Knowledge, Practice (KP) and Healthcare Satisfaction Among Pregnant Women Using Blood Glucose Monitors for Gestational Diabetes Mellitus (GDM) Management in Trinidad and Tobago: A Cross-Sectional Observational Study

Knowledge, Practice (KP) and Healthcare Satisfaction Among Pregnant Women Using Blood Glucose Monitors for Gestational Diabetes Mellitus (GDM) Management in Trinidad and Tobago: A Cross-Sectional Observational Study | 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 Knowledge, Practice (KP) and Healthcare Satisfaction Among Pregnant Women Using Blood Glucose Monitors for Gestational Diabetes Mellitus (GDM) Management in Trinidad and Tobago: A Cross-Sectional Observational Study Akini James, Adesh Sirjusingh, Zachary Ramsumair, Wendy Sam, Zaria Choo Quan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8399355/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Hyperglycaemia and consequently, GDM, is the predominant medical condition encountered during pregnancy in Trinidad and Tobago, leading to significant maternal and neonatal complications. Thus, there was a need to reduce mortality and morbidity through the early detection and management of GDM while empowering women to take long-term control of their health. Methods This cross-sectional study used two-stage stratified random sampling across all five Regional Health Authorities (RHAs). Data were collected via telephone surveys with 323 eligible women aged 18–40 who had received blood glucose monitors between September 2023 and September 2024. Composite scores for knowledge, practice, and satisfaction were derived from validated survey sections. Data were analysed using descriptive statistics, t-tests, ANOVA, Tukey’s post-hoc tests, and multiple linear regression models. Sampling weights were applied to adjust for proportional RHA representation. Results Most participants were from SWRHA (28.9%) and NCRHA (28.0%), with the majority aged 25–34 (49.1%). Mean scores were Knowledge = 6.9 (SD = 1.9, possible range 0–11), Practice = 11.5 (2.7, 0–16), and Satisfaction = 49.6 (5.1, 8–56). Older age ( \(\:\beta\:\) =0.156, p = 0.006), current pregnancy status ( \(\:\beta\:\) =0.114, p = 0.041), and higher perceived prior knowledge ( \(\:\beta\:\) =0.198, p < 0.001) were significantly associated with higher knowledge scores. Older age showed a significant association with diabetes management practices ( \(\:\beta\:\) =0.231, p < 0.001). Lower satisfaction scores were observed among women pregnant during the interview ( \(\:\beta\:\) =–0.124, p = 0.037) and those diagnosed later in pregnancy ( \(\:\beta\:\) = − 0.225, p < 0.001). All analyses were performed using IBM SPSS version 25. Conclusions Among women enrolled in the HSSP-DiP initiative higher knowledge and self-management scores were observed in older participants and those with prior awareness. BMI did not significantly influence knowledge, practices, or satisfaction, which suggests equitable reach across weight categories. However, currently pregnant women and those diagnosed later in pregnancy reported lower satisfaction with their care, indicating a need for more responsive support during late diagnosis and active pregnancy. These findings suggest age-sensitive and timing-specific considerations may be important for improving maternal experiences and outcomes. Gestational Diabetes Mellitus Pregnancy Self-management Knowledge Practices Satisfaction Blood Glucose Monitors Trinidad and Tobago Figures Figure 1 Figure 2 Background Introduction Hyperglycaemia occurs when there is too much glucose in the bloodstream due to insufficient insulin or an inability of the body to use insulin effectively for glucose uptake by cells ( 1 , 2 ). Insulin is the hormone responsible for regulating blood glucose levels ( 3 ). Diabetes, impairs the body’s ability to metabolize glucose, leading to periods of both hypoglycaemia (low blood sugar) and hyperglycaemia ( 2 ). Gestational Diabetes Mellitus (GDM) specifically refers to glucose intolerance of varying degrees first identified during pregnancy ( 4 ). Globally, one in six live births (16.8%) are affected by Diabetes in Pregnancy (DiP), 84% due to GDM and 16% to pre-existing diabetes, type 1 or type 2 diabetes, diagnosed during pregnancy ( 5 ). GDM correlates with higher incidences of maternal morbidities including caesarean delivery, shoulder dystocia, birth trauma, preeclampsia, postpartum diabetes, and hypertensive and cardiovascular disease later in life. Additionally, GDM is linked to perinatal and neonatal morbidities such as macrosomia, birth injury, hypoglycaemia, polycythaemia, congenital malformations, hyperbilirubinemia and respiratory distress syndrome ( 4 , 5 ). Notably, cases of DiP involving pre-existing diabetes may carry higher risks of complications compared to GDM alone. DiP has some distinct risk factors but shares several with GDM, including increased body weight, low physical activity pre-pregnancy, advanced maternal age (35 years or older at estimated delivery), a body mass index \(\:\ge\:\) 30 kg/m², and a family history of diabetes mellitus ( 6 ). A 2017 study among an Asian population identified associated risk factors, including a prior history of GDM, macrosomia, congenital anomalies, a body mass index (BMI) of \(\:\ge\:\) 25 kg/m², hypertension related to pregnancy, Polycystic Ovary Syndrome (PCOS), being aged 25 or older, having two or more previous pregnancies, and a history of abortion, stillbirth or preterm delivery ( 7 ). Evidence also suggests that other risk factors include infants with a birth weight of 4.5 kg or greater, particularly first-degree relative who has a family history of diabetes and links to specific ethnic backgrounds. ( 8 ). An article in UWI today by Fallon Lutchmansingh revealed approximately 20,000 births occur yearly in Trinidad and Tobago. About 1,000 of these are diagnosed pre-gestational diabetes, with gestational cases being estimated to be as much as three times this figure (9). Further research reinforced that between 1 in 5 to 1 in 6 pregnant women experience DiP, with over 80% of these cases attributed to GDM. Additionally, there is a 50% likelihood that affected mothers will develop diabetes later in life ( 10 ). A pilot screening initiative conducted in T&T utilized a standardized 75g oral glucose tolerance test (OGTT) following an overnight fast and identified a GDM prevalence of 14.1% among 658 pregnant women ( 11 ). This highlights the urgent need for enhanced management of DiP in the region. Effective management of DiP and GDM is essential, as outlined by the ‘Diabetes in Pregnancy: Management from Preconception to the Postnatal Period’ guidelines from the National Institute for Health and Care Excellence (NICE) ( 8 ). Guidelines emphasize knowledge, resources, and healthcare support to reduce complications and improve outcomes, highlighting women empowerment through education on blood glucose control and individualized dietary and weight management. For women with GDM, understanding its implications is critical. Education on blood glucose control through diet, exercise, and medication is essential as poor management negatively impacts maternal and foetal health. Self-monitoring training supports greater autonomy in GDM management, and guidelines promote informed decision-making on risk assessments and testing, encouraging active participation in health management to prevent complications affecting both mother and child ( 8 ). Relevance to Public Health In response to the high rates of DiP in Trinidad and Tobago (T&T), a screening and treatment initiative for DiP was introduced in 2015 by the Helen Bhagwansingh Diabetes Education, Research, and Prevention Institute (DERPi) at the University of the West Indies (UWI), St. Augustine, Trinidad, to improve health outcomes for pregnant women through targeted interventions. This initiative was expanded in 2020 through a collaboration between the Ministry of Health (MoH) and the Inter-American Development Bank (IDB), providing blood glucose monitors to healthcare facilities and pregnant women, training healthcare providers in DiP management, and promoting public awareness through educational campaigns ( 11 – 13 ). This continued support provided, aimed to improve health outcomes and reduce complications associated with DiP. Rationale Enhancing women's knowledge about DiP, hyperglycaemia management, and access to quality healthcare has been shown to improve DiP outcomes( 14 , 15 ). This research aims to evaluate the ongoing efforts to reduce DiP rates in T&T. It is expected that improved knowledge, management practices, and higher patient satisfaction will lead to earlier detection and better overall health outcomes by reducing undiagnosed cases and improving maternal, perinatal, and neonatal wellbeing. Literature review A study conducted in Ethiopia by Dissassa et al. highlighted that insufficient knowledge regarding the risks, screening methods, and management among pregnant women about GDM correlated with poorer maternal and neonatal health outcomes ( 16 ). Similarly, Siuluta et al. at Kinango District Hospital, Coastal Kenya assessed the knowledge, attitudes and practices of GDM among pregnant women showing that among the 354 participants, only 29.0% were knowledgeable but 46.98% were willing to learn more and apply better practices that could prevent or manage GDM effectively ( 17 ). The study also shows that 60.17% of participants had good practice in GDM management, however, the majority did not consistently monitor their blood glucose levels nor attend regular antenatal check-ups ( 17 ). Furthermore, the analysis from 84 observational studies conducted in Asia showed that the overall prevalence of GDM was 11.5% (95% CI 10.9–12.1), highlighting the burden of GDM in the area and indicating a serious public health concern( 7 ). In 2022, T&T’s health sector noticed a similar trend of hesitance and non-recording with respect to screening for DiP ( 18 ). In 2023, provisional data collected among three Regional Health Authorities (RHAs), showed that diabetes mellitus had a prevalence of 11.1% ( 10 ). There were gaps in the screening process and data recording which suggest that the true prevalence of DiP is likely underestimated and more widespread in T&T's broader population. The effects of unmanaged DiP must be reinforced and continuous interventions to improve the knowledge, practice and management within the wider population should be viewed as a benefit in addressing the growing concern and reducing associated risks. Furthermore, the discrepancies imply that local health policies pertaining to screening and management must be modified to consider regional circumstances. The study advocates for targeted interventions aimed at high-risk groups, particularly women with previous GDM or those presenting other risk factors like obesity or advanced maternal age. Early identification and management can mitigate adverse outcomes for mothers and their children. Moreover, to lower the prevalence in at-risk groups, educational public health initiatives prioritizing lifestyle changes are crucial. In conclusion the meta-analysis and systematic review by Lee et al. shed important light on the risk factors and prevalence of GDM in Asia, highlighting the urgent need for improved screening procedures and focused therapies for high-risk women. Healthcare systems must prioritize GDM through specialized public health initiatives meant to lower its prevalence and enhance maternal-child health outcomes throughout the region given that it presents serious short- and long-term health hazards. Methods Aim and Objectives Aim The study aimed to analyse the knowledge and practices of pregnant women accessing public healthcare who received blood glucose monitors for managing Diabetes in Pregnancy (DiP) and to assess their satisfaction with the implemented Health Service Support Programme. Objectives Evaluate the level of knowledge about GDM management among pregnant women who received blood glucose monitors through the HSSP-DiP Project. Investigate the practices of pregnant women in managing their blood glucose levels, including the use of blood glucose monitors, diet, and exercise. Assess the satisfaction of participants with the healthcare services and resources provided under the HSSP-DiP Project, including the support received in managing their condition. Identify key demographic, medical, and lifestyle risk factors associated with poor knowledge, ineffective management, and low satisfaction among participants. Study Design This was a cross-sectional observational study design. Setting of the Study and Characteristics of Participants The study population comprised pregnant women aged 18–40 with DiP accessing public healthcare and having received blood glucose monitors from the HSSP-DiP Project across T&T between September 2023 and September 2024. Sample Size Calculation and Sampling Methodology A stratified random sampling method was employed, with population stratified by RHAs: Eastern Regional Health Authority (ERHA), North Central Regional Health Authority (NCRHA), North-West Regional Health Authority (NWRHA), Southwest Regional Health Authority (SWRHA), and Tobago Regional Health Authority (TRHA). A simple random sample was conducted from each RHA, with proportional allocation based on the size of each stratum to ensure accurate representation of the national population. Based on a total population size of 1,190 pregnant women receiving care through the public health facilities. The sample size was calculated assuming a 50% estimated prevalence, 95% confidence interval at a 5% margin of error and an expected 85% response rate. A finite population correction was applied, resulting in a final adjusted sample size of the sample of 342. Inclusion and Exclusion Criteria Inclusion Criteria Women aged 18–40 with DiP (either Gestational Diabetes Mellitus or Diabetes Mellitus in Pregnancy) enrolled in HSSP-DiP from September 2023 to September 2024. Pregnant women who received a blood glucose monitoring device from the HSSP-DiP. Exclusion Criteria Non-English Speakers (Language Barriers) Excludes participants with serious health conditions not commonly associated with DiP such as Cardiovascular Complications, Autoimmune Diseases, CKD, Severe Respiratory Illnesses, Infectious Diseases, Oncological Conditions, Mental Health Conditions, Blood, Liver and Neurological Disorders. Cognitive impairment. No contact information available. Data Collection Participants were recruited from the HSSP-DiP project participant register using random selection methods. Selected individuals were contacted via telephone by trained interviewers. Each potential participant underwent eligibility screening, and their verbal consent was recorded prior to survey administration. A combination of pre-validated survey instruments was used and adapted for this study. Internal reliability was assessed for the modified scales using Cronbach’s alpha. ( 34 , 35 , 36 ). The study was thoroughly explained to eligible participants and time was given for consideration before they provided final consent. The process remained non-coercive and emphasized informed consent and voluntary participation. Data was collected using password-protected tablets and entered directly into a secure Google Form. The survey lasted approximately 20 to 30 minutes and consisted of the following four sections. Section