.000, showing that these variables did not contribute further unexplained variability to the model.\u003c/p\u003e\u003cp\u003eMAP revealed a significant variance estimate of 77.560 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a normalised estimate of 0.888, indicating high unexplained variability in BP across the subjects.\u003c/p\u003e\u003cp\u003eBMI also exhibited substantial variability, with an estimate of 24.631 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a standardised value of 0.750. This reveals large variability in BMI levels among PW, supporting BMI as a key CVD risk factor that varies between people, presumably related to socioeconomic or lifestyle disparities.\u003c/p\u003e\u003cp\u003eFinally, the dietary pattern latent variable (diet) demonstrated a significant variance of 4.341 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a standardised variance of 0.880. These data suggest considerable diversity in food patterns among women, indicating that poor nutrition is a major component contributing to individual CVD risk profiles.\u003c/p\u003e\u003cp\u003eIn summary, BP, BMI, and diet all revealed considerable and noteworthy variability, highlighting their importance as critical determinants related to cardiovascular health among pregnant women. These differences underscore the relevance of addressing these modifiable risk variables in programs aimed at lowering CVD risk within this group.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariances in the CVD risk factors for the SEM\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Err\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP values\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStd.lv\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStd.all\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77.560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.880\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eSource: field data, 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the relationships between SES and CVD risk factors in pregnant women, with a focus on blood pressure (BP), body mass index (BMI), and dietary habits. Similar research globally aligns with or contrasts these findings in various ways.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSocio-demographic and maternal characteristics of pregnant women at risk of CVD\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe socio-demographic and maternal characteristics of the participants revealed important patterns in the prevalence of CVD risk factors. Among the participants, 51.9% exhibited poor dietary patterns, 57.5% had abnormal body weights (overweight or obese), and only 2.5% had high mean arterial pressure (MAP). These findings align with studies conducted in different regions.\u003c/p\u003e\u003cp\u003eThe findings of this study are consistent with those of other studies showing a high prevalence of abnormal BMI among pregnant women. Chairat et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported that more than 50% of pregnant women were classified as overweight. Similarly, Deputy et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported that 47.2% of pregnant women had a BMI above normal. A recent systematic review of obesity and overweight among pregnant women showed a 43.8% pooled prevalence (Kent et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The high prevalence of abnormal BMI in this study further emphasises the growing concern over abnormal weight gain during pregnancy, which is a well-known risk factor for CVD and other health complications. However, the prevalence in this study is significantly higher than that reported in Croatia, where only 29.2% of pregnant women were classified as overweight or obese (Vince et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Cultural perceptions of body size may explain some of these variations, as larger body sizes in some African and African diaspora communities are often associated with wealth, beauty, and dignity (Appiah et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hoenink et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Naigaga et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These cultural factors may contribute to higher rates of obesity and overweight in these populations.\u003c/p\u003e\u003cp\u003eIn terms of dietary habits, 51.9% of the participants reported poor dietary patterns, which is consistent with findings from Garanet et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Franck (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Researchers have shown that unhealthy diets contribute significantly to hypertension and metabolic disorders in pregnancy worldwide and are directly correlated with future cardiovascular risk. These findings align with the results of the present study, highlighting poor dietary habits as a major risk factor for CVD among pregnant women. The prevalence of poor dietary patterns in this study contrasts with the lower percentages observed in some developing countries, where limited access to affordable, nutritious food might contribute to poor dietary habits, further exacerbating cardiovascular risks.