Self-Reported Diabetes Or Hypertension Diagnoses And Antenatal Care Among Child-Bearing Women In Rural Bangladesh: A Cross-Sectional Study

Self-Reported Diabetes Or Hypertension Diagnoses And Antenatal Care Among Child-Bearing Women In Rural Bangladesh: A Cross-Sectional 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 Self-Reported Diabetes Or Hypertension Diagnoses And Antenatal Care Among Child-Bearing Women In Rural Bangladesh: A Cross-Sectional Study Allyson P. Bear, Wendy L. Bennett, Joanne Katz, Kyu Han Lee, Atique Iqbal Chowdhury, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-964052/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Health care systems in limited resource settings may not meet the needs of pregnant women where the burden of diabetes and hypertension is rapidly increasing. We described screening and diagnosis of diabetes or hypertension among recently pregnant women in rural Bangladesh and the antenatal care received. Methods: We asked recently pregnant women about ever having been screened for or diagnosed with hypertension or diabetes and their antenatal care-seeking experiences in a cross-sectional survey in the Baliakandi, Bangladesh. We used chi-squared tests and logistic regression to test the associations between self-reported coverage of hypertension and diabetes screening, diagnoses, and elements of antenatal care by age, wealth, educational attainment, and gravidity. Results: Among 4,692 respondents, 97% reported having been screened and 10% of screened women reported a diagnosis of hypertension. Women 30–39 years of age (aOR 3.02, 95% CI 2.00, 4.56) or in the top wealth quintile (aOR 1.70, 95% CI 1.18, 2.44) were more likely to be diagnosed with hypertension compared to reference groups. Any hypertension diagnosis was associated with reporting four or more antenatal care contacts (44% vs. 35%, p < 0.01), blood pressure measurements (85% vs. 79%, p < 0.01), and urine (71% vs. 61%, p < 0.01) tests conducted during antenatal care visits. For diabetes, 46% of respondents reported having been screened and 3% of screened women reported a diagnosis. Women 30–39 years of age were more likely to be diagnosed with diabetes (aOR 8.19, 95% CI 1.74, 38.48) compared to the reference group. Any diabetes diagnosis was associate with reporting four or more antenatal care contacts (48% vs. 36%, p = 0.04) and having blood testing during pregnancy (83% vs. 66%, p < 0.01). However, the frequency and quality of antenatal care was below the national guidelines among all groups. Conclusion: Focused efforts to ensure that women receive the recommended number of antenatal care contacts, coupled with improved compliance with antenatal care guidelines (including universal screening for diabetes at 24–28 weeks of pregnancy), would improve awareness of hypertension and diabetes among women in Bangladesh. Health Policy Hypertension Diabetes Pregnancy Bangladesh Antenatal Care Figures Figure 1 Background The prevalence of diabetes and hypertension is rising in low- and middle-income countries. Globally, the number of people living with diabetes has risen from 151 million in 2000 to 463 million in 2019; of these, 79% live in low- or middle-income countries.(1) The number of people living with hypertension has risen from 932 million in 2000 to 1.4 billion in 2010.(2,3) Growing urbanization, changing lifestyle habits, and genetic factors are some of the reasons for this increase in low- and middle-income countries.(1–3) South Asia accounts for 60% of the global diabetes and 23% of the global hypertension burdens, and these health conditions play increasing roles in pregnancy-related morbidity and mortality.(4–9) Hyperglycemia, including pre-existing and gestational diabetes, is estimated to complicate 17% of all pregnancies globally; 9 out of 10 of these cases occur in less developed countries.(5) In underdeveloped health care systems, the risk of perinatal mortality is 2.5–5 times higher for women with pre-existing diabetes, and an estimated 50% of neonates born to women with the condition require admission to intensive care units.(10–13) Hypertensive disorders of pregnancy are estimated to complicate 5–10% of all pregnancies globally and are responsible for an estimated 16% of stillbirths and 10% of all early neonatal deaths.(9,14–16) These two conditions can also create detrimental synergies; for example, mothers with pre-existing diabetes are also at a higher risk of hypertensive disorders during pregnancy, including a nine-times greater risk of developing pre-eclampsia.(6,11,17,18) Diabetes and hypertension are the major causes of morbidity and mortality in Bangladesh, including maternal mortality, 24% of which is attributable to pre-eclampsia or eclampsia.(19–21) From 2011 to 2018, hypertension prevalence increased from 32% to 45% among women over 35 years of age and was estimated to be 12.5% among women 18–34 years of age in 2018.