A: Demographics and Pregnancy History – 16 questions (3–5 minutes) Section B: Knowledge – 16 questions (10–12 minutes) Section C: Practice – 10 questions (5–8 minutes) Section D: Patient Satisfaction – 9 questions (3–5 minutes) Data Analysis The dataset was cleaned, and missing values were addressed using the pairwise deletion method. Participants’ responses were coded accordingly. A descriptive analysis was performed on the patient characteristics section of the survey using frequencies and percentages. Cronbach’s alpha statistics were calculated for the Knowledge, Practice and Healthcare Satisfaction (KPS) sections of the survey to assess internal consistency. Items with low reliability (Cronbach’s alpha < 0.7) were removed from the analysis. Composite scores for Knowledge, Practice, and Satisfaction were computed by summing the corresponding item scores for each participant. Outliers in the KPS scores were identified using boxplots and standardized Z-scores. Values with Z-scores less than − 2 or greater than + 2 were considered potential outliers and were excluded from all subsequent inferential analyses to minimize bias. Normality of continuous variables was assessed visually using Q-Q plots and statistically using skewness and kurtosis. Descriptive analyses were also conducted on the KPS scores. To ensure more representative estimates across RHAs, sampling weights were applied prior to inferential analyses. To explore group differences in average KPS scores by socio-demographic variables, independent sample t-tests were used for binary variables, and one-way ANOVA for categorical variables with three or more groups. Where significant differences were found via ANOVA, Tukey’s HSD post-hoc tests were applied to identify specific group-level differences. Following the initial comparison of means, Multilinear Regression (MLR) analyses were conducted to identify independent predictors of KPS scores among women with DiP accessing the programme. Variables that were statistically significant or showed borderline significance (p < 0.10) in the univariate analyses were entered into the regression models. A backward selection method was applied to arrive at the most parsimonious models. All key assumptions of linear regression were evaluated and satisfied. Residuals for each model were approximately normally distributed (standardized residuals within ± 3), with no evidence of heteroscedasticity or multicollinearity (all variance inflation factors [VIFs] 0.97). Lastly, Pearson correlation analyses were conducted to explore significant relationships between KPS scores. All statistical analyses were performed using SPSS version 27, with significance set at p < 0.05. Results Descriptive Analysis A total of 322 participants from five RHAs were included in the analysis. From Table 1 , the largest proportion being from SWRHA (28.9%) and NCRHA (28.0%), followed by NWRHA (22.0%), ERHA (16.5%), and TRHA (4.7%). Ethnic distribution showed a relatively even spread, with participants identifying as Mixed (34.2%), African (33.2%), East Indian (31.7%) and Other (0.9%). Most respondents were aged 25–34 years (49.1%), 37.6% aged 35 and older, and 13.4% under 25. Regarding BMI, the majority of the women were obese and overweight (36.1% and 33.1% respectively) while 3.7% underweight. Most participants (98.7%) were not pregnant at the time of the survey. Table 1 Socio-demographic and clinical characteristics of study participants Variable Missing n (%) Frequency (n) Percent (%) RHA SWRHA 0 (0.0) 93 28.9 NCRHA 90 28 NWRHA 71 22 ERHA 53 16.5 TRHA 15 4.7 Ethnicity Mixed 0 (0.0) 110 34.2 African 107 33.2 East Indian 102 31.7 Other 3 0.9 Age 25–34 0 (0.0) 158 49.1 35+ 121 37.6 < 25 43 13.4 BMI Obese 17 (5.3) 110 36.1 Overweight 101 33.1 Normal 82 26.9 Underweight 12 3.9 Currently Pregnant No 8 (2.5) 310 98.7 Yes 4 1.3 Number of Pregnancies 1 2 (0.6) 125 39.1 2 87 27.2 ≥ 3 108 33.7 Number of Live Births 0 2 (0.6) 1.6 1–2 228 71.3 >=3 87 27.2 Age of Youngest Child (months) 36 months 4 1.3 Number of Weeks Pregnant Third trimester ( 28 – 36 ) 318 (98.8) 4 100 First, Second, Full-term 0 0 When Diagnosed with GDM (weeks) Early-onset GDM ( = 29) 42 13.9 Previously Diagnosed with Diabetes No 2 (0.6) 282 88.1 Yes 38 11.9 Type of Previous Diabetes Diagnosis Type 1 284 (88.2) 7 18.4 Type 2 24 63.2 I don't know 7 18.4 Previous Knowledge Perception A little 0 (0.0) 164 50.9 Quite a lot 67 20.8 A lot 56 17.4 Nothing 35 10.9 GDM in a Previous Pregnancy No 53 (16.5) 217 80.7 Yes 49 18.2 I don’t know 3 1.1 This table summarizes the frequency distributions of socio-demographic and clinical characteristics of study participants. For each variable, the table reports the number of missing cases and the percentage missing (in parentheses), followed by the observed frequency and corresponding percentage for each category. For reproductive history, only four women (1.3%), all being in their third trimester, were pregnant at the time of the survey where 39.1% were primipara and 33.7% previously experienced more than two pregnancies. Five women who had previously been pregnant, excluding those were currently pregnant at the time of the survey, reported no live births. 17.2% reported that their youngest child’s age was above one year old. Most women had neither a prior diabetes diagnosis (88.1%) nor a history of GDM (80.7%) before their last pregnancy. In that pregnancy, early-onset GDM (< 24 weeks) was most common (62.6%), while late-onset GDM ( \(\:\ge\:\) 29 weeks) was least prevalent (13.9%). Among women previously diagnosed with diabetes, 63.2% had Type 2 diabetes, and nearly half (50%) reported limited perceived knowledge of diabetes in pregnancy. [Insert Table 1 here] Reliability Analysis The internal consistency of the three scales was assessed using Cronbach’s alpha. For the Knowledge scale, 11 out of 16 items were retained, yielding a Cronbach’s alpha of 0.7, indicating acceptable reliability. The Practice scale retained all 10 items, also achieving Cronbach’s alpha of 0.7. For the Satisfaction scale, 8 of the original 9 items were retained, resulting in Cronbach’s alpha of 0.8, suggesting good internal consistency. These results indicate that all three scales demonstrated adequate reliability for further analysis. Outliers Detection and Mitigation Figure 1 below presents boxplots comparing the distribution of KPS scores before (a) and after (b) the removal of outliers. Figure 1 a shows that a few mild outliers are observed below the lower whisker, indicating some participants scored noticeably lower than the majority. After removal, as shown in Fig. 1 b, the distribution becomes more symmetric, with none to moderate visible outliers. The interquartile range remains consistent, although Fig. 1 b reflects a slightly more compact distribution. The satisfaction variable displays the highest number of outliers in before outlier removal, with several participants scoring far below the main cluster. After outlier removal, the distribution becomes more symmetrical and representative of the central tendency, with a marked reduction in variability. The removal of outliers resulted in clearer, more normally distributed data especially for the Satisfaction Score, where extreme low values were influencing the spread and skewness. The figure presents boxplots of knowledge, practice and satisfaction scores: (a) before outlier removal and (b) after outlier removal. Descriptive Analysis of Knowledge, Practice and Satisfaction Scores From Table 2 , the mean Knowledge Score was 6.9 (SD = 1.9), with scores ranging from 3 to 11. The Practice Score had a mean of 11.5 (SD = 2.7), ranging from 6 to 16, while the Satisfaction Score ranged from 36 to 56, with a mean of 49.6 (SD = 5.1). Assessment of normality was first done using Q-Q plots and was further assessed using skewness and kurtosis values as shown in Table 2 . The results indicated that all three variables were approximately normally distributed where skewness values ranged from − 0.6 to 0.0, and kurtosis values from − 1.0 to -0.3, all within the acceptable range of \(\:\pm\:2\) . These results support the use of parametric statistical tests in subsequent analyses. Table 2 Descriptive statistics for knowledge, practice and satisfaction scores among study participants Variable Minimum Maximum Mean Standard Deviation Skewness Kurtosis Knowledge Score 3 11 6.9 1.9 -0.2 -0.8 Practice Score 6 16 11.5 2.7 0.0 -1.0 Satisfaction Score 36 56 49.6 5.1 -0.6 -0.3 This table summarizes the minimum and maximum values, means, standard deviations, and measures of distribution (skewness and kurtosis) for knowledge, practice, and satisfaction scores among study participants after the removal of outliers. The skewness and kurtosis values demonstrated that the scores are approximately normal since they are within the acceptable range ( \(\:\pm\:2\) ), supporting the use of parametric statistical analyses. Inferential Analysis Table 3 shows the means and standard deviation of Knowledge, Practice, and Satisfaction scores across participant characteristics. Significant differences were found across age groups for all three age categories. Participants aged 35 + had significantly higher Knowledge scores than those under 25 (p = 0.005). Likewise, older participants (35+) reported significantly better Practice (Mean Diff. = 1.93, p < 0.001) and Satisfaction scores compared to those under 25 (p = 0.102, not significant, but trend noted). The 25–34 age group also reported higher Satisfaction than those < 25 (Mean Diff. = 2.72, p = 0.005). Table 3 Comparison of mean knowledge, practice, and satisfaction scores across sociodemographic and clinical characteristics of study participants Variable Knowledge Practice Satisfaction Mean SD p-value Mean SD p-value Mean SD p-value RHA SWRHA 6.7 2.1 0.134 11.9 2.6 0.389 50.0 4.8 0.254 NCRHA 6.9 1.8 11.6 2.5 49.8 5.0 NWRHA 7.3 2.0 11.3 2.9 48.3 5.6 ERHA 6.8 1.7 11.4 2.7 50.2 5.1 TRHA 7.9 1.7 10.6 2.2 49.2 4.5 Ethnicity Mixed 6.9 2.0 0.056 11.5 2.7 0.774 49.9 5.0 0.509 African 7.3 1.8 11.4 2.7 49.5 5.6 East Indian 6.6 2.0 11.8 2.6 49.4 4.6 Other 7.7 1.5 11.6 3.7 53.6 4.5 Age < 25 6.1 2.2 10.2 2.4 47.6 5.0 0.007* 25–34 6.9 1.9 0.006* 11.5 2.8 < 0.001 ᵻ * 50.3 4.9 35+ 7.2 1.9 12.1 2.4 49.5 5.0 BMI Underweight 7.0 2.0 0.925 11.3 2.7 0.894 50.5 2.7 0.422 Normal 6.8 2.0 11.5 2.6 50.0 5.2 Overweight 7.0 2.0 11.7 2.6 49.0 5.0 Obese 7.0 1.9 11.6 2.8 50.1 5.1 Currently Pregnant No 6.9 2.0 0.074 11.6 2.7 0.958 49.8 5.0 0.024* Yes 8.6 1.7 11.7 3.3 44.3 5.0 Number of Pregnancies 1–2 6.9 2.0 0.595 11.4 2.7 0.078 49.4 5.4 0.231 ≥ 3 7.0 1.8 11.9 2.5 50.1 4.3 Number of Live Births 0 7.2 0.8 0.961 ᵻ 10.2 1.8 0.133 48.2 4.2 0.382 1–2 6.9 2.0 11.4 2.7 49.4 5.3 ≥ 3 7.0 1.9 12.0 2.6 50.2 4.5 Age of Youngest Child (months) 36 months 7.8 1.5 11.5 3.6 42.6 4.4 Number of Weeks Pregnant (Currently pregnant women at time of interview) Third trimester ( 28 – 36 ) 8.6 1.7 N/A 11.7 3.3 N/A 44.3 5.0 N/A When Diagnosed with GDM (weeks) 28 6.8 2.3 11.5 2.9 48.4 5.2 Previously Diagnosed with Diabetes No 6.9 2.0 0.839 11.6 2.7 0.394 49.5 5.1 0.260 Yes 7.0 1.9 11.2 2.5 50.5 4.7 Type of Previous Diabetes Diagnosis Type 1 6.8 1.8 0.813 12.2 3.2 0.393 51.2 3.6 0.636 Type 2 7.1 2.0 10.8 2.1 50.0 5.3 I don't know 6.6 1.9 11.7 3.1 51.8 3.7 Previous Knowledge Perception about Diabetes Nothing 6.0 2.1 < 0.001* 11.9 2.8 0.095 49.5 5.9 0.079 A little 6.7 2.0 11.2 2.6 49.0 5.0 Quite a lot 7.6 1.7 11.7 2.6 50.6 4.6 A lot 7.3 1.9 12.2 2.7 50.6 5.0 GDM in a Previous Pregnancy No 6.8 2.0 0.422 11.6 2.7 0.651 49.6 5.2 0.458 Yes 7.2 1.9 11.6 2.4 50.0 4.6 I don't know 6.7 0.6 10.2 3.1 46.3 7.7 ᵻ Kruskal Wallis Test *Significant at the 5% level of significance Comparison of mean knowledge, practice, and satisfaction scores across sociodemographic and clinical characteristics. Means (SD) are reported. p-values were calculated using independent sample T-test or ANOVA where appropriate unless indicated by ᵻ (Kruskal Wallis test). * indicates statistical significance at the 5% level (p < 0.05). Participants who youngest child is over 36 months had significantly lower Satisfaction scores compared to those with younger children. Specifically, they scored significantly lower than the 12–36 months group (Mean Diff. = − 7.60, p = 0.010) and the < 12 months group (Mean Diff. = − 7.05, p = 0.015). Participants diagnosed with GDM before 24 weeks gestation had significantly higher Satisfaction scores than those diagnosed between 24–28 weeks (Mean Diff. = 2.02, p = 0.015). No other pairwise differences were statistically significant. There were significant differences in Knowledge scores by perceived prior knowledge. Those who reported knowing "quite a lot" scored significantly higher than those knowing "nothing" (Mean Diff. = 1.59, p = 0.001) or "a little" (Mean Diff. = 0.95, p = 0.005). Similarly, those reported knowing "a lot" also scored higher than those with no knowledge (Mean Diff. = 1.34, p = 0.012). While not all comparisons were statistically significant, currently pregnant participants tended to report higher Knowledge scores (M = 8.6) but lower Satisfaction (M = 44.3) than those not pregnant (p = 0.074 and p = 0.024 respectively). [Insert Table 3 here] Multiple Linear Regression Model All regression models shown in Fig. 2 met the assumptions for normality, linearity, homoscedasticity, and the absence of multicollinearity, Table 4 . The Knowledge Score model was statistically significant (F(3, 299) = 8.923, p < 0.001), explaining 7.3% of the variance. Significant predictors included older age ( \(\:\beta\:\) = 0.156, p = 0.006), current pregnancy status ( \(\:\beta\:\) = 0.114, p = 0.041), and higher perceived prior knowledge about DiP ( \(\:\beta\:\) = 0.198, p < 0.001). This suggests that these factors contribute modestly to better patient knowledge. The Practice Score model was also significant (F(1, 307) = 17.262, p < 0.001), accounting for 5.0% of the variance. Age was the sole significant predictor ( \(\:\beta\:\) = 0.231, p < 0.001), indicating that older women engaged in stronger diabetes management practices. Other variables were excluded during model refinement due to lack of significance. The Satisfaction Score model explained 5.6% of variance (F(2, 267) = 8.928, p < 0.001). Women currently pregnant reported lower satisfaction with their care ( \(\:\beta\:\) = − 0.124, p = 0.037), and those diagnosed with gestational diabetes later in pregnancy had lower satisfaction scores ( \(\:\beta\:\) = − 0.225, p < 0.001). These findings highlight potential gaps in patient experience related to timing of diagnosis and pregnancy status. This figure presents the standardized regression coefficients \(\:\left(\beta\:\right)\) for significant predictors of knowledge, practice, and satisfaction scores among women with diabetes in pregnancy. Only predictors that remained statistically significant in the final models are shown. All models met assumptions of normality, linearity, homoscedasticity, and absence of multicollinearity. Table 4 Summary of multiple linear regression models predicting knowledge, practice, and satisfaction (KPS) scores among women with diabetes in pregnancy Predictor Knowledge Practice Satisfaction Coefficient \(\:\left(\varvec{\beta\:}\right)\) p-value Coefficient \(\:\:\left(\varvec{\beta\:}\right)\) p-value Coefficient \(\:\left(\varvec{\beta\:}\right)\) p-value Age 0.156 0.006 0.231 < 0.001 NS (excluded) Currently Pregnant 0.114 