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSocio-demographic Predictors of Pregnant Women at Risk of CVD\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe analysis identified several socio-demographic factors, such as age and marital status, as significant predictors of CVD risk among pregnant women. Specifically, older maternal age was significantly correlated with increased BMI (estimate\u0026thinsp;=\u0026thinsp;0.408, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which is a well-established risk factor for CVD. Age-related increases in BMI are documented in studies by Gozuyesil et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which suggest that older women tend to have higher BMIs due to metabolic changes and lifestyle factors that become more pronounced with age. Similarly, marital status was found to influence BMI, with married women being more likely to have a higher BMI (estimate\u0026thinsp;=\u0026thinsp;4.318, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), likely due to lifestyle factors, shared resources, and social support mechanisms. This finding is consistent with research by (Corr\u0026ecirc;a et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Silva et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), who reported that marital status was positively correlated with BMI in pregnant women (Lee et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, SES did not have a significant effect on BMI in this study, which contrasts with findings from studies in high-income countries, such as (Kominiarek et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which highlighted that lower SES was linked to elevated BMI due to limited access to nutritious food and healthcare services. The lack of a significant SES impact in this study could be due to factors such as greater access to healthcare resources or more homogenous access to nutrition and exercise in the sample population.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInfluence of Socio-demographic Factors on Cardiovascular Risk among Diverse Maternal Groups\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe findings from this study revealed that socio-demographic factors, including age, marital status, and employment, influence CVD risk factors such as BMI and dietary habits. For example, studies in high-income countries often emphasise SES as a key predictor of BMI, with lower SES being linked to higher obesity rates due to limited access to healthcare and nutritious food (Kominiarek et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In contrast, the present study revealed that sociodemographic factors such as age and marital status had more significant effects on CVD risk factors than SES alone. The lack of a strong SES effect may be due to various factors, including access to healthcare resources or community-based health interventions that mitigate the negative impacts of lower SES.\u003c/p\u003e\u003cp\u003eEmployment status and contraceptive use were significant predictors of dietary patterns, with employed women reporting more time constraints that negatively impacted their dietary habits, which aligns with findings from Bangladesh (Islam et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Contraceptive use was positively associated with better dietary patterns, similar to findings from (Barker et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe overall findings suggest that while sociodemographic factors such as age and marital status significantly influence CVD risk factors among pregnant women, the role of SES is complex and may vary on the basis of the local context, healthcare infrastructure, and cultural norms. This study underscores the importance of considering both demographic and lifestyle factors when designing interventions to reduce CVD risk during pregnancy, especially in diverse socioeconomic contexts.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e5.1. Summary\u003c/h2\u003e\u003cp\u003eThis study aimed to explore the relationship between socioeconomic status (SES) and the presence of cardiovascular risk factors among pregnant women. The results provide valuable insights into the distribution and severity of key cardiovascular risk factors, including mean arterial pressure (MAP), body mass index (BMI), and dietary patterns. The findings revealed that cardiovascular risk is prevalent across all SES groups, albeit with variations in the number and severity of risk factors.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrevalence of Cardiovascular Risk Factors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe prevalence of cardiovascular risk factors among pregnant women in this study was significant across all SES categories. The MAP risk was notably low, with only 2.5% of women exhibiting elevated MAP. These findings suggest that MAP is not a major concern in this study population. The relatively low MAP risk across SES groups is encouraging, as effective monitoring and control of blood pressure could be contributing factors. However, this finding does not discount the need for continued vigilance regarding hypertension during pregnancy, particularly in women with multiple risk factors.