(22) The burden of pregnancy-induced or primary hypertension in pregnancy is less well understood, as is its impact on pregnancy outcomes other than maternal mortality. Similarly, the prevalence of diabetes is also increasing in Bangladesh; from 2011 to 2018, it increased from 12% to 14% among women over 35 years of age and was estimated to be 5% among women 18–34 years of age.(22) An estimated 13% of women in rural Bangladesh develop gestational diabetes mellitus during pregnancy.(23) National guidelines for maternity care in Bangladesh include screening for both hypertension and diabetes as part of routine antenatal care, but the extent to which these services are provided to women in pregnancy is not well-documented.(24) The objective of this study was to describe the self-reported prevalence of screening and diagnoses of diabetes and hypertension among recently pregnant women in a rural area of Bangladesh and the antenatal care received by women with self-reported diabetes and hypertension during their pregnancies. Methods This study was conducted at the Child Health and Mortality Prevention Surveillance (CHAMPS) project site in the Baliakandi sub-district of Bangladesh. CHAMPS Bangladesh began active population-based demographic surveillance in the sub-district on approximately 220,000 people in September 2017.(25,26) From April to August 2019, we conducted a survey of married women of reproductive age to ascertain prior screening for and diagnosis of hypertension and diabetes. All married women of reproductive age living in households with a child (living or dead) under five years of age or pregnant or recently pregnant women were eligible to participate. One week prior to the start of data collection in each block of the demographic surveillance system, a listing of households that met the eligibility criteria was generated using the CHAMPS data on pregnancies and children under five years of age. Data collectors visited each household and conducted face-to-face interviews. Written informed consent was obtained from the woman, or, in the event that the woman was under the age of 18 years, informed assent was taken and witnessed by a guardian from the household. If a woman eligible for participation was not at home during the initial visit, data collectors conducted up to nine follow-up visits to complete the data collection. The cross-sectional survey contained two modules used in this study: maternal hypertension and diabetes, and antenatal care. The data collection tool was based on questionnaires developed by the Demographic and Health Survey (DHS) Program and the WHO STEPwise approach to surveillance.(27,28) The questions were translated into Bengali and validated through prior national surveys.(29–31) Using structured questions, the data collectors asked eligible women about previous screening and diagnoses of diabetes and hypertension, the timings of diagnoses, and having received each of the following antenatal services at least once at any point in their pregnancy: height, weight, and blood pressure measurements; urine tests (unspecified); blood tests (unspecified); calcium supplements; iron supplements; and tetanus toxoid vaccinations. No medical records were available to confirm the self-reported information. Data Analysis Using the demographic surveillance information available as of February 26, 2020, we retrospectively identified pregnancy outcomes that occurred within the 12 months prior to the date of the cross-sectional survey for each respondent. We extracted demographic, socio-economic, and pregnancy history information from the demographic surveillance database for each survey respondent by linking unique identification numbers. Using summary statistics and chi-squared tests, we examined the following variables for each eligible respondent: socio-economic characteristics, including age (< 20, 20–29, 30–39, and 40+ years of age), wealth quintile, and educational attainment (none, primary, secondary, and post-secondary); health history variables, including gravidity (the total number of lifetime pregnancies), diabetes, and hypertension; and care-seeking in pregnancy, including the number of antenatal care visits (0, < 4, 4–8, and 9+) and elements of antenatal care. The wealth quintile was constructed using the DHS wealth index score.(32,33) Based on a literature review of known risk factors for hypertension, hypertensive disorders of pregnancy, diabetes, and hyperglycemia in pregnancy, we controlled for age, wealth quintile, educational attainment, and gravidity as potential confounders in the analysis.