0.041 NS (excluded) –0.124, 0.037 Perceived Knowledge About Diabetes 0.198 < 0.001 NS (excluded) NS (excluded) GDM Diagnosis Timing (weeks) NS (excluded) NS (excluded) –0.225 NS – Not significant The table summarizes the regression coefficients \(\:\left(\beta\:\right)\:\) and p-values for predictors included in the multiple linear regression models of knowledge, practice, and satisfaction scores. Adjusted \(\:R²\) values indicate the proportion of variance explained by each model. NS indicates predictors that were excluded from the final model due to lack of statistical significance (p > 0.05). Correlation Analysis Table 5 shows that there exists a significantly weak positive linear correlation between women’s knowledge of diabetes in pregnancy and their management practice scores (r = 0.119, p = 0.037). Similarly, a significantly weak positive correlation exists between their management practice and healthcare satisfaction scores (r = 0.168, p = 0.003). On the other hand, the data does not suggest any significant linear correlation between their knowledge and healthcare satisfaction scores. Table 5 Pearson correlation analysis of knowledge, practice and satisfaction (KPS) scores of women with diabetes in pregnancy Knowledge Score Practice Score Satisfaction Score Knowledge Score - Practice Score 0.119* - (0.037) Satisfaction Score 0.036 0.168** - (0.531) (0.003) *Significant at the 5% level of significance **Significant at the 1% level of significance This table presents Pearson correlation coefficients (r) among knowledge, practice, and satisfaction scores for women with diabetes in pregnancy. A single asterisk (*) indicates significance at the 5% level (p < 0.05), and a double asterisk (**) indicates significance at the 1% level (p < 0.01). Weak positive correlations were observed between knowledge and practice scores (r = 0.119, p = 0.037) and between practice and satisfaction scores (r = 0.168, p = 0.003). No significant correlation was observed between knowledge and satisfaction scores. Discussion Obesity increases insulin resistance, which can predispose some women to a diabetic state. Although elevated BMI is a well-established risk factor for gestational diabetes mellitus (GDM), this study found no significant association between BMI categories and women's knowledge of diabetes in pregnancy (DiP), their management practices during pregnancy, or their satisfaction with the HSSP-DiP program ( 19 ). Evidence from the United Kingdom indicates that for every 1 kg/m² increase in body mass index (BMI), the prevalence of gestational diabetes mellitus (GDM) increases by approximately 0.92% ( 20 ). Despite more than half of the women in this study from Trinidad and Tobago being classified as overweight or obese, BMI did not significantly influence any of the key outcomes: knowledge of DiP, management practices, or healthcare satisfaction. This suggests that the HSSP-DiP intervention was equitably effective across BMI groups and capable of reaching a diverse population. These findings reinforce the Ministry of Health's commitment to delivering inclusive and equitable maternal health services ( 21 ). Nevertheless, the high prevalence of GDM observed in this population warrants attention. There is a clear need for targeted preconception weight management strategies and healthy lifestyle interventions among women in Trinidad and Tobago planning to conceive. Such efforts may contribute to reducing the burden of GDM and, by extension, improving both maternal and fetal health outcomes. Additionally, a large proportion of women, excluding primiparas, reported no prior history of diabetes or GDM before their most recent pregnancy. This raises concerns about why these women are now developing GDM and whether the prevalence is increasing over time. Furthermore, the 11.9% prevalence of pre-existing diabetes observed in this study is markedly higher than the global estimate of 1–2%, suggesting either a population with elevated baseline risk factors or that global benchmarks may not fully reflect the local and cultural context in T&T ( 24 , 25 ). The International Diabetes Federation estimates adult diabetes prevalence in T&T to be approximately 12%, and given the increasing prevalence of obesity and other susceptibility factors in the population, the rate of GDM may be rising over time ( 22 , 23 , 37 ). Typically according to the global norms, the WHO recommends universal screening for GDM is between 24–28 weeks of gestation and only earlier screening for high-risk individuals ( 24 ). However, it was recommended by the MoH, T&T that screening of women in the country should not be based on risk factors since 50% of cases can be missed and screening should be considered within the first trimester when booking blood tests are being done ( 21 ). Consequently, more than half the women in this study had early-onset GDM (less than 24 weeks) and the least proportion of mothers were diagnosed within the third trimester. The promotion for early screening among women in the country is evident thus highlighting the positive impact made by the Ministry and HSSP-DiP initiatives to apply a more aggressive early screening practice in the primary care settings regardless of gestation ( 21 ). Additionally, a substantial proportion of women diagnosed early with GDM had no prior history of DiP or GDM. Although early screening appears effective in detecting GDM at an earlier stage, an outcome with several clinical benefits, this finding also raises concerns. It suggests a possible increase in underlying risk factors among the antenatal population, given that these women are now presenting with GDM in the first trimester despite no previous diagnosis. This trend may reflect not only the success of earlier screening initiatives but also a shifting risk profile among pregnant women nationally. From the moment a diagnosis of GDM is made, women’s healthcare experiences are largely shaped by their interactions with providers, which can vary depending on the model of care delivered. This study found that women diagnosed later in pregnancy tended to report lower satisfaction with their healthcare. It is possible that those diagnosed later did not fully experience or adequately assess the entire HSSP-DiP program, which may have influenced their satisfaction levels. Previous research has demonstrated that women’s satisfaction often declines immediately following diagnosis, largely due to emotional distress, psychological shock, and the sudden adjustment to managing a complex condition ( 25 ). The range of emotions experienced can be traumatic for the expectant mother, as most pregnant women anticipate a healthy fetus and do not perceive themselves to be at risk ( 25 ). A late adverse diagnosis can therefore be overwhelming, and with limited time for management and adjustment, it may negatively affect the overall care experience. While the findings of this study highlight the program’s effectiveness, where women engaged in care for a longer period reported greater satisfaction, targeted strategies may be necessary to improve healthcare satisfaction among women diagnosed in the third trimester, as they tend to be more sensitive to treatment during this late stage of pregnancy. Older women in the sample were found to have higher levels of knowledge regarding DiP and GDM. Specifically, women of advanced maternal age (35 years and older) scored significantly higher on knowledge assessments compared to mothers under 25 years. This finding is supported by other studies, which suggest that older women may have higher parity and are more likely to have received information about GDM during antenatal care visits in previous pregnancies ( 26 , 27 ). However, this is not a consistent finding across all settings. For example, some studies report no significant differences in knowledge scores between women above and below the advanced maternal age threshold ( 28 ). Dissassa et al. even found that women under 24 years had higher odds of possessing sufficient knowledge about GDM, possibly due to higher education levels in that age group, which may enhance understanding ( 16 ). These mixed findings suggest the need for further investigation into why older women in this context appear more knowledgeable, and to inform the development of targeted interventions aimed at improving DiP and GDM knowledge among younger women. Additionally, although limited empirical evidence directly links increased maternal age to better gestational diabetes management, the findings of this study suggest that these women may engage in more effective self-management practices during pregnancy. However, previous research provides indirect evidence suggesting that these women are more likely to be multiparous and may benefit from prior pregnancy experience resulting in better self-management and glycaemia control. Additionally, healthcare providers may exert greater effort to ensure appropriate management practices, given the higher baseline risk associated with advanced maternal age ( 29 ). Therefore, although some reports have shown younger women having a higher knowledge level regarding GDM, advanced maternal age may benefit from practical experience and structured health behaviors established over time. Other studies would have shown that older adults were more compliant with dietary and self-monitoring routines ( 30 ). With advancing maternal age, these women may have had more exposure to health messaging or previous pregnancies, enhancing their awareness and ability to manage their condition ( 17 ). One research found that pregnant women aged \(\:\ge\:\) 35 years were over twice as likely to be aware of GDM compared to younger women aged 20–24 years suggesting that they may have greater health exposure from previous pregnancies or better understanding due to increased maturity and health-seeking behavior ( 26 ). Furthermore, it was acknowledged that parity alone did not account for this increased awareness, meaning age itself may play a direct role in shaping attitudes and behavior around GDM ( 26 ). Together, these factors could account for the observed differences in management practices. Hence, recommendations include tailoring diabetes education and support materials specifically for younger women. These could include simplified messages, visual aids, and engagement through mobile and social media platforms. Women who reported prior knowledge of diabetes in pregnancy (DiP) and gestational diabetes mellitus (GDM) scored significantly higher on the knowledge assessment in this study. Women who believed they knew “quite a lot” or “a lot” about DiP scored significantly higher on knowledge assessments than those who felt they knew “nothing” or “a little.” This suggests that self-perception closely reflects actual knowledge. This suggests that increased awareness, potentially gained through formal education or public health messaging, may positively influence younger women's understanding of pregnancy-related conditions in Trinidad and Tobago. Greater knowledge may contribute to improved pregnancy experiences by promoting earlier recognition of risk factors and facilitating healthier lifestyle choices from a younger age, thereby helping to reduce the risk of GDM and its associated complications. Materials should be designed to challenge assumptions and promote active learning, particularly for those who feel they already “know enough”. Several countries, including Finland and Singapore, have adopted similar strategies by incorporating GDM awareness into preconception and maternal health initiatives targeting young women. These efforts aim to reduce the burden of diabetes and other chronic conditions through early education and prevention ( 31 , 32 ). Lastly, women who were currently pregnant at the time of the interview, though comprising only 1.3% of the sample, demonstrated significantly higher knowledge regarding pregnancy-related conditions but reported lower levels of healthcare satisfaction. This may reflect their heightened awareness of GDM during the ongoing pregnancy, while simultaneously being less able to evaluate the overall quality of the HSSP-DiP intervention. Notably, previous research has shown that the timing of measurement can influence women's reported experiences of antenatal care, highlighting that perceptions may differ depending on whether assessments occur before or after childbirth ( 33 ). However, this finding should be interpreted with caution, as only four currently pregnant women participated in the study, limiting the generalizability and statistical power of conclusions drawn from this subgroup. Limitations Despite the strengths of the cross-sectional design and the use of nationwide sampling, several limitations affected the interpretation of the findings. The study relied on self-reported data, which may introduce recall bias. Furthermore, the absence of clinical outcome measures (such as birth weight or pregnancy complications) limits the ability to directly correlate maternal knowledge and practices with specific health outcomes. Logistic and operational challenges significantly impacted participant recruitment and data collection. New mothers were often occupied with childcare responsibilities or work, making follow-up calls challenging. While several women initially agreed to participate, many became unreachable afterward. Incorrect or out-of-service contact numbers further hindered efforts and contributed to a smaller-than-anticipated sample size. Survival bias posed significant challenges in data collection as some mothers had miscarriages or neonatal deaths and opted to discontinue participation. This would impact the interpretation of data, introducing bias to the rate ratio estimate and the evaluation of prevalence rate. Conclusions This study provides important insights into the knowledge, management, and healthcare satisfaction related to diabetes in pregnancy (DiP) and gestational diabetes mellitus (GDM) among women in Trinidad and Tobago. Despite the high prevalence of obesity and GDM, BMI was not a significant factor in influencing women’s knowledge, practices, or satisfaction, indicating the equitable reach of the HSSP-DiP intervention across demographic groups. However, the notable prevalence of early-onset and pre-existing diabetes highlights a shifting risk profile that calls for continued surveillance and preconception public health strategies. Age, parity, and prior experience appear to influence knowledge and self-management, with older women generally demonstrating greater awareness and potentially more effective management behaviors. Conversely, younger and first-time mothers may require tailored educational efforts to build competence and confidence in managing GDM. Additionally, timing of diagnosis and pregnancy status were found to affect healthcare satisfaction, underscoring the need for more responsive care models, particularly for women diagnosed later in pregnancy. Together, these findings reinforce the importance of culturally relevant, age-sensitive, and timing-specific interventions to reduce the burden of GDM and enhance maternal and fetal outcomes across Trinidad and Tobago. Abbreviations BMI Body Mass Index CI Confidence Interval CKD Chronic Kidney Disease DERPi Helen Bhagwansingh Diabetes Education, Research, and Prevention Institute DiP Diabetes in Pregnancy ERHA Eastern Regional Health Authority FMS Faculty of Medical Sciences GDM Gestational Diabetes Mellitus HSSP Health Service Support Programme HSSP DiP –Health Services Support Programme’s Diabetes in Pregnancy IDB International Development Bank KPS Knowledge, Practice and Satisfaction MLR Multiple Linear Regression MoH Ministry of Health NCRHA North Central Regional Health Authority NICE National Institute for Health and Care Excellence NWRHA North West Regional Health Authority OGTT Oral Glucose Tolerance Test p p–value PIU Project Implementation Unit PCOS Polycystic Ovary Syndrome RHAs Regional Health Authorities SPSS Statistical Package for the Social Sciences SWRHA South West Regional Health Authority TRHA Tobago Regional Health Authority T&T Trinidad and Tobago UWI The University of the West Indies WHO World Health Organization Declarations Ethics approval and consent to participate Ethics approval was obtained from the University of the West Indies, St. Augustine Campus Research Ethics Committee (Reference: CREC-SA.2991/11/2024 ), the Ministry of Health Ethics Committee (Reference: He: 3/13/1441 Vol. II ), and the Tobago House of Assembly Research Ethics Committee (Reference: THAREC: 005/01/2025 . Informed verbal consent was obtained from all study participants prior to data collection. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ contributions AJ, AS, RF, and : Conceptualization; supervision; project administration. AJ: Visualization; methodology; formal analysis; data curation; writing – original draft; writing – review & editing. AS and RF: Writing – original draft; writing – review & editing. ZR, WS, ZCQ, ZG, ZA, AF, and ZP: Investigation; formal analysis; data curation; writing – original draft; writing – review & editing. All authors read and approved the final manuscript. 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[cited 2025 Dec 20]. Available from: https://idf.org/our-network/regions-and-members/north-america-and-caribbean/members/trinidad-and-tobago/ Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 23 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers agreed at journal 07 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers invited by journal 05 Feb, 2026 Editor invited by journal 05 Jan, 2026 Editor assigned by journal 21 Dec, 2025 Submission checks completed at journal 21 Dec, 2025 First submitted to journal 18 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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before and after outlier removal\u003c/p\u003e\n\u003cp\u003eThe figure presents boxplots of knowledge, practice and satisfaction scores: (a) before outlier removal and (b) after outlier removal.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8399355/v1/afde0def92b57df2192e9485.png"},{"id":102244890,"identity":"aa9a1557-a3ba-46c2-bd6c-3102e144a4c0","added_by":"auto","created_at":"2026-02-09 17:41:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":135629,"visible":true,"origin":"","legend":"\u003cp\u003ePredictors of knowledge, practice, and satisfaction (KPS) scores from multiple linear regression models\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8399355/v1/34cec33bc7832880ef41af50.png"},{"id":102244943,"identity":"7ca2fc49-fa99-4dab-a870-9f0b579e326b","added_by":"auto","created_at":"2026-02-09 17:41:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2154574,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8399355/v1/bdc5bc72-c1fb-4903-b34f-3ca84ae52338.pdf"},{"id":102244906,"identity":"b63af73f-ca23-417b-a1ad-cb9a3167b758","added_by":"auto","created_at":"2026-02-09 17:41:16","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1299510,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8399355/v1/44535c29258d705a65fec715.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Knowledge, Practice (KP) and Healthcare Satisfaction Among Pregnant Women Using Blood Glucose Monitors for Gestational Diabetes Mellitus (GDM) Management in Trinidad and Tobago: A Cross-Sectional Observational Study","fulltext":[{"header":"Background","content":"\n\u003ch3\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003eHyperglycaemia occurs when there is too much glucose in the bloodstream due to insufficient insulin or an inability of the body to use insulin effectively for glucose uptake by cells (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Insulin is the hormone responsible for regulating blood glucose levels (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Diabetes, impairs the body\u0026rsquo;s ability to metabolize glucose, leading to periods of both hypoglycaemia (low blood sugar) and hyperglycaemia (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Gestational Diabetes Mellitus (GDM) specifically refers to glucose intolerance of varying degrees first identified during pregnancy (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Globally, one in six live births (16.8%) are affected by Diabetes in Pregnancy (DiP), 84% due to GDM and 16% to pre-existing diabetes, type 1 or type 2 diabetes, diagnosed during pregnancy (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGDM correlates with higher incidences of maternal morbidities including caesarean delivery, shoulder dystocia, birth trauma, preeclampsia, postpartum diabetes, and hypertensive and cardiovascular disease later in life. Additionally, GDM is linked to perinatal and neonatal morbidities such as macrosomia, birth injury, hypoglycaemia, polycythaemia, congenital malformations, hyperbilirubinemia and respiratory distress syndrome (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Notably, cases of DiP involving pre-existing diabetes may carry higher risks of complications compared to GDM alone.\u003c/p\u003e \u003cp\u003eDiP has some distinct risk factors but shares several with GDM, including increased body weight, low physical activity pre-pregnancy, advanced maternal age (35 years or older at estimated delivery), a body mass index \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e30 kg/m\u0026sup2;, and a family history of diabetes mellitus (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). A 2017 study among an Asian population identified associated risk factors, including a prior history of GDM, macrosomia, congenital anomalies, a body mass index (BMI) of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e25 kg/m\u0026sup2;, hypertension related to pregnancy, Polycystic Ovary Syndrome (PCOS), being aged 25 or older, having two or more previous pregnancies, and a history of abortion, stillbirth or preterm delivery (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Evidence also suggests that other risk factors include infants with a birth weight of 4.5 kg or greater, particularly first-degree relative who has a family history of diabetes and links to specific ethnic backgrounds. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAn article in UWI today by Fallon Lutchmansingh revealed approximately 20,000 births occur yearly in Trinidad and Tobago. About 1,000 of these are diagnosed pre-gestational diabetes, with gestational cases being estimated to be as much as three times this figure (9). Further research reinforced that between 1 in 5 to 1 in 6 pregnant women experience DiP, with over 80% of these cases attributed to GDM. Additionally, there is a 50% likelihood that affected mothers will develop diabetes later in life (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). A pilot screening initiative conducted in T\u0026amp;T utilized a standardized 75g oral glucose tolerance test (OGTT) following an overnight fast and identified a GDM prevalence of 14.1% among 658 pregnant women (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This highlights the urgent need for enhanced management of DiP in the region.\u003c/p\u003e \u003cp\u003eEffective management of DiP and GDM is essential, as outlined by the \u0026lsquo;Diabetes in Pregnancy: Management from Preconception to the Postnatal Period\u0026rsquo; guidelines from the National Institute for Health and Care Excellence (NICE) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Guidelines emphasize knowledge, resources, and healthcare support to reduce complications and improve outcomes, highlighting women empowerment through education on blood glucose control and individualized dietary and weight management.\u003c/p\u003e \u003cp\u003eFor women with GDM, understanding its implications is critical. Education on blood glucose control through diet, exercise, and medication is essential as poor management negatively impacts maternal and foetal health. Self-monitoring training supports greater autonomy in GDM management, and guidelines promote informed decision-making on risk assessments and testing, encouraging active participation in health management to prevent complications affecting both mother and child (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eRelevance to Public Health\u003c/h3\u003e\n\u003cp\u003eIn response to the high rates of DiP in Trinidad and Tobago (T\u0026amp;T), a screening and treatment initiative for DiP was introduced in 2015 by the Helen Bhagwansingh Diabetes Education, Research, and Prevention Institute (DERPi) at the University of the West Indies (UWI), St. Augustine, Trinidad, to improve health outcomes for pregnant women through targeted interventions. This initiative was expanded in 2020 through a collaboration between the Ministry of Health (MoH) and the Inter-American Development Bank (IDB), providing blood glucose monitors to healthcare facilities and pregnant women, training healthcare providers in DiP management, and promoting public awareness through educational campaigns (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This continued support provided, aimed to improve health outcomes and reduce complications associated with DiP.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRationale\u003c/h2\u003e \u003cp\u003eEnhancing women's knowledge about DiP, hyperglycaemia management, and access to quality healthcare has been shown to improve DiP outcomes(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This research aims to evaluate the ongoing efforts to reduce DiP rates in T\u0026amp;T. It is expected that improved knowledge, management practices, and higher patient satisfaction will lead to earlier detection and better overall health outcomes by reducing undiagnosed cases and improving maternal, perinatal, and neonatal wellbeing.\u003c/p\u003e \u003c/div\u003e"},{"header":"Literature review","content":"\u003cp\u003eA study conducted in Ethiopia by Dissassa et al. highlighted that insufficient knowledge regarding the risks, screening methods, and management among pregnant women about GDM correlated with poorer maternal and neonatal health outcomes (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Similarly, Siuluta et al. at Kinango District Hospital, Coastal Kenya assessed the knowledge, attitudes and practices of GDM among pregnant women showing that among the 354 participants, only 29.0% were knowledgeable but 46.98% were willing to learn more and apply better practices that could prevent or manage GDM effectively (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The study also shows that 60.17% of participants had good practice in GDM management, however, the majority did not consistently monitor their blood glucose levels nor attend regular antenatal check-ups (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Furthermore, the analysis from 84 observational studies conducted in Asia showed that the overall prevalence of GDM was 11.5% (95% CI 10.9\u0026ndash;12.1), highlighting the burden of GDM in the area and indicating a serious public health concern(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2022, T\u0026amp;T\u0026rsquo;s health sector noticed a similar trend of hesitance and non-recording with respect to screening for DiP (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In 2023, provisional data collected among three Regional Health Authorities (RHAs), showed that diabetes mellitus had a prevalence of 11.1% (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). There were gaps in the screening process and data recording which suggest that the true prevalence of DiP is likely underestimated and more widespread in T\u0026amp;T's broader population. The effects of unmanaged DiP must be reinforced and continuous interventions to improve the knowledge, practice and management within the wider population should be viewed as a benefit in addressing the growing concern and reducing associated risks.\u003c/p\u003e \u003cp\u003eFurthermore, the discrepancies imply that local health policies pertaining to screening and management must be modified to consider regional circumstances. The study advocates for targeted interventions aimed at high-risk groups, particularly women with previous GDM or those presenting other risk factors like obesity or advanced maternal age. Early identification and management can mitigate adverse outcomes for mothers and their children. Moreover, to lower the prevalence in at-risk groups, educational public health initiatives prioritizing lifestyle changes are crucial.\u003c/p\u003e \u003cp\u003eIn conclusion the meta-analysis and systematic review by Lee et al. shed important light on the risk factors and prevalence of GDM in Asia, highlighting the urgent need for improved screening procedures and focused therapies for high-risk women. Healthcare systems must prioritize GDM through specialized public health initiatives meant to lower its prevalence and enhance maternal-child health outcomes throughout the region given that it presents serious short- and long-term health hazards.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAim and Objectives\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eThe study aimed to analyse the knowledge and practices of pregnant women accessing public healthcare who received blood glucose monitors for managing Diabetes in Pregnancy (DiP) and to assess their satisfaction with the implemented Health Service Support Programme.\u003c/p\u003e \u003cp\u003e \u003cem\u003eObjectives\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEvaluate the level of knowledge about GDM management among pregnant women who received blood glucose monitors through the HSSP-DiP Project.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eInvestigate the practices of pregnant women in managing their blood glucose levels, including the use of blood glucose monitors, diet, and exercise.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAssess the satisfaction of participants with the healthcare services and resources provided under the HSSP-DiP Project, including the support received in managing their condition.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIdentify key demographic, medical, and lifestyle risk factors associated with poor knowledge, ineffective management, and low satisfaction among participants.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis was a cross-sectional observational study design.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSetting of the Study and Characteristics of Participants\u003c/h3\u003e\n\u003cp\u003eThe study population comprised pregnant women aged 18\u0026ndash;40 with DiP accessing public healthcare and having received blood glucose monitors from the HSSP-DiP Project across T\u0026amp;T between September 2023 and September 2024.