\u003c/p\u003e\u003cp\u003eIn contrast, BMI has emerged as a key area of concern. A significant proportion of women (57.5%) were classified as being at risk due to being overweight or obese. This finding is particularly concerning, as high BMI is an established risk factor for both cardiovascular diseases and complications during pregnancy, such as gestational diabetes and preeclampsia. The prevalence of overweight and obesity underscores the importance of interventions aimed at promoting healthy weight and preventing excessive weight gain during pregnancy.\u003c/p\u003e\u003cp\u003eDietary patterns were also a significant concern, with 51.9% of participants exhibiting poor eating habits. A poor diet, characterised by low fruit and vegetable intake and high consumption of processed foods, is a known contributor to CVD. The high proportion of women with poor dietary patterns suggests that nutritional interventions should be prioritised as part of prenatal care. The role of dietary education in managing and reducing CVD cannot be overstated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSES and CVD risk factors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe relationship between socioeconomic status (SES) and cardiovascular risk factors was an important aspect of the analysis. The study revealed that lower-middle SES women were particularly vulnerable to the accumulation of multiple risk factors, with 50% of participants in this group having two risk factors. This finding suggests that lower SES is associated with a greater likelihood of experiencing multiple cardiovascular risks. The increased risk in this group could be attributed to several factors, including limited access to healthcare, poor diet, and reduced opportunities for physical activity, all of which are common in lower SES populations.\u003c/p\u003e\u003cp\u003eInterestingly, the upper-middle and high SES groups also presented notable levels of CVD risk. While lower-SES women accounted for a greater proportion of women with two risk factors, upper-middle- and high-SES women presented a significant incidence of two and three risk factors, especially in the high-SES group (44.4%). This means that higher SES does not offer complete protection against CVD risk. Even in these groups, factors such as sedentary lifestyles, stress, and poor dietary habits seem to contribute to the accumulation of CVD. These findings challenge the common assumption that higher SES automatically correlates with better health outcomes and underscore the need for targeted interventions that address lifestyle factors common to all SES groups.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSocioeconomic status and its impact on individual CVD risk factors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe correlation between SES and BP, although modest (r\u0026thinsp;=\u0026thinsp;0.101), suggests that higher SES might be linked with slightly elevated BP among pregnant women. While the association was small, it is in line with findings from studies that suggest that individuals from higher SES backgrounds often experience increased stress or engage in less healthy lifestyle practices, which may contribute to higher blood pressure (Kraft \u0026amp; Kraft, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, this result contrasts with those of some studies, which have shown a more substantial impact of SES on BP, likely due to variations in lifestyle factors, access to healthcare, and diet (Qin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this cohort, the small effect size may be attributable to other dominant factors, such as age or marital status, which may overshadow SES in influencing BP levels.\u003c/p\u003e\u003cp\u003eThe finding of a modest positive correlation between SES and BMI (r\u0026thinsp;=\u0026thinsp;0.170) also mirrors the literature, which often links higher SES with increased BMI due to better access to food, sedentary lifestyles, or stress-induced eating. However, the correlation was still relatively weak, indicating that while SES plays a role in BMI, other factors, such as physical activity and health education, might mitigate its effect. This aligns with studies suggesting that higher SES alone does not guarantee a higher BMI but is often compounded by other variables, such as dietary habits and exercise patterns.\u003c/p\u003e\u003cp\u003eIn terms of dietary habits, the weak positive correlation between SES and diet quality (r\u0026thinsp;=\u0026thinsp;0.077) suggests that women with higher SES may have slightly better dietary patterns. While this finding is consistent with the notion that wealthier individuals typically have access to healthier food, the modest association indicates that SES alone may not significantly influence diet. Research has shown that access to quality food is a key determinant of dietary habits, but factors such as cultural preferences, time constraints, and education may play a more significant role in shaping food choices (Larson et al., 2006). Therefore, while SES is a contributing factor, its impact on diet quality appears to be limited in this cohort.