(34–40) We used logistic regression to estimate the associations between diabetes or hypertension screening and selected background characteristics and adjusted for known confounders. We then used logistic regression to estimate the associations between diabetes or hypertension diagnoses and selected background characteristics among women who had been previously screened, adjusting for the same confounders. We used chi-squared tests and logistic regression to examine the associations between previous diabetes or hypertension diagnoses and the measured elements of antenatal care, adjusting for the same confounders. All variables were analyzed categorically. A value of p < 0.05 was considered statistically significant for all analyses. Results Among 59,180 married women of reproductive age, we identified 5,314 women with a pregnancy outcome within one year prior to the survey (Figure 1). Of these, 622 women were excluded from the analysis: 87 (2%) were misclassified and no pregnancy information was collected, and 535 (10%) could not be located. It is common in this population for women to relocate to their natal home to give birth and return to their marital home several months later. A total of 4,692 women were included in the analysis (Figure 1). Approximately 46% (2,163 out of 4,692) of respondents reported previously having been screened for diabetes, compared to nearly all having been previously screened for hypertension (97%). Of those screened, 3% (75 out of 2,163) reported previous diagnoses of diabetes, and 10% (434 out of 4,552) reported previous diagnoses of hypertension (Figure 1). Most recently pregnant women (78%) were under 30 years of age, and 35% had recently completed their first pregnancy (Table 1). We observed a prominent generational difference in educational status among the women surveyed: 72% of women over 40 years of age reported primary school completion or lower, while 89% of women under 20 years of age reported secondary school completion or higher. Table 1. Socio-economic and health history characteristics of the study population Characteristic All women N = 4,692 n(%) Age (Years) <20 925 (20%) 20–29 2,707 (58%) 30–39 1,021 (22%) 40–49 39 (1%) Education (Completed) None 202 (4%) Primary 873 (19%) Secondary 2,406 (51%) Post-Secondary 1,211 (26%) Household Wealth Quintile Lowest 909 (19%) Second 921 (20%) Middle 949 (20%) Fourth 981 (21%) Highest 932 (20%) Gravidity (the total number of lifetime pregnancies) 1 1,651 (35%) 2–4 2,765 (59%) 5+ 276 (6%) Characteristics associated with diabetes and hypertension screening In crude and adjusted analyses, young women and women with no education had the lowest odds of ever having been screened for diabetes compared to other groups (Table 2). After adjusting for age, education, wealth, and gravidity, primigravid or multigravida (5+) women below the fourth wealth quintile had significantly lower odds of ever having been screened for diabetes compared to wealthier women and women reporting a lifetime total of 2–4 pregnancies (Table 2). All women over 40 years of age reported having previously been screened for hypertension at least once in their lives. Women who had completed post-secondary education were two-fold more likely to report having been previously screened for hypertension (OR 2.21, 95% CI 1.02, 4.81), and this association strengthened after controlling for age, wealth, and gravidity (aOR 2.46, 95% CI 1.06, 5.70) (Table 1). Among the respondents, 3% (140 of 4,692) reported having never been screened for hypertension; 41% (57 out of 140) were primigravida. Overall, having never been screened was associated with having very little interaction with the health care system during pregnancy; 46% (65 out of 140) reported either receiving no antenatal care or having an ultrasound as their only antenatal care during pregnancy. Diabetes and hypertension diagnoses While higher educational attainment and increased wealth were associated with an increased likelihood of ever having been screened for diabetes (Table 3), these characteristics were not associated with higher odds of reporting a diagnosis of diabetes in adjusted analyses (Table 3). Membership in the highest wealth quintile (aOR 1.70, 95% CI 1.18, 2.44) was the only statistically significant socio-economic factor associated with increased risk for hypertension in fully adjusted analyses. Table 2. Association between diabetes and hypertension screening and selected background characteristics using logistic regression Total number screened/not screened Diabetes Hypertension 2,163/2,529 4,552/140 %^ Crude Adjusted %^ Crude Adjusted OR 95% CI aOR 95% CI OR 95% CI aOR 95% CI Age (Years) < 20 38 Ref Ref 96 Ref Ref 20–29 46 1.39* (1.19, 1.62) 1.23* (1.03, 1.47) 97 1.71* (1.14, 2.55) 1.49 (0.92, 2.42) 30–39 52 1.79* (1.49, 2.15) 1.80* (1.44, 2.27) 97 1.32 (0.82, 2.11) 1.33 (0.70, 2.51) 40–49 59 2.33* (1.21, 4.47) 2.93* (1.44, 5.92) 100 -- -- -- -- Education (Completed) None 32 Ref Ref 95 Ref Ref Primary 41 1.53* (1.10, 2.11) 1.71* (1.23, 2.39) 96 1.15 (0.54, 2.44) 1.24 (0.58, 2.64) Secondary 44 1.72* (1.27, 2.34) 2.15* (1.56, 2.97) 97 1.51 (0.74, 3.07) 1.79 (0.85, 3.74) Post-Secondary 55 2.66* (1.94, 3.66) 3.27* (2.32, 4.60) 98 2.21* (1.02, 4.81) 2.46* (1.06, 5.70) Household Wealth Quintile Lowest 40 Ref Ref 97 Ref Ref Second 44 1.15 (0.95, 1.38) 1.16 (0.96, 1.33) 97 0.91 (0.54, 1.54) 0.92 (0.54, 1.56) Middle 44 1.15 (0.95, 1.38) 1.10 (0.91, 1.33) 96 0.80 (0.48, 1.33) 0.74 (0.44, 1.24) Fourth 48 1.37* (1.15, 1.65) 1.26* (1.04, 1.52) 97 1.08 (0.63, 1.86) 0.97 (0.56, 1.69) Highest 54 1.75* (1.45, 2.10) 1.38* (1.14, 1.69) 98 1.33 (0.74, 2.37) 1.04 (0.57, 1.91) Gravidity 1 43 Ref Ref 96 Ref Ref 2–4 48 1.22* (1.08, 1.38) 1.20* (1.03, 1.40) 97 1.34 (0.94, 1.90) 1.32 (0.83, 2.09) 5+ 48 1.24* (0.96, 1.60) 1.16 (0.86, 1.57) 96 0.86 (0.45, 1.67) 0.93 (0.41, 2.10) Adjusted model includes age, education, wealth, and gravidity. *Denotes significance at the p < 0.05 level ^Percent screened out of the total number of women in the category. Table 3. Association between diabetes and hypertension diagnoses and selected background characteristics using logistic regression Total number diagnosed/not diagnosed Diabetes Hypertension 75/2,140 434/4,552 %^ Crude Adjusted %^ Crude Adjusted OR 95% CI aOR 95% CI OR 95% CI aOR 95% CI Age (Years) < 20 0.6 Ref Ref 5.4 Ref Ref 20–29 2.7 4.90* (1.17,20.51) 3.79 (0.84,17.02) 8.2 1.56* (1.13,2.15) 1.44* (1.01,2.08) 30–39 7.3 13.77* (3.30,57.41) 8.19* (1.74,38.48) 16.4 3.42* (2.45,4.79) 3.02* (2.00,4.56) 40–49 0.0 - -- - -- 20.5 4.50* (1.97,10.33) 3.37* (1.36,8.31) Education (Completed) None 7.8 Ref Ref 12.4 Ref Ref Primary 4.4 0.55 (0.19,1.55) 0.67 (0.23,1.92) 10.2 0.80 (0.50,1.30) 0.94 (0.58,1.55) Secondary 3.6 0.45 (0.17,1.78) 0.69 (0.26,1.89) 8.9 0.68 (0.44,1.08) 0.99 (0.62,1.60) Post-Secondary 2.2 0.27 (0.17,1.18) 0.44 (0.14,1.38) 9.9 0.77 (0.48,1.23) 1.08 (0.65,1.82) Household Wealth Quintile Lowest 3.8 Ref Ref 7.7 Ref Ref Second 3.5 0.91 (0.43,1.93) 0.96 (0.45,2.07) 9.7 1.28 (0.92,1.78) 1.35 (0.96,1.89) Middle 2.9 0.75 (0.34,1.65) 0.83 (0.37,1.83) 8.7 1.15 (0.82,1.61) 1.22 (0.86,1.71) Fourth 2.7 0.71 (0.33,1.53) 0.91 (0.41,2.01) 8.9 1.17 (0.84,1.63) 1.26 (0.89,1.77) Highest 4.4 1.15 (0.58,2.28) 1.51 (0.72,3.17) 12.6 1.73* (1.26,2.37) 1.73* (1.24,2.43) Gravidity 1 1.3 Ref Ref 7.0 Ref Ref 2–4 4.2 3.36* (1.65,6.85) 1.66 (0.74,3.69) 10.1 1.49* (1.19,1.88) 1.09 (0.82,1.44) 5+ 8.3 6.99* (2.84,17.23) 2.40 (0.84,6.91) 18.5 3.00* (2.08,4.32) 1.56 (0.99,2.44) Adjusted model includes age, education, wealth, and gravidity. ^Percent diagnosed out of total number of women ever screened in the category. * denotes significance at the p<0.05 level In the fully adjusted analyses, a higher age was significantly associated with higher odds of diagnoses of both hypertension and diabetes compared to a lower age of < 20 years. Women 30–39 years of age had significantly higher odds of hypertension (aOR 3.02, 95% CI 2.00, 4.56) and diabetes (aOR 8.19, 95% CI 1.74, 38.48) diagnoses compared to women under 20 years of age (Table 3). Among the 39 recently pregnant women over 40 years of age (Table 1), 23 (59%) had ever been screened for diabetes, and none reported a history of diabetes diagnosis. Women over 40 years of age had the highest odds of hypertension diagnosis (aOR 3.37, 95% CI 1.36, 8.31) compared to women under 20 years of age. The number of total lifetime pregnancies was not associated with higher odds of hypertension or diabetes diagnoses in the adjusted analyses (Table 3). Antenatal care services among women with diabetes and hypertension Among women with reported diabetes diagnoses, 53% (40 out of 75) occurred before and 47% (35 out of 75) occurred during or after the index pregnancy (Table 4). Women with any diabetes diagnosis were more likely to have four or more antenatal care contacts compared to women who were never diagnosed (48% vs. 36%, p = 0.04). Women with any diabetes diagnosis were significantly more likely to report having blood tests during antenatal care compared to women who were never diagnosed (83% vs. 66%, p < 0.01) (Table 4). A greater proportion of women with any diabetes diagnosis reported receiving calcium and iron folate supplements, any urine test, and having their weight and blood pressure measured compared to women who have never been diagnosed, but these differences were not statistically significant (Table 4). Among women with any diabetes diagnosis, 17% (13 out of 75) received all seven measured elements of antenatal care, including 15% (8 out of 53) of women diagnosed with diabetes prior to the index pregnancy. Table 4. Timing of diagnoses of diabetes or hypertension and antenatal care services Diabetes P-value Hypertension P-value Ever diagnosed No Yes No Yes 4,617 75 4,258 434 n(%) n(%) n(%) n(%) Timing of diagnosis Before pregnancy 40 (53) 158 (36) During pregnancy 29 (38) 195 (45) After