\u003c/p\u003e\n\u003ch3\u003eSample Size Calculation and Sampling Methodology\u003c/h3\u003e\n\u003cp\u003eA stratified random sampling method was employed, with population stratified by RHAs: Eastern Regional Health Authority (ERHA), North Central Regional Health Authority (NCRHA), North-West Regional Health Authority (NWRHA), Southwest Regional Health Authority (SWRHA), and Tobago Regional Health Authority (TRHA). A simple random sample was conducted from each RHA, with proportional allocation based on the size of each stratum to ensure accurate representation of the national population. Based on a total population size of 1,190 pregnant women receiving care through the public health facilities. The sample size was calculated assuming a 50% estimated prevalence, 95% confidence interval at a 5% margin of error and an expected 85% response rate. A finite population correction was applied, resulting in a final adjusted sample size of the sample of 342.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and Exclusion Criteria\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eInclusion Criteria\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eWomen aged 18\u0026ndash;40 with DiP (either Gestational Diabetes Mellitus or Diabetes Mellitus in Pregnancy) enrolled in HSSP-DiP from September 2023 to September 2024.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePregnant women who received a blood glucose monitoring device from the HSSP-DiP.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eExclusion Criteria\u003c/h2\u003e \u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eNon-English Speakers (Language Barriers)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eExcludes participants with serious health conditions not commonly associated with DiP such as Cardiovascular Complications, Autoimmune Diseases, CKD, Severe Respiratory Illnesses, Infectious Diseases, Oncological Conditions, Mental Health Conditions, Blood, Liver and Neurological Disorders.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCognitive impairment.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eNo contact information available.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eParticipants were recruited from the HSSP-DiP project participant register using random selection methods. Selected individuals were contacted via telephone by trained interviewers. Each potential participant underwent eligibility screening, and their verbal consent was recorded prior to survey administration. A combination of pre-validated survey instruments was used and adapted for this study. Internal reliability was assessed for the modified scales using Cronbach\u0026rsquo;s alpha. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study was thoroughly explained to eligible participants and time was given for consideration before they provided final consent. The process remained non-coercive and emphasized informed consent and voluntary participation.\u003c/p\u003e \u003cp\u003eData was collected using password-protected tablets and entered directly into a secure Google Form. The survey lasted approximately 20 to 30 minutes and consisted of the following four sections.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSection A: Demographics and Pregnancy History \u0026ndash; 16 questions (3\u0026ndash;5 minutes)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSection B: Knowledge \u0026ndash; 16 questions (10\u0026ndash;12 minutes)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSection C: Practice \u0026ndash; 10 questions (5\u0026ndash;8 minutes)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSection D: Patient Satisfaction \u0026ndash; 9 questions (3\u0026ndash;5 minutes)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eThe dataset was cleaned, and missing values were addressed using the pairwise deletion method. Participants\u0026rsquo; responses were coded accordingly. A descriptive analysis was performed on the patient characteristics section of the survey using frequencies and percentages.\u003c/p\u003e \u003cp\u003eCronbach\u0026rsquo;s alpha statistics were calculated for the Knowledge, Practice and Healthcare Satisfaction (KPS) sections of the survey to assess internal consistency. Items with low reliability (Cronbach\u0026rsquo;s alpha\u0026thinsp;\u0026lt;\u0026thinsp;0.7) were removed from the analysis. Composite scores for Knowledge, Practice, and Satisfaction were computed by summing the corresponding item scores for each participant.\u003c/p\u003e \u003cp\u003eOutliers in the KPS scores were identified using boxplots and standardized Z-scores. Values with Z-scores less than \u0026minus;\u0026thinsp;2 or greater than +\u0026thinsp;2 were considered potential outliers and were excluded from all subsequent inferential analyses to minimize bias. Normality of continuous variables was assessed visually using Q-Q plots and statistically using skewness and kurtosis. Descriptive analyses were also conducted on the KPS scores. To ensure more representative estimates across RHAs, sampling weights were applied prior to inferential analyses.\u003c/p\u003e \u003cp\u003eTo explore group differences in average KPS scores by socio-demographic variables, independent sample t-tests were used for binary variables, and one-way ANOVA for categorical variables with three or more groups. Where significant differences were found via ANOVA, Tukey\u0026rsquo;s HSD post-hoc tests were applied to identify specific group-level differences.\u003c/p\u003e \u003cp\u003eFollowing the initial comparison of means, Multilinear Regression (MLR) analyses were conducted to identify independent predictors of KPS scores among women with DiP accessing the programme. Variables that were statistically significant or showed borderline significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10) in the univariate analyses were entered into the regression models. A backward selection method was applied to arrive at the most parsimonious models. All key assumptions of linear regression were evaluated and satisfied. Residuals for each model were approximately normally distributed (standardized residuals within \u0026plusmn;\u0026thinsp;3), with no evidence of heteroscedasticity or multicollinearity (all variance inflation factors [VIFs]\u0026thinsp;\u0026lt;\u0026thinsp;1.03; tolerance\u0026thinsp;\u0026gt;\u0026thinsp;0.97). Lastly, Pearson correlation analyses were conducted to explore significant relationships between KPS scores.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using SPSS version 27, with significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Analysis\u003c/h2\u003e \u003cp\u003eA total of 322 participants from five RHAs were included in the analysis. From Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the largest proportion being from SWRHA (28.9%) and NCRHA (28.0%), followed by NWRHA (22.0%), ERHA (16.5%), and TRHA (4.7%). Ethnic distribution showed a relatively even spread, with participants identifying as Mixed (34.2%), African (33.2%), East Indian (31.7%) and Other (0.9%). Most respondents were aged 25\u0026ndash;34 years (49.1%), 37.6% aged 35 and older, and 13.4% under 25. Regarding BMI, the majority of the women were obese and overweight (36.1% and 33.1% respectively) while 3.7% underweight. Most participants (98.7%) were not pregnant at the time of the survey.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic and clinical characteristics of study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \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\u003eMissing\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRHA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWRHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCRHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNWRHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eERHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Indian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e17 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.1\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=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.9\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=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrently Pregnant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e8 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Pregnancies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Live Births\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge of Youngest Child (months)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e14 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;36 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;36 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Weeks Pregnant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird trimester (\u003cspan additionalcitationids=\"CR29 CR30 CR31 CR32 CR33 CR34 CR35\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e318\u003c/p\u003e \u003cp\u003e(98.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst, Second, Full-term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhen Diagnosed with GDM (weeks)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly-onset GDM (\u0026lt;\u0026thinsp;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e20 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u0026ndash;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate-onset GDM (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePreviously Diagnosed with Diabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of Previous Diabetes Diagnosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e284 (88.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI don't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious Knowledge Perception\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA little\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuite a lot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA lot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNothing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGDM in a Previous Pregnancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e53 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI don\u0026rsquo;t know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThis table summarizes the frequency distributions of socio-demographic and clinical characteristics of study participants. For each variable, the table reports the number of missing cases and the percentage missing (in parentheses), followed by the observed frequency and corresponding percentage for each category.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor reproductive history, only four women (1.3%), all being in their third trimester, were pregnant at the time of the survey where 39.1% were primipara and 33.7% previously experienced more than two pregnancies. Five women who had previously been pregnant, excluding those were currently pregnant at the time of the survey, reported no live births. 17.2% reported that their youngest child\u0026rsquo;s age was above one year old.\u003c/p\u003e \u003cp\u003eMost women had neither a prior diabetes diagnosis (88.1%) nor a history of GDM (80.7%) before their last pregnancy. In that pregnancy, early-onset GDM (\u0026lt;\u0026thinsp;24 weeks) was most common (62.6%), while late-onset GDM (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e29 weeks) was least prevalent (13.9%). Among women previously diagnosed with diabetes, 63.2% had Type 2 diabetes, and nearly half (50%) reported limited perceived knowledge of diabetes in pregnancy.\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eReliability Analysis\u003c/h2\u003e \u003cp\u003eThe internal consistency of the three scales was assessed using Cronbach\u0026rsquo;s alpha. For the Knowledge scale, 11 out of 16 items were retained, yielding a Cronbach\u0026rsquo;s alpha of 0.7, indicating acceptable reliability. The Practice scale retained all 10 items, also achieving Cronbach\u0026rsquo;s alpha of 0.7. For the Satisfaction scale, 8 of the original 9 items were retained, resulting in Cronbach\u0026rsquo;s alpha of 0.8, suggesting good internal consistency. These results indicate that all three scales demonstrated adequate reliability for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eOutliers Detection and Mitigation\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below presents boxplots comparing the distribution of KPS scores before (a) and after (b) the removal of outliers. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea shows that a few mild outliers are observed below the lower whisker, indicating some participants scored noticeably lower than the majority. After removal, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, the distribution becomes more symmetric, with none to moderate visible outliers. The interquartile range remains consistent, although Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb reflects a slightly more compact distribution. The satisfaction variable displays the highest number of outliers in before outlier removal, with several participants scoring far below the main cluster. After outlier removal, the distribution becomes more symmetrical and representative of the central tendency, with a marked reduction in variability. The removal of outliers resulted in clearer, more normally distributed data especially for the Satisfaction Score, where extreme low values were influencing the spread and skewness.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe figure presents boxplots of knowledge, practice and satisfaction scores: (a) before outlier removal and (b) after outlier removal.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Analysis of Knowledge, Practice and Satisfaction Scores\u003c/h2\u003e \u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the mean Knowledge Score was 6.9 (SD\u0026thinsp;=\u0026thinsp;1.9), with scores ranging from 3 to 11. The Practice Score had a mean of 11.5 (SD\u0026thinsp;=\u0026thinsp;2.7), ranging from 6 to 16, while the Satisfaction Score ranged from 36 to 56, with a mean of 49.6 (SD\u0026thinsp;=\u0026thinsp;5.1). Assessment of normality was first done using Q-Q plots and was further assessed using skewness and kurtosis values as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results indicated that all three variables were approximately normally distributed where skewness values ranged from \u0026minus;\u0026thinsp;0.6 to 0.0, and kurtosis values from \u0026minus;\u0026thinsp;1.0 to -0.3, all within the acceptable range of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:2\\)\u003c/span\u003e\u003c/span\u003e. These results support the use of parametric statistical tests in subsequent analyses.