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSeverity of Risk Factor Accumulation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhile the study revealed that no participant had all four cardiovascular risk factors, the accumulation of two or three risk factors was prevalent in many participants. This is particularly significant, as the presence of multiple risk factors can exacerbate the overall risk of developing CVD during pregnancy and later in life. The upper-middle SES group had the highest proportion of women with three risk factors, suggesting that even women with higher SES are at risk for severe CVD complications.\u003c/p\u003e\u003cp\u003eThe lowest proportion of women with a lower-middle SES had three risk factors, yet a high proportion of women still had two risk factors. This highlights the compounding effects of multiple CVD risks and the importance of early intervention in these women to prevent the escalation of risk factors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMarital Status and Age as Predictors of CVD Risk\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study revealed that age was a significant predictor of both BP (estimate\u0026thinsp;=\u0026thinsp;0.695, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and BMI (estimate\u0026thinsp;=\u0026thinsp;0.408, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which is consistent with many studies linking advanced age with increased CVD risk. Older pregnant women in this study presented higher BP and BMI values, reinforcing the idea that age is a critical determinant of CVD risk factors. This finding underscores the importance of age as a nonmodifiable risk factor in pregnancy, which warrants increased monitoring and early intervention for older pregnant women.\u003c/p\u003e\u003cp\u003eSimilarly, marital status was found to have a significant positive association with both BP (estimate\u0026thinsp;=\u0026thinsp;4.091, p\u0026thinsp;=\u0026thinsp;0.010) and BMI (estimate\u0026thinsp;=\u0026thinsp;4.318, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that married or cohabiting women may experience increased CVD risk compared with their unmarried counterparts. This aligns with studies that indicate that marital status can impact health outcomes, possibly due to stress, lifestyle habits, or shared resources within a marriage (Umberson et al., 2010). Married women may face additional stressors or engage in less healthy behaviours, which could contribute to higher BP and BMI. These findings further highlight the need for targeted prenatal care that considers marital status a risk factor.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInteractions among SES, lifestyle factors, and CVD risk\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDietary habits were found to be significantly influenced by employment status (estimate = -0.687, p\u0026thinsp;=\u0026thinsp;0.018) and contraceptive use (estimate\u0026thinsp;=\u0026thinsp;1.034, p\u0026thinsp;=\u0026thinsp;0.021), whereas SES had no significant effect. The employed women were found to have poorer dietary patterns, possibly due to time constraints, stress, or limited access to nutritious food due to busy schedules. This finding is consistent with studies that show that employed individuals may struggle to maintain healthy eating habits due to a lack of time or resources. On the other hand, contraceptive use was positively associated with better dietary habits, which could be reflective of health-conscious behaviours among women using contraception. These findings suggest that factors beyond SES, such as employment status and reproductive health choices, may play a more significant role in influencing dietary habits.\u003c/p\u003e\u003cp\u003eThe findings of this study demonstrate some alignment with existing research on the relationship between sociodemographic factors and CVD risk, particularly the significant influence of age and marital status. However, the study also presents contrasting findings in terms of the role of SES. While SES is typically viewed as a significant predictor of health outcomes, its impact on CVD risk in this cohort was modest, suggesting that SES may not be as influential as other sociodemographic factors, such as age or marital status. This could be due to the specific context of the study, where other unmeasured variables (e.g., access to healthcare, local health policies, or community support systems) might have played a more significant role in shaping maternal health.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation between SES and risk factors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe analysis of correlations between SES and CVD risk factors revealed modest relationships. A slight positive correlation between SES and BMI indicated that women with higher SES are more likely to have higher BMIs, potentially due to dietary patterns and lifestyle factors. A slight positive correlation between SES and dietary patterns was observed, although the relationship was not strong. This finding indicates that women in higher SES groups may, on average, exhibit slightly better dietary habits than those in lower SES groups, but the correlation is not robust enough to suggest a definitive trend.