pregnancy 6 (8) 81 (19) Number of antenatal care contacts 0.04* <0.01* None 535 (12) 6 (8) 502 (12) 39 (9) < 4 2,427 (53) 33 (44) 2,257 (53) 203 (47) 4–8 1,507 (33) 30 (40) 1,360 (32) 177 (41) 9+ 148 (3) 6 (8) 139 (3) 15 (3) Elements of antenatal care Weight Taken 3,486 (76) 63 (84) 0.09 3,206 (75) 343 (79) 0.08 Blood Pressure Taken 3,648 (79) 65 (87) 0.11 3,343 (79) 370 (85) <0.01* Any Urine Test 2,857 (62) 51 (68) 0.28 2,599 (61) 309 (71) <0.01* Any Blood Test 3,070 (66) 62 (83) <0.01* 2,814 (66) 318 (73) <0.01* Tetanus Toxoid Vaccine 2,092 (45) 27 (36) 0.27 1,946 (46) 173 (40) 0.05 Any Iron Folate Supplement 3,202 (69) 55 (73) 0.75 2,944 (69) 313 (72) 0.42 Calcium Supplement 3,107 (67) 54 (72) 0.68 2,863 (67) 298 (69) 0.29 Received all seven measured elements of care 1051 (23) 13 (17) 0.27 97 (22) 97 (22) 0.86 The p-value compares women ever diagnosed to women never diagnosed with the disease. * denotes significance at the p < 0.05 level Among women with reported hypertension diagnoses, 36% (158 out of 434) occurred before and 64% (276 out of 434) occurred during or after the index pregnancy (Table 4). Women with any hypertension diagnosis were more likely to have four or more antenatal care contacts compared to women who have never been diagnosed (44% vs. 35%, p < 0.01). Women with any hypertension diagnosis were significantly more likely to receive calcium supplements and tetanus toxoid vaccination, report having their weight and blood pressure measured, and have any blood test during pregnancy compared to women who have never been diagnosed (Table 4). Among women with any hypertension diagnosis, 22% (97 out of 434) received all seven measured elements of antenatal care, including 15% (28 out of 158) of women diagnosed with hypertension prior to the index pregnancy. Among the respondents, 8% of diabetes and 19% of hypertension diagnoses occurred after delivery, indicating that the disease may not have been identified through routine antenatal care in pregnancy. Despite more antenatal contacts during pregnancy, after adjusting for age, education, wealth, and the total number of lifetime pregnancies, women with any diagnosis of diabetes (aOR 0.87, 95% CI 0.47, 1.60) or hypertension (aOR 1.05, 95% CI 0.82, 1.34) were no more likely to receive all seven measured elements of antenatal care services at least once in their pregnancy compared to never diagnosed women. Discussion Nearly all the women in this study (97%) reported ever having been screened for hypertension, and nearly half (46%) reported ever having been screened for diabetes. Per the national guidelines for antenatal care, all study respondents should have been screened for hypertension, with a medical history of hypertension and diabetes taken at their first antenatal care visit followed by blood pressure readings at each subsequent visit and a blood glucose test between 24 and 28 weeks of pregnancy.( 41 ) Among women in this study, 79% had their blood pressure checked at an antenatal care visit, and 67% reported having some type of blood test as part of their antenatal care during the index pregnancy. These rates of screening are higher than in other national surveys, which could suggest that antenatal care is a primary source of hypertension and diabetes screening for women in rural Bangladesh.( 22 , 29 , 31 ) Our findings suggest that diabetes screening may be offered selectively, based on risk factors (such as age), patient advocacy, or the choice of facility, depending on the patient’s socio-economic status. This hypothesis merits further research. Nearly 9 out of 10 women interacted with the health system at least once to receive antenatal care during pregnancy, but our findings suggest that antenatal care is not being provided according to the national guidelines. The results of our study were consistent with previous studies in which higher age was significantly associated with diabetes or hypertension diagnoses.( 23 , 34 , 35 ) Previous research on the associations between hypertension or diabetes and educational attainment has yielded mixed findings.( 23 , 30 , 34 , 37 , 42 ) Our findings of no associations between educational attainment and hypertension or diabetes diagnoses contribute to the body of research attempting to better understand the nature of these relationships. Higher wealth has been consistently associated with hypertension and diabetes in previous studies.