\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\u003eDescriptive statistics for knowledge, practice and satisfaction scores among study participants\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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKurtosis\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\u003eKnowledge Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePractice Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSatisfaction Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.3\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\u003eThis table summarizes the minimum and maximum values, means, standard deviations, and measures of distribution (skewness and kurtosis) for knowledge, practice, and satisfaction scores among study participants after the removal of outliers. The skewness and kurtosis values demonstrated that the scores are approximately normal since they are within the acceptable range (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:2\\)\u003c/span\u003e\u003c/span\u003e), supporting the use of parametric statistical analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eInferential Analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the means and standard deviation of Knowledge, Practice, and Satisfaction scores across participant characteristics. Significant differences were found across age groups for all three age categories. Participants aged 35\u0026thinsp;+\u0026thinsp;had significantly higher Knowledge scores than those under 25 (p\u0026thinsp;=\u0026thinsp;0.005). Likewise, older participants (35+) reported significantly better Practice (Mean Diff. = 1.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Satisfaction scores compared to those under 25 (p\u0026thinsp;=\u0026thinsp;0.102, not significant, but trend noted). The 25\u0026ndash;34 age group also reported higher Satisfaction than those\u0026thinsp;\u0026lt;\u0026thinsp;25 (Mean Diff. = 2.72, p\u0026thinsp;=\u0026thinsp;0.005).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of mean knowledge, practice, and satisfaction scores across sociodemographic and clinical characteristics of study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePractice\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eSatisfaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\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\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eRHA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWRHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCRHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNWRHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eERHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfrican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Indian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e53.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eᵻ\u003c/sup\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\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\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.2\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\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrently Pregnant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.024*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Pregnancies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Live Births\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.961 \u003csup\u003eᵻ\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge of Youngest Child (months)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.014*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;36 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;36 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Weeks Pregnant (Currently pregnant women at time of interview)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird trimester (\u003cspan additionalcitationids=\"CR29 CR30 CR31 CR32 CR33 CR34 CR35\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhen Diagnosed with GDM (weeks)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u0026ndash;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePreviously Diagnosed with Diabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of Previous Diabetes Diagnosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI don't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious Knowledge Perception about Diabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNothing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA little\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuite a lot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA lot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGDM in a Previous Pregnancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI don't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003eᵻ\u003c/sup\u003e Kruskal Wallis Test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e*Significant at the 5% level of significance\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eComparison of mean knowledge, practice, and satisfaction scores across sociodemographic and clinical characteristics. Means (SD) are reported. p-values were calculated using independent sample T-test or ANOVA where appropriate unless indicated by \u003csup\u003eᵻ\u003c/sup\u003e (Kruskal Wallis test). * indicates statistical significance at the 5% level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eParticipants who youngest child is over 36 months had significantly lower Satisfaction scores compared to those with younger children. Specifically, they scored significantly lower than the 12\u0026ndash;36 months group (Mean Diff. = \u0026minus;\u0026thinsp;7.60, p\u0026thinsp;=\u0026thinsp;0.010) and the \u0026lt;\u0026thinsp;12 months group (Mean Diff. = \u0026minus;\u0026thinsp;7.05, p\u0026thinsp;=\u0026thinsp;0.015).\u003c/p\u003e \u003cp\u003eParticipants diagnosed with GDM before 24 weeks gestation had significantly higher Satisfaction scores than those diagnosed between 24\u0026ndash;28 weeks (Mean Diff. = 2.02, p\u0026thinsp;=\u0026thinsp;0.015). No other pairwise differences were statistically significant.\u003c/p\u003e \u003cp\u003eThere were significant differences in Knowledge scores by perceived prior knowledge. Those who reported knowing \"quite a lot\" scored significantly higher than those knowing \"nothing\" (Mean Diff. = 1.59, p\u0026thinsp;=\u0026thinsp;0.001) or \"a little\" (Mean Diff. = 0.95, p\u0026thinsp;=\u0026thinsp;0.005). Similarly, those reported knowing \"a lot\" also scored higher than those with no knowledge (Mean Diff. = 1.34, p\u0026thinsp;=\u0026thinsp;0.012).\u003c/p\u003e \u003cp\u003eWhile not all comparisons were statistically significant, currently pregnant participants tended to report higher Knowledge scores (M\u0026thinsp;=\u0026thinsp;8.6) but lower Satisfaction (M\u0026thinsp;=\u0026thinsp;44.3) than those not pregnant (p\u0026thinsp;=\u0026thinsp;0.074 and p\u0026thinsp;=\u0026thinsp;0.024 respectively).\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMultiple Linear Regression Model\u003c/h2\u003e \u003cp\u003eAll regression models shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e met the assumptions for normality, linearity, homoscedasticity, and the absence of multicollinearity, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe Knowledge Score model was statistically significant (F(3, 299)\u0026thinsp;=\u0026thinsp;8.923, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), explaining 7.3% of the variance. Significant predictors included older age (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e = 0.156, p\u0026thinsp;=\u0026thinsp;0.006), current pregnancy status (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e = 0.114, p\u0026thinsp;=\u0026thinsp;0.041), and higher perceived prior knowledge about DiP (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e = 0.198, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggests that these factors contribute modestly to better patient knowledge.\u003c/p\u003e \u003cp\u003eThe Practice Score model was also significant (F(1, 307)\u0026thinsp;=\u0026thinsp;17.262, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), accounting for 5.0% of the variance. Age was the sole significant predictor (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e = 0.231, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that older women engaged in stronger diabetes management practices. Other variables were excluded during model refinement due to lack of significance.\u003c/p\u003e \u003cp\u003eThe Satisfaction Score model explained 5.6% of variance (F(2, 267)\u0026thinsp;=\u0026thinsp;8.928, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Women currently pregnant reported lower satisfaction with their care (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e = \u0026minus;\u0026thinsp;0.124, p\u0026thinsp;=\u0026thinsp;0.037), and those diagnosed with gestational diabetes later in pregnancy had lower satisfaction scores (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e = \u0026minus;\u0026thinsp;0.225, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings highlight potential gaps in patient experience related to timing of diagnosis and pregnancy status.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis figure presents the standardized regression coefficients \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\beta\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e for significant predictors of knowledge, practice, and satisfaction scores among women with diabetes in pregnancy. Only predictors that remained statistically significant in the final models are shown. All models met assumptions of normality, linearity, homoscedasticity, and absence of multicollinearity.\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\u003eSummary of multiple linear regression models predicting knowledge, practice, and satisfaction (KPS) scores among women with diabetes in pregnancy\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePractice\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSatisfaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\varvec{\\beta\\:}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\left(\\varvec{\\beta\\:}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\varvec{\\beta\\:}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\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\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNS (excluded)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently Pregnant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNS (excluded)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.124,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Knowledge About Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNS (excluded)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNS (excluded)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDM Diagnosis Timing (weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNS (excluded)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNS (excluded)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdjusted\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{R}\u0026sup2;\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e7.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e5.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNS \u0026ndash; Not significant\u003c/p\u003e\u003cp\u003eThe table summarizes the regression coefficients \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\beta\\:\\right)\\:\\)\u003c/span\u003e\u003c/span\u003eand p-values for predictors included in the multiple linear regression models of knowledge, practice, and satisfaction scores. Adjusted \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:R\u0026sup2;\\)\u003c/span\u003e\u003c/span\u003e values indicate the proportion of variance explained by each model. NS indicates predictors that were excluded from the final model due to lack of statistical significance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eCorrelation Analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that there exists a significantly weak positive linear correlation between women\u0026rsquo;s knowledge of diabetes in pregnancy and their management practice scores (r\u0026thinsp;=\u0026thinsp;0.119, p\u0026thinsp;=\u0026thinsp;0.037). Similarly, a significantly weak positive correlation exists between their management practice and healthcare satisfaction scores (r\u0026thinsp;=\u0026thinsp;0.168, p\u0026thinsp;=\u0026thinsp;0.003). On the other hand, the data does not suggest any significant linear correlation between their knowledge and healthcare satisfaction scores.\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\u003ePearson correlation analysis of knowledge, practice and satisfaction (KPS) scores of women with diabetes in pregnancy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnowledge Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePractice Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSatisfaction Score\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\u003eKnowledge Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePractice Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.119*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSatisfaction Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.168**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.531)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Significant at the 5% level of significance\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e**Significant at the 1% level of significance\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis table presents Pearson correlation coefficients (r) among knowledge, practice, and satisfaction scores for women with diabetes in pregnancy. A single asterisk (*) indicates significance at the 5% level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and a double asterisk (**) indicates significance at the 1% level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Weak positive correlations were observed between knowledge and practice scores (r\u0026thinsp;=\u0026thinsp;0.119, p\u0026thinsp;=\u0026thinsp;0.037) and between practice and satisfaction scores (r\u0026thinsp;=\u0026thinsp;0.168, p\u0026thinsp;=\u0026thinsp;0.003). No significant correlation was observed between knowledge and satisfaction scores.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eObesity increases insulin resistance, which can predispose some women to a diabetic state. Although elevated BMI is a well-established risk factor for gestational diabetes mellitus (GDM), this study found no significant association between BMI categories and women's knowledge of diabetes in pregnancy (DiP), their management practices during pregnancy, or their satisfaction with the HSSP-DiP program (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Evidence from the United Kingdom indicates that for every 1 kg/m\u0026sup2; increase in body mass index (BMI), the prevalence of gestational diabetes mellitus (GDM) increases by approximately 0.92% (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Despite more than half of the women in this study from Trinidad and Tobago being classified as overweight or obese, BMI did not significantly influence any of the key outcomes: knowledge of DiP, management practices, or healthcare satisfaction. This suggests that the HSSP-DiP intervention was equitably effective across BMI groups and capable of reaching a diverse population. These findings reinforce the Ministry of Health's commitment to delivering inclusive and equitable maternal health services (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Nevertheless, the high prevalence of GDM observed in this population warrants attention. There is a clear need for targeted preconception weight management strategies and healthy lifestyle interventions among women in Trinidad and Tobago planning to conceive. Such efforts may contribute to reducing the burden of GDM and, by extension, improving both maternal and fetal health outcomes.