\u003c/p\u003e\u003cp\u003eThe findings highlight the prevalence of CVD risk factors among pregnant women across all SES groups, with BMI and dietary patterns emerging as key risk factors. While the MAP risk was low, multiple risk factors were common, particularly in the lower-middle SES group, where women were more likely to experience two or more risks. However, the upper-middle and high SES groups also presented considerable risk, particularly in terms of BMI and physical inactivity. These findings suggest that CVD risk during pregnancy is prevalent across all SES categories and that interventions should focus on managing modifiable risk factors, such as diet and weight management, in pregnant women. Further research is needed to explore the underlying causes of these associations and to develop tailored strategies to reduce cardiovascular risk in pregnant women from different socioeconomic backgrounds.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e5.2. Conclusion\u003c/h2\u003e\u003cp\u003eThis study underscores the complex interplay between sociodemographic and lifestyle factors in influencing cardiovascular risk among pregnant women. While age and marital status emerged as strong predictors of CVD risk factors, SES had a more modest impact. The findings suggest that future interventions should focus on the sociodemographic factors most strongly associated with CVD risk, such as age and marital status, while also addressing lifestyle factors such as diet, which can be influenced by employment and reproductive health choices. Further research is needed to explore the nuanced relationships between SES, lifestyle behaviours, and cardiovascular health, particularly in different cultural and geographical contexts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e5.3. Recommendations\u003c/h2\u003e\u003cp\u003eOn the basis of the insights from this study on the impact of socioeconomic status, age, marital status, and other variables on CVD risk factors among pregnant women, some targeted recommendations for parents, spouses, healthcare professionals, policymakers, and hospital staff exist.\u003c/p\u003e\u003cp\u003eFirst, spouses and family members should \u003cb\u003ee\u003c/b\u003encourage pregnant women to stay active by offering support, whether by taking walks together, helping with household responsibilities, or facilitating time for exercise. Family support is essential for maintaining healthy activity levels, especially in the face of daily stressors.\u003c/p\u003e\u003cp\u003eThey should also ensure access to nutritious food choices at home by cooking healthy meals and maintaining a supportive environment for balanced eating habits. Marital support, particularly from spouses, can positively influence dietary habits by fostering a culture of healthy eating in the household.\u003c/p\u003e\u003cp\u003eThe management of stress levels should consider vulnerable people. Pregnant women may experience additional stress, particularly if they are balancing work and pregnancy. Providing emotional support and helping alleviate daily pressures can help reduce stress-related impacts on blood pressure.\u003c/p\u003e\u003cp\u003eSecond, nurses and prenatal healthcare providers monitor age and marital status-related risks of CVD. Given the positive correlation between age, BP and BMI, healthcare providers should monitor these variables closely. Nurses should assess older PW more frequently for hypertension and weight gain, providing tailored advice for managing these risk factors.\u003c/p\u003e\u003cp\u003eSince contraceptive use is associated with better dietary practices, nurses could promote contraceptive counselling as part of general health education, as it may correlate with health-conscious behaviour.\u003c/p\u003e\u003cp\u003eImplement Dietary Education for Working Pregnant Women. Since employment is associated with less healthy dietary habits, policymakers should advocate for nutritional support programs tailored to employed pregnant women, including resources for quick, healthy meal planning. Flexible work policies could be encouraged to provide breaks for meal preparation and promote work‒life balance.\u003c/p\u003e\u003cp\u003eHospital workers and maternal health clinicians should integrate routine screening for high blood pressure and high BMI, especially among older and married pregnant women, as these groups may be at greater risk of CVD. Maternal health clinics can establish protocols for closer monitoring of these populations.\u003c/p\u003e\u003cp\u003eOffer onsite or referral-based nutritional counselling for pregnant women, especially those who are employed, to guide them in making healthy dietary choices. This service should emphasise easy-to-prepare, nutritious meals that align with busy schedules.\u003c/p\u003e\u003cp\u003eFrequent prenatal visits should be promoted to monitor dietary habits and overall health, especially among high-risk groups. Regular check-ins with healthcare professionals can provide opportunities to reinforce healthy lifestyle behaviours.