( 23 , 34 , 35 ) Our findings support these previous estimates for hypertension, but not for diabetes. The most plausible explanation for this inconsistency is that this study focuses on a sub-population for whom the nature of the disease is different. While we asked the women in our study population about the timing of their diagnosis, we did not ask if their previous diagnosis was during a prior pregnancy. If the primary source of diabetes and hypertension screening for these women is antenatal care, then many of the diagnosed cases reported in this study may be transient gestational disease as opposed to chronic underlying conditions. There are several limitations to this study. First, the absence of direct measurement or medical records to confirm self-reported information, coupled with low and unrepresentative screening coverage for diabetes, resulted in a high likelihood of misclassification of the women by disease status. Comparing the national data to our findings, up to half of the women with diabetes could have been misclassified in our study.( 22 ) However, this study complements other studies that have estimated point prevalence with additional understanding about access to screening services and elements of antenatal care for women with known disease in rural contexts.( 29 , 31 , 35 ) Second, women were asked about the timing of any diagnosis of hypertension or diabetes, but no further questions were asked to differentiate between gestationally and non-gestationally induced disease diagnoses. Self-reported diabetes diagnoses among respondents could have been due to gestational diabetes, type 1 diabetes, or type 2 diabetes. Self-reported hypertension diagnoses could have been associated with pregnancy-related hypertensive disorders like preeclampsia and gestational hypertension. Additional questions, if included in the survey, would have been subject to the same misclassification biases described above. From our experience and findings with this study, we conclude that differentiating between gestational and non-gestational diseases in this context would require a prospective study design. Third, the low rates of reported diabetes diagnoses made it difficult to detect differences in risk through our study. For example, diabetes disease prevalence ranged from 7.8% among women with no education to 2.2% among women with post-secondary education, suggesting a possible higher risk among women with no education, but that was not statistically significant in the analysis. Conclusion Health care system constraints are a global challenge in addressing the burden of diabetes and hypertension.(43–45) This study contributes to the global evidence base on the burden of diabetes and hypertension among childbearing women in low-income countries. Antenatal care provides an important opportunity for hypertension and diabetes screening among childbearing women. Focused efforts to ensure that women receive the recommended number of antenatal care contacts, coupled with improved compliance with antenatal care guidelines (including universal screening for diabetes at 24–28 weeks of pregnancy), would improve awareness of these diseases among women in their childbearing years in Bangladesh. List Of Abbreviations aOR Adjusted Odds Ratio CHAMPS Child Health and Mortality Prevention Surveillance CI Confidence Interval DHS Demographic and Health Survey GDM Gestational Diabetes Mellitus OR Odds Ratio STEPS STEPwise approach to Surveillance WHO World Health Organization Declarations Ethics approval and consent to participate All methods were carried out in accordance with relevant guidelines and regulations. Ethical approval was provided by the Ethical Review Committee of the International Centre for Diarrheal Disease Research, Bangladesh on April 24, 2019 under protocol number PR-19023. Written informed consent was obtained from the woman interviewed, or, in the event that the woman was under the age of 18 years, informed assent was taken and witnessed by a guardian from the household. Consent for publication Not applicable Availability of data and materials The datasets analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research was funded by the Bill and Melinda Gates Foundation as a part of the CHAMPS project. Authors' contributions APB, WB, JK and ESG conceptualized and designed the study. AC, SB, SEA and ESG oversaw data collection. KHL and APB curated the data. APB conducted data analysis and prepared the first draft. APB, WB, JK, and ESG participated in critical revision of the manuscript and contributed to its intellectual improvement. All authors read and approved the final manuscript. Acknowledgements The study team is grateful to the Bill and Melinda Gates Foundation for their support to the CHAMPS study in Bangladesh which provided a strong foundation for this data collection. We are also grateful to the CHAMPS study team for their work in the data collection. References IDF Diabetes Atlas, 9th edition [Internet]. Brussels, Belgium; 2019 [cited 2021 Feb 5]. Available from: https://www.diabetesatlas.org/upload/resources/material/20200302_133351_IDFATLAS9e-final-web.pdf Mills KT, Bundy JD, Kelly TN, Reed JE, Kearney PM, Reynolds K, et al. Global disparities of hypertension prevalence and control. Circulation [Internet]. 