\u003c/p\u003e \u003cp\u003eAdditionally, a large proportion of women, excluding primiparas, reported no prior history of diabetes or GDM before their most recent pregnancy. This raises concerns about why these women are now developing GDM and whether the prevalence is increasing over time. Furthermore, the 11.9% prevalence of pre-existing diabetes observed in this study is markedly higher than the global estimate of 1\u0026ndash;2%, suggesting either a population with elevated baseline risk factors or that global benchmarks may not fully reflect the local and cultural context in T\u0026amp;T (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The International Diabetes Federation estimates adult diabetes prevalence in T\u0026amp;T to be approximately 12%, and given the increasing prevalence of obesity and other susceptibility factors in the population, the rate of GDM may be rising over time (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTypically according to the global norms, the WHO recommends universal screening for GDM is between 24\u0026ndash;28 weeks of gestation and only earlier screening for high-risk individuals (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, it was recommended by the MoH, T\u0026amp;T that screening of women in the country should not be based on risk factors since 50% of cases can be missed and screening should be considered within the first trimester when booking blood tests are being done (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Consequently, more than half the women in this study had early-onset GDM (less than 24 weeks) and the least proportion of mothers were diagnosed within the third trimester. The promotion for early screening among women in the country is evident thus highlighting the positive impact made by the Ministry and HSSP-DiP initiatives to apply a more aggressive early screening practice in the primary care settings regardless of gestation (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Additionally, a substantial proportion of women diagnosed early with GDM had no prior history of DiP or GDM. Although early screening appears effective in detecting GDM at an earlier stage, an outcome with several clinical benefits, this finding also raises concerns. It suggests a possible increase in underlying risk factors among the antenatal population, given that these women are now presenting with GDM in the first trimester despite no previous diagnosis. This trend may reflect not only the success of earlier screening initiatives but also a shifting risk profile among pregnant women nationally.\u003c/p\u003e \u003cp\u003eFrom the moment a diagnosis of GDM is made, women\u0026rsquo;s healthcare experiences are largely shaped by their interactions with providers, which can vary depending on the model of care delivered. This study found that women diagnosed later in pregnancy tended to report lower satisfaction with their healthcare. It is possible that those diagnosed later did not fully experience or adequately assess the entire HSSP-DiP program, which may have influenced their satisfaction levels. Previous research has demonstrated that women\u0026rsquo;s satisfaction often declines immediately following diagnosis, largely due to emotional distress, psychological shock, and the sudden adjustment to managing a complex condition (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The range of emotions experienced can be traumatic for the expectant mother, as most pregnant women anticipate a healthy fetus and do not perceive themselves to be at risk (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). A late adverse diagnosis can therefore be overwhelming, and with limited time for management and adjustment, it may negatively affect the overall care experience. While the findings of this study highlight the program\u0026rsquo;s effectiveness, where women engaged in care for a longer period reported greater satisfaction, targeted strategies may be necessary to improve healthcare satisfaction among women diagnosed in the third trimester, as they tend to be more sensitive to treatment during this late stage of pregnancy.\u003c/p\u003e \u003cp\u003eOlder women in the sample were found to have higher levels of knowledge regarding DiP and GDM. Specifically, women of advanced maternal age (35 years and older) scored significantly higher on knowledge assessments compared to mothers under 25 years. This finding is supported by other studies, which suggest that older women may have higher parity and are more likely to have received information about GDM during antenatal care visits in previous pregnancies (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). However, this is not a consistent finding across all settings. For example, some studies report no significant differences in knowledge scores between women above and below the advanced maternal age threshold (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Dissassa et al. even found that women under 24 years had higher odds of possessing sufficient knowledge about GDM, possibly due to higher education levels in that age group, which may enhance understanding (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). These mixed findings suggest the need for further investigation into why older women in this context appear more knowledgeable, and to inform the development of targeted interventions aimed at improving DiP and GDM knowledge among younger women.\u003c/p\u003e \u003cp\u003eAdditionally, although limited empirical evidence directly links increased maternal age to better gestational diabetes management, the findings of this study suggest that these women may engage in more effective self-management practices during pregnancy. However, previous research provides indirect evidence suggesting that these women are more likely to be multiparous and may benefit from prior pregnancy experience resulting in better self-management and glycaemia control. Additionally, healthcare providers may exert greater effort to ensure appropriate management practices, given the higher baseline risk associated with advanced maternal age (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Therefore, although some reports have shown younger women having a higher knowledge level regarding GDM, advanced maternal age may benefit from practical experience and structured health behaviors established over time. Other studies would have shown that older adults were more compliant with dietary and self-monitoring routines (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). With advancing maternal age, these women may have had more exposure to health messaging or previous pregnancies, enhancing their awareness and ability to manage their condition (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). One research found that pregnant women aged \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e35 years were over twice as likely to be aware of GDM compared to younger women aged 20\u0026ndash;24 years suggesting that they may have greater health exposure from previous pregnancies or better understanding due to increased maturity and health-seeking behavior (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Furthermore, it was acknowledged that parity alone did not account for this increased awareness, meaning age itself may play a direct role in shaping attitudes and behavior around GDM (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Together, these factors could account for the observed differences in management practices. Hence, recommendations include tailoring diabetes education and support materials specifically for younger women. These could include simplified messages, visual aids, and engagement through mobile and social media platforms.\u003c/p\u003e \u003cp\u003eWomen who reported prior knowledge of diabetes in pregnancy (DiP) and gestational diabetes mellitus (GDM) scored significantly higher on the knowledge assessment in this study. Women who believed they knew \u0026ldquo;quite a lot\u0026rdquo; or \u0026ldquo;a lot\u0026rdquo; about DiP scored significantly higher on knowledge assessments than those who felt they knew \u0026ldquo;nothing\u0026rdquo; or \u0026ldquo;a little.\u0026rdquo; This suggests that self-perception closely reflects actual knowledge. This suggests that increased awareness, potentially gained through formal education or public health messaging, may positively influence younger women's understanding of pregnancy-related conditions in Trinidad and Tobago. Greater knowledge may contribute to improved pregnancy experiences by promoting earlier recognition of risk factors and facilitating healthier lifestyle choices from a younger age, thereby helping to reduce the risk of GDM and its associated complications. Materials should be designed to challenge assumptions and promote active learning, particularly for those who feel they already \u0026ldquo;know enough\u0026rdquo;. Several countries, including Finland and Singapore, have adopted similar strategies by incorporating GDM awareness into preconception and maternal health initiatives targeting young women. These efforts aim to reduce the burden of diabetes and other chronic conditions through early education and prevention (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLastly, women who were currently pregnant at the time of the interview, though comprising only 1.3% of the sample, demonstrated significantly higher knowledge regarding pregnancy-related conditions but reported lower levels of healthcare satisfaction. This may reflect their heightened awareness of GDM during the ongoing pregnancy, while simultaneously being less able to evaluate the overall quality of the HSSP-DiP intervention. Notably, previous research has shown that the timing of measurement can influence women's reported experiences of antenatal care, highlighting that perceptions may differ depending on whether assessments occur before or after childbirth (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). However, this finding should be interpreted with caution, as only four currently pregnant women participated in the study, limiting the generalizability and statistical power of conclusions drawn from this subgroup.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eDespite the strengths of the cross-sectional design and the use of nationwide sampling, several limitations affected the interpretation of the findings.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe study relied on self-reported data, which may introduce recall bias. Furthermore, the absence of clinical outcome measures (such as birth weight or pregnancy complications) limits the ability to directly correlate maternal knowledge and practices with specific health outcomes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLogistic and operational challenges significantly impacted participant recruitment and data collection. New mothers were often occupied with childcare responsibilities or work, making follow-up calls challenging. While several women initially agreed to participate, many became unreachable afterward. Incorrect or out-of-service contact numbers further hindered efforts and contributed to a smaller-than-anticipated sample size.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSurvival bias posed significant challenges in data collection as some mothers had miscarriages or neonatal deaths and opted to discontinue participation. This would impact the interpretation of data, introducing bias to the rate ratio estimate and the evaluation of prevalence rate.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides important insights into the knowledge, management, and healthcare satisfaction related to diabetes in pregnancy (DiP) and gestational diabetes mellitus (GDM) among women in Trinidad and Tobago. Despite the high prevalence of obesity and GDM, BMI was not a significant factor in influencing women\u0026rsquo;s knowledge, practices, or satisfaction, indicating the equitable reach of the HSSP-DiP intervention across demographic groups. However, the notable prevalence of early-onset and pre-existing diabetes highlights a shifting risk profile that calls for continued surveillance and preconception public health strategies. Age, parity, and prior experience appear to influence knowledge and self-management, with older women generally demonstrating greater awareness and potentially more effective management behaviors. Conversely, younger and first-time mothers may require tailored educational efforts to build competence and confidence in managing GDM. Additionally, timing of diagnosis and pregnancy status were found to affect healthcare satisfaction, underscoring the need for more responsive care models, particularly for women diagnosed later in pregnancy. Together, these findings reinforce the importance of culturally relevant, age-sensitive, and timing-specific interventions to reduce the burden of GDM and enhance maternal and fetal outcomes across Trinidad and Tobago.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCKD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic Kidney Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDERPi\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHelen Bhagwansingh Diabetes Education, Research, and Prevention Institute\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDiP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiabetes in Pregnancy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eERHA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEastern Regional Health Authority\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFMS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFaculty of Medical Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGDM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGestational Diabetes Mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHSSP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth Service Support Programme\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHSSP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003eDiP\u003c/b\u003e\u0026ndash;Health Services Support Programme\u0026rsquo;s Diabetes in Pregnancy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIDB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Development Bank\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eKPS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKnowledge, Practice and Satisfaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMLR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultiple Linear Regression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMoH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinistry of Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNCRHA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNorth Central Regional Health Authority\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNICE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Institute for Health and Care Excellence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNWRHA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNorth West Regional Health Authority\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOGTT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOral Glucose Tolerance Test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ep\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ep\u0026ndash;value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePIU\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProject Implementation Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePCOS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolycystic Ovary Syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRHAs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegional Health Authorities\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSPSS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSWRHA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSouth West Regional Health Authority\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTRHA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTobago Regional Health Authority\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eT\u0026amp;T\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTrinidad and Tobago\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUWI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe University of the West Indies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWHO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was obtained from the University of the West Indies, St. Augustine Campus Research Ethics Committee (Reference: \u003cstrong\u003eCREC-SA.2991/11/2024\u003c/strong\u003e), the Ministry of Health Ethics Committee (Reference: \u003cstrong\u003eHe: 3/13/1441 Vol. II\u003c/strong\u003e), and the Tobago House of Assembly Research Ethics Committee (Reference: \u003cstrong\u003eTHAREC: 005/01/2025\u003c/strong\u003e. Informed verbal consent was obtained from all study participants prior to data collection.