\u003c/p\u003e\u003cp\u003eStakeholders should take responsibility for educating the community on the impact of age on pregnancy health, emphasising the need for proactive health management for older pregnant women. Awareness campaigns could focus on the importance of regular blood pressure checks, healthy weight maintenance, and proper nutrition during pregnancy. Stakeholders should implement programs and initiatives that reach pregnant women of all socioeconomic backgrounds, emphasising that health behaviours such as poor diet are crucial for maternal and foetal health. Educational materials should be widely accessible and cover cost-effective ways to adopt healthy lifestyles.\u003c/p\u003e\u003cp\u003eThese recommendations are intended to address the different CVD risk factors identified in the study, emphasising lifestyle interventions, comprehensive support, and education across various demographics to promote the health of pregnant women and reduce risk factors for adverse pregnancy outcomes.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe School of Nursing and Midwifery, college of Health and allied health, University of Cape Coast gave approval for the study. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors consented\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no competing interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for the research is available on request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI\u0026rsquo;m deeply grateful to my supervisor, \u003cstrong\u003eDr. Siakwa Mate\u003c/strong\u003e, whose guidance, patience, and encouragement have been invaluable throughout this research journey. I would also like to thank Mr\u003cstrong\u003e. Albert Opoku\u003c/strong\u003e, the Principal of my school, for his constant support, motivation, and belief in me\u0026mdash;his presence has always been a source of strength.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMy name is \u003cstrong\u003eAbu Tia Dimongso\u003c/strong\u003e, and I work as a tutor at the \u003cstrong\u003eNursing and Midwifery Training College in Tepa\u003c/strong\u003e. I\u0026rsquo;m a Registered Mental Health Nurse with over eight years of experience working at the bedside before transitioning into the classroom. I hold a \u003cstrong\u003eCertificate and Diploma in Mental Health Nursing\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e a \u003cstrong\u003eBachelor of Science in Nursing\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eand a\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003eMaster of Nursing\u003c/strong\u003e\u0026nbsp;\u003c/strong\u003edegree. My background in clinical care continues to shape my approach to teaching and research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI took the lead in writing this manuscript and managing the entire submission process. I coordinated the team of research assistants who supported data collection, personally handled data entry, and conducted the analysis myself. This work represents a culmination of personal commitment, practical experience, and academic effort.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgbeno, E. K., Osarfo, J., Owusu, G. B., Opoku Aninng, D., Anane-Fenin, B., Amponsah, J. A., Ashong, J. A., Amanfo, A. O., Ken-Amoah, S., Kudjonu, H. T., \u0026amp; Mohammed, M. (2022). Knowledge of hypertensive disorders of pregnancy among pregnant women attending antenatal clinic at a tertiary hospital in Ghana. \u003cem\u003eSAGE Open Medicine\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e. https://doi.org/10.1177/20503121221088432\u003c/li\u003e\n\u003cli\u003eAppiah, C. A., Otoo, G. E., \u0026amp; Steiner-Asiedu, M. (2016). 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Epidemiological trends of maternal hypertensive disorders of pregnancy at the global, regional, and national levels: a population-based study. \u003cem\u003eBMC Pregnancy and Childbirth\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(1), 1\u0026ndash;10. https://doi.org/10.1186/S12884-021-03809-2/FIGURES/3\u003c/li\u003e\n\u003cli\u003eWang, W., Xie, X., Yuan, T., Wang, Y., Zhao, F., Zhou, Z., \u0026amp; Zhang, H. (2021b). Epidemiological trends of maternal hypertensive disorders of pregnancy at the global, regional, and national levels: a population-based study. \u003cem\u003eBMC Pregnancy and Childbirth\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(1), 364. https://doi.org/10.1186/s12884-021-03809-2\u003c/li\u003e\n\u003cli\u003eWHO. (2021). \u003cem\u003eCardiovascular diseases (CVDs)\u003c/em\u003e. https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)\u003c/li\u003e\n\u003cli\u003eWHO. (2023). \u003cem\u003eTrends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division: executive summary\u003c/em\u003e. https://www.who.int/publications/i/item/9789240069251\u003c/li\u003e\n\u003cli\u003eYan, X., Kong, F., Wang, A., Li, F., \u0026amp; Chen, L. (2021). Prevalence and the influencing factors for critical situation of 6 579 pregnant women with hypertensive disorders complicating