2016 Aug 9 [cited 2020 Aug 30];134(6):441–50. Available from: /pmc/articles/PMC4979614/?report=abstract Mills KT, Stefanescu A, He J. The global epidemiology of hypertension [Internet]. Vol. 16, Nature Reviews Nephrology. Nature Research; 2020 [cited 2021 Feb 5]. p. 223–37. Available from: www.nature.com/nrneph Zhou B, Bentham J, Di Cesare M, Bixby H, Danaei G, Cowan MJ, et al. Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19.1 million participants NCD Risk Factor Collaboration (NCD-RisC). Lancet [Internet]. 2017 [cited 2020 Aug 30];389:37–55. Available from: http://dx.doi.org/10.1016/ Guariguata L, Linnenkamp U, Beagley J, Whiting DR, Cho NH. Global estimates of the prevalence of hyperglycaemia in pregnancy. Diabetes Res Clin Pract [Internet]. 2014 [cited 2017 Nov 24];103:176–85. Available from: http://dx.doi.org/10.1016/j.diabres.2013.11.003 Diagnostic Criteria and Classification of Hyperglycaemia First Detected in Pregnancy [Internet]. Geneva; 2013 [cited 2017 Nov 24]. Available from: http://apps.who.int/iris/bitstream/10665/85975/1/WHO_NMH_MND_13.2_eng.pdf Rahman LA, Hairi NN, Salleh N. Association Between Pregnancy Induced Hypertension and Low Birth Weight; A Population Based Case-Control Study. Asia-Pacific J Public Heal [Internet]. 2008 [cited 2018 Feb 9];20(2):152–8. Available from: http://aph.sagepub.com Noctor E, Dunne FP. Type 2 diabetes after gestational diabetes: The influence of changing diagnostic criteria. World J Diabetes [Internet]. 2015 Mar 15 [cited 2017 Dec 1];6(2):234–44. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25789105 Magee LA, Pels A, Helewa M, Rey E, von Dadelszen P, Audibert F, et al. Diagnosis, evaluation, and management of the hypertensive disorders of pregnancy. Pregnancy Hypertens An Int J Women’s Cardiovasc Heal [Internet]. 2014 Apr 1 [cited 2017 Oct 23];4(2):105–45. Available from: http://dx.doi.org/10.1016/j.preghy.2014.01.003 Allnutt KJ, Allan CA, Brown J. Early pregnancy screening for identification of undiagnosed pre-existing diabetes to improve maternal and infant health. Cochrane Database Syst Rev [Internet]. 2015 [cited 2018 Mar 11]; Available from: http://doi.wiley.com/10.1002/14651858.CD011601 Cundy T, Gamble G, Townend K, Henley PG, MacPherson P, Roberts AB. Perinatal mortality in Type 2 diabetes mellitus. Diabet Med [Internet]. 2000 Jan 1 [cited 2018 Mar 11];17(1):33–9. Available from: http://doi.wiley.com/10.1046/j.1464-5491.2000.00215.x Sacks DA, Black MH, Li X, Montoro MN, Lawrence JM. Adverse Pregnancy Outcomes Using The International Association of the Diabetes and Pregnancy Study Groups Criteria. Obstet Gynecol [Internet]. 2015 Jul [cited 2017 Nov 24];126(1):67–73. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26241258 Confidential Enquiry into Maternal and Child Health. Diabetes in Pregnancy: Are we providing the best care? Findings of a National Enquiry: England, Wales and Northern Ireland [Internet]. London; 2007 [cited 2017 Nov 25]. Available from: https://www.publichealth.hscni.net/sites/default/files/Diabetes in Pregnancy - executive summary.pdf Vogel JP, Souza JPJ, Mori R, Morisaki N, Lumbiganon P, Laopaiboon M, et al. Maternal complications and perinatal mortality: findings of the World Health Organization Multicountry Survey on Maternal and Newborn Health. BJOG An Int J Obstet Gynaecol [Internet]. 2014 Mar [cited 2018 Dec 1];121:76–88. Available from: https://pubmed.ncbi.nlm.nih.gov/24641538/ von Dadelszen P, Magee LA. Preventing deaths due to the hypertensive disorders of pregnancy. Best Pract Res Clin Obstet Gynaecol [Internet]. 2016 Oct 1 [cited 2017 Oct 23];36:83–102. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5096310/ Hutcheon JA, Fellow P-D, Lisonkova S, Joseph KS. Epidemiology of pre-eclampsia and the other hypertensive disorders of pregnancy. Best Pract Res Clin Obstet Gynaecol [Internet]. 2011 Aug 1 [cited 2017 Nov 26];25(4):391–403. Available from: http://www.sciencedirect.com.ezp.welch.jhmi.edu/science/article/pii/S1521693411000198?via%3Dihub Evers IM, de Valk HW, Visser GHA. Risk of complications of pregnancy in women with type 1 diabetes: nationwide prospective study in the Netherlands. BMJ [Internet]. 2004 Apr 17 [cited 2017 Nov 25];328(7445):915–0. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15066886 Negrato CA, Mattar R, Gomes MB. Adverse pregnancy outcomes in women with diabetes. Diabetol Metab Syndr [Internet]. 2012 Sep 11 [cited 2017 Sep 21];4(1):41. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22964143 Saquib N, Saquib J, Ahmed T, Khanam MA, Cullen MR. Cardiovascular diseases and Type 2 Diabetes in Bangladesh: A systematic review and meta-analysis of studies between 1995 and 2010. BMC Public Health [Internet]. 