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAJ, AS, RF, and : Conceptualization; supervision; project administration.\u003cbr\u003e\u0026nbsp;AJ: Visualization; methodology; formal analysis; data curation; writing \u0026ndash; original draft; writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u0026nbsp;AS and RF: Writing \u0026ndash; original draft; writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u0026nbsp;ZR, WS, ZCQ, ZG, ZA, AF, and ZP: Investigation; formal analysis; data curation; writing \u0026ndash; original draft; writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to acknowledge the Health Services Support Programme - Project Implementation Unit (HSSP-PIU) Ministry of Health, the Inter-American Development Bank (IDB), and Samantha Llanos for their support and contributions to this research.\u003c/p\u003e\n\n"},{"header":"References ","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHyperglycemia. 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Effect of Implementation of Health Educational Guidelines on Maternal and Neonatal Outcomes among Women with Gestational Diabetes Mellitus. Tanta Sci Nurs J. 2019;17(2):148\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDissassa HD, Tufa DG, Geleta LA, Dabalo YA, Oyato BT. Knowledge on gestational diabetes mellitus and associated factors among pregnant women attending antenatal care clinics of North Shewa zone public hospitals, Oromia region, Central Ethiopia: a cross-sectional study. BMJ Open 2023 Sept 26;13(9):e073339.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiuluta N, Sato M, Linh LK, Wanjihia V, Changoma MS, Huy NT, et al. Assessment of gestational diabetes mellitus knowledge, attitudes, and practices and associated factors among pregnant women at a district hospital in Coastal Kenya. Trop Med Health. 2024;52(1):74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoopnarinesingh N, Brennan N, Khan C, Ladenson P, Hill-Briggs F, Kalyani R. Barriers to optimal diabetes care in Trinidad and Tobago: a health care Professionals\u0026rsquo; perspective. BMC Health Serv Res. 2015 July;19:15:396.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee C, Zhu S, Wu Q, Hu Y, Chen Y, Chen D, et al. Independent and Joint Associations of Age, Prepregnancy BMI, and Gestational Weight Gain with Adverse Pregnancy Outcomes in Gestational Diabetes Mellitus. Diabetes Therapy. 2022;14:363\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorloni MR, Betr\u0026aacute;n AP, Horta BL, Nakamura MU, Atallah AN, Moron AF, et al. Prepregnancy BMI and the risk of gestational diabetes: a systematic review of the literature with meta-analysis. Obes Rev. 2009;10(2):194\u0026ndash;203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDirectorate of Women\u0026rsquo;s Health Ministry of Health, Rukiya Livan, Obstetrics and Gynaecology team, Sangre Grande Hospital, ERHA. Diabetes Mellitus and Pregnancy: Clinical Guideline [Internet]. Directorate of Women\u0026rsquo;s Health Ministry of Health, Trinidad and Tobago. 2018. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://health.gov.tt/sites/default/files/womenshealth/20181121-Womens-Health-diabetes-mellitus.pdf\u003c/span\u003e\u003cspan address=\"https://health.gov.tt/sites/default/files/womenshealth/20181121-Womens-Health-diabetes-mellitus.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChivese T, Hoegfeldt CA, Werfalli M, Yuen L, Sun H, Karuranga S, IDF Diabetes Atlas. : The prevalence of pre-existing diabetes in pregnancy \u0026ndash; A systematic review and meta-analysis of studies published during 2010\u0026ndash;2020. Diabetes Research and Clinical Practice [Internet]. 2021 June 12;183. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.diabetesresearchclinicalpractice.com/article/S0168-8227(21)00408-3/fulltext\u003c/span\u003e\u003cspan address=\"https://www.diabetesresearchclinicalpractice.com/article/S0168-8227(21)00408-3/fulltext\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnastasiou E, Farmakidis G, Gerede A, Goulis DG, Koukkou E, Kourtis A, et al. Clinical practice guidelines on diabetes mellitus and pregnancy: Ι. Pre-existing type 1 and type 2 diabetes mellitus. Int J Endocrinol Metabolism. 2020;19:593\u0026ndash;600.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (WHO). Diagnostic Criteria and Classification of Hyperglycaemia First Detected in Pregnancy [Internet]. 2013. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.who.int/bitstream/handle/10665/85975/WHO_NMH_MND_13.2_eng.pdf\u003c/span\u003e\u003cspan address=\"https://iris.who.int/bitstream/handle/10665/85975/WHO_NMH_MND_13.2_eng.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsaacs NZ, Andipatin MG. A systematic review regarding women\u0026rsquo;s emotional and psychological experiences of high-risk pregnancies. BMC Psychol. 2020;8:45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eByakwaga E, Sekikubo M, Nakimuli A. Level of and factors associated with awareness of gestational diabetes mellitus among pregnant women attending antenatal care at Kawempe National Referral Hospital: a cross sectional study. BMC Pregnancy Childbirth. 2021 June 30;21:467.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas S, Pienyu R, Rajan SK. Awareness and knowledge about gestational diabetes mellitus among antenatal women. Psychol Community Health. 2020;8(1):237\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheinis M, Carpe N, Gold S, Selk A. Ignorance is bliss: women\u0026rsquo;s knowledge regarding age-related pregnancy risks. J Obstet Gynaecol 2017 June 22;38(3):344\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang R, Chen J, Yao F, Sun T, Qiang Y, Li H, et al. Number of parous events affects the association between physical exercise and glycemic control among women with gestational diabetes mellitus: A prospective cohort study. J Sport Health Sci. 2022 Sept;11(5):586\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie Z, Liu K, Or C, Chen J, Yan M, Wang H. An examination of the socio-demographic correlates of patient adherence to self-management behaviors and the mediating roles of health attitudes and self-efficacy among patients with coexisting type 2 diabetes and hypertension. BMC Public Health. 2020;20(1):1227.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBioSpectrum Asia [Internet]. 2019. Singapore makes progress in war against diabetes. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.biospectrumasia.com/news/30/14941/singapore-makes-progress-in-war-against-diabetes.html\u003c/span\u003e\u003cspan address=\"https://www.biospectrumasia.com/news/30/14941/singapore-makes-progress-in-war-against-diabetes.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOECD, Paris/European Observatory on Health Systems and Policies, Brussels. Finland: Country Health Profile 2019. In Finland: OECD. 2019. (State of Health in the EU). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.oecd.org/en/publications/finland-country-health-profile-2019_20656739-en.html\u003c/span\u003e\u003cspan address=\"https://www.oecd.org/en/publications/finland-country-health-profile-2019_20656739-en.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScheerhagen M, Birnie E, Franx A, Van Stel HF, Bonsel GJ. Measuring clients\u0026rsquo; experiences with antenatal care before or after childbirth: it matters. PeerJ. 2018;6:e5851.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcLarty C. Development of a questionnaire to assess knowledge in women with gestational diabetes. In. 1993. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://api.semanticscholar.org/CorpusID:58254906\u003c/span\u003e\u003cspan address=\"https://api.semanticscholar.org/CorpusID:58254906\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan J, Chen L, Wu Y, Zhu X, Fei H, Knowledge. Attitude and Practice of Patients with Gestational Diabetes Mellitus Regarding Gestational Diabetes Mellitus: A Cross-Sectional Study. Int J Gen Med. 2023;16:4365\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirst JE, Mackillop L, Loerup L, Kevat DA, Bartlett K, Gibson O, et al. Acceptability and User Satisfaction of a Smartphone-Based, Interactive Blood Glucose Management System in Women With Gestational Diabetes Mellitus. J Diabetes Sci Technol. 2015;9(1):111\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrinidad. and Tobago - International Diabetes Federation [Internet]. [cited 2025 Dec 20]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://idf.org/our-network/regions-and-members/north-america-and-caribbean/members/trinidad-and-tobago/\u003c/span\u003e\u003cspan address=\"https://idf.org/our-network/regions-and-members/north-america-and-caribbean/members/trinidad-and-tobago/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Gestational Diabetes Mellitus, Pregnancy, Self-management, Knowledge, Practices, Satisfaction, Blood Glucose Monitors, Trinidad and Tobago","lastPublishedDoi":"10.21203/rs.3.rs-8399355/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8399355/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHyperglycaemia and consequently, GDM, is the predominant medical condition encountered during pregnancy in Trinidad and Tobago, leading to significant maternal and neonatal complications. Thus, there was a need to reduce mortality and morbidity through the early detection and management of GDM while empowering women to take long-term control of their health.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study used two-stage stratified random sampling across all five Regional Health Authorities (RHAs). Data were collected via telephone surveys with 323 eligible women aged 18\u0026ndash;40 who had received blood glucose monitors between September 2023 and September 2024. Composite scores for knowledge, practice, and satisfaction were derived from validated survey sections. Data were analysed using descriptive statistics, t-tests, ANOVA, Tukey\u0026rsquo;s post-hoc tests, and multiple linear regression models. Sampling weights were applied to adjust for proportional RHA representation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMost participants were from SWRHA (28.9%) and NCRHA (28.0%), with the majority aged 25\u0026ndash;34 (49.1%). Mean scores were Knowledge\u0026thinsp;=\u0026thinsp;6.9 (SD\u0026thinsp;=\u0026thinsp;1.9, possible range 0\u0026ndash;11), Practice\u0026thinsp;=\u0026thinsp;11.5 (2.7, 0\u0026ndash;16), and Satisfaction\u0026thinsp;=\u0026thinsp;49.6 (5.1, 8\u0026ndash;56). Older age (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e=0.156, p\u0026thinsp;=\u0026thinsp;0.006), current pregnancy status (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e=0.114, p\u0026thinsp;=\u0026thinsp;0.041), and higher perceived prior knowledge (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e=0.198, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly associated with higher knowledge scores. Older age showed a significant association with diabetes management practices (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e=0.231, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Lower satisfaction scores were observed among women pregnant during the interview (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e=\u0026ndash;0.124, p\u0026thinsp;=\u0026thinsp;0.037) and those diagnosed later in pregnancy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e= \u0026minus;\u0026thinsp;0.225, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). All analyses were performed using IBM SPSS version 25.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003e Among women enrolled in the HSSP-DiP initiative higher knowledge and self-management scores were observed in older participants and those with prior awareness. BMI did not significantly influence knowledge, practices, or satisfaction, which suggests equitable reach across weight categories. However, currently pregnant women and those diagnosed later in pregnancy reported lower satisfaction with their care, indicating a need for more responsive support during late diagnosis and active pregnancy. These findings suggest age-sensitive and timing-specific considerations may be important for improving maternal experiences and outcomes.\u003c/p\u003e","manuscriptTitle":"Knowledge, Practice (KP) and Healthcare Satisfaction Among Pregnant Women Using Blood Glucose Monitors for Gestational Diabetes Mellitus (GDM) Management in Trinidad and Tobago: A Cross-Sectional Observational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 17:40:38","doi":"10.21203/rs.3.rs-8399355/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-24T02:38:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12082976932853670421678626556835395747","date":"2026-02-10T15:28:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217442326239385120176752067817747901935","date":"2026-02-07T14:24:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70500526739709142109770152687681140850","date":"2026-02-07T03:02:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-05T11:38:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-05T19:36:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-22T01:53:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-22T01:52:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2025-12-19T00:01:10+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":"9add0110-e047-47c8-95a8-fc23b8bf9e99","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T17:40:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 17:40:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8399355","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8399355","identity":"rs-8399355","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}