pregnancy. \u003cem\u003eZhong Nan Da Xue Xue Bao. Yi Xue Ban = Journal of Central South University. Medical Sciences\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(8), 814\u0026ndash;821. https://doi.org/10.11817/J.ISSN.1672-7347.2021.200601\u003c/li\u003e\n\u003cli\u003eYarney, L. (2019). Does knowledge on sociocultural factors associated with maternal mortality affect maternal health decisions? A cross-sectional study of the Greater Accra region of Ghana. \u003cem\u003eBMC Pregnancy and Childbirth\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1), 47. https://doi.org/10.1186/s12884-019-2197-7\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Socioeconomic status, cardiovascular diseases, pregnant woman and Risk factors, overweight, obese, blood pressure, dietary pattern","lastPublishedDoi":"10.21203/rs.3.rs-7200664/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7200664/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNoncommunicable diseases (NCDs) account for 74% of global mortalities, with cardiovascular diseases (CVDs) being the leading cause. The objective of this study was to investigate the associations between socioeconomic status (SES) and CVD risk factors among pregnant women in the Cape Coast Metropolis, Ghana.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis quantitative cross-sectional study was conducted in three health facilities in the Cape Coast Metropolis. Systematic random sampling was used to select 160 pregnant women attending the three antenatal clinics. Data were collected via a structured questionnaire and analysed via IBM SPSS Statistics 27 and R 4.3.1. Sociodemographic data, anthropometric data, maternal characteristics, dietary patterns, and blood pressure data were collected and analysed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults and findings:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings revealed that 25 (15.6%), 51 (31.9%), 64 (40%) and 20 (12.5%) patients had no risk factor, one risk factor, two risk factors, and three risk factors, respectively. Approximately 84.4% of the participants had at least one risk factor. Higher SES was associated with increased CVD risk factors. Approximately 56.9% of the participants were either overweight or obese, 7.5% had high blood pressure, 51.9% had a poor dietary pattern, and 37.5% were physically inactive. Age (estimate = 0.695, p \u0026lt; 0.001) and marital status (estimate = 4.091, p = 0.010) had positive and significant influences on blood pressure. SES (estimate = -0.002, p = 0.957), employment (estimate = 0.737, p = 0.547), and educational level (estimate = 1.198, p = 0.1405) had no significant effects on BP. Age (estimate = 0.408, p \u0026lt; 0.001) and marital status (estimate = 4.318, p \u0026lt; 0.001) had substantial positive influences on body mass index (BMI). In contrast, SES (estimate = 0.037, p = 0.109), parity (estimate = 0.559, p = 0.174), and job status (estimate = -0.619, p = 0.370) had a lower, nonsignificant influence on BMI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study highlights the significant impacts of age and marital status on blood pressure and body mass index among pregnant women, whereas socioeconomic status had no meaningful influence on cardiovascular disease risk factors. Employment status demonstrated a notable negative association with dietary patterns, underscoring the complex interplay between sociodemographic factors and health outcomes in this population.\u003c/p\u003e","manuscriptTitle":"Socioeconomic Status and Cardiovascular Disease (CVD) Risk Factors among Pregnant Women in the Cape Coast Metropolitan Assembly – Central Region, Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 06:50:29","doi":"10.21203/rs.3.rs-7200664/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"309605659792606565982445281838030956583","date":"2026-05-20T06:57:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164994951045479563979611025184056573240","date":"2026-03-21T00:07:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-22T14:01:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333070363530814577170084450436366908486","date":"2025-09-14T19:30:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203624171720812688848984130824111301871","date":"2025-09-13T15:57:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-13T13:21:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-10T19:14:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-30T12:06:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-30T12:04:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2025-07-24T02:33:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5864f0c2-e26f-41d9-86f0-ba3855cbee89","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"309605659792606565982445281838030956583","date":"2026-05-20T06:57:31+00:00","index":79,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-23T06:50:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 06:50:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7200664","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7200664","identity":"rs-7200664","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}