2012 Dec 13 [cited 2017 Dec 4];12(1):434. Available from: http://bmcpublichealth.biomedcentral.com/articles/ 10.1186/1471-2458-12-434 National Institute of Population Research and Training (NIPORT), MEASURE Evaluation, and icddr b. Bangladesh Maternal Mortality and Health Care Survey 2010 [Internet]. Dhaka, Bangladesh; 2012 [cited 2017 Jun 5]. Available from: https://www.measureevaluation.org/resources/publications/tr-12-87 MEASURE Evaluation, National Institute of Population Research and Training, International Centre for Diarrhoeal Disease Research, Bangladesh and ME. Bangladesh: Maternal Mortality and Health Care Survey 2016: Preliminary Report. Dhaka, Bangladesh and Chapel Hill, North Carolina, USA; 2017. National Institute of Population Research and Training (NIPORT), Mitra and Associates and II. Bangladesh Demographic and Health Survey 2017-18: Key Indicators. Dhaka, Bangladesh, and Rockville, Maryland, USA; 2019. Jesmin S, Akter S, Akashi H, Al-Mamun A, Rahman MA, Islam MM, et al. Screening for gestational diabetes mellitus and its prevalence in Bangladesh. Diabetes Res Clin Pract [Internet]. 2014 Jan 1 [cited 2017 Nov 18];103(1):57–62. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24369985 Standard Operating Procedures, Maternal and Newborn Health [Internet]. Standard Operating Procedures, Maternal and Newborn Health. Dhaka; [cited 2020 Aug 22]. Available from: https://drive.google.com/file/d/0B4bW0fmAqJeHRTRmV 0R3b292cjQ/view Cunningham SA, Shaikh NI, Nhacolo A, Raghunathan PL, Kotloff K, Naser AM, et al. Health and Demographic Surveillance Systems Within the Child Health and Mortality Prevention Surveillance Network. Clin Infect Dis. 2019 Oct 9;69(4):S274–9. Salzberg NT, Sivalogan K, Bassat Q, Taylor AW, Adedini S, El Arifeen S, et al. Mortality Surveillance Methods to Identify and Characterize Deaths in Child Health and Mortality Prevention Surveillance Network Sites. Clin Infect Dis. 2019 Oct 9;69(4):S262–73. Riley L, Guthold R, Cowan M, Savin S, Bhatti L, Armstrong T, et al. The World Health Organization STEPwise Approach to Noncommunicable Disease Risk-Factor Surveillance: Methods, Challenges, and Opportunities. Am J Public Health [Internet]. 2016 Jan [cited 2018 Oct 30];106(1):74–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26696288 ICF. DHS Model Questionnaire - Phase 7 [Internet]. Rockville, Maryland; 2015 [cited 2020 Feb 23]. Available from: https://dhsprogram.com/publications/publication-dhsq7-dhs-questionnaires-and-manuals.cfm World Health Organization. Non-Communicable Disease Risk Factor Survey Bangladesh 2010 [Internet]. Dhaka, Bangladesh: World Health Organization, South-East Asia Regional Office; 2011 [cited 2018 Jan 10]. Available from: http://www.searo.who.int/bangladesh/publications/ncd_risk_factor_2010/en/ National Institute of Population Research and Training (NIPORT), Mitra and Associates and II. Bangladesh Demographic and Health Survey 2014 [Internet]. Dhaka, Bangladesh, and Rockville, Maryland, USA; 2016 [cited 2017 Sep 24]. Available from: https://dhsprogram.com/pubs/pdf/FR311/FR311.pdf National Institute of Population Research and Training (NIPORT), Mitra and Associates and II. Bangladesh Demographic and Health Survey 2011 [Internet]. Dhaka, Bangladesh, and Rockville, Maryland; 2013 [cited 2017 Sep 24]. Available from: https://dhsprogram.com/pubs/pdf/fr265/fr265.pdf Rutstein SO. The DHS Wealth Index: Approaches for Rural and Urban Areas [Internet]. Rockville, Maryland; 2008 Oct [cited 2020 Dec 24]. Available from: Rutstein SO, Johnson K. The DHS wealth index. DHS Comparative Reports No. 6. [Internet]. Calverton, Maryland; 2004 Aug [cited 2020 Dec 24]. Available from: http://www.measuredhs.comorbycontacting Tareque MI, Koshio A, Tiedt AD, Hasegawa T. Are the rates of hypertension and diabetes higher in people from lower socioeconomic status in Bangladesh? Results from a nationally representative survey. Ciccozzi M, editor. PLoS One [Internet]. 2015 May 27 [cited 2017 Nov 18];10(5):e0127954. Available from: http://dx.plos.org/10.1371/journal.pone.0127954 Fottrell E, Ahmed N, Shaha SK, Jennings H, Kuddus A, Morrison J, et al. Distribution of diabetes, hypertension and non-communicable disease risk factors among adults in rural Bangladesh: a cross-sectional survey. BMJ Glob Heal [Internet]. 2018 [cited 2020 Aug 30];3:787. Available from: http://gh.bmj.com/ Akter S, Jesmin S, Rahman MMMM, Islam MM, Khatun MT, Yamaguchi N, et al. Higher Gravidity and Parity Are Associated with Increased Prevalence of Metabolic Syndrome among Rural Bangladeshi Women. PLoS One [Internet]. 2013 Aug 2 [cited 2017 Nov 18];8(8):e68319. Available from: http://dx.plos.org/10.1371/journal.pone.0068319 Bishwajit G, Yaya S, Seydou I. Diabetes mellitus and high blood pressure in relation to BMI among adult non-pregnant women in Bangladesh. Diabetes Metab Syndr Clin Res Rev [Internet]. 2016 Dec 13 [cited 2017 Nov 18];11:S217–21. Available from: https://doi.org/10.1016/j.dsx.2016.12.034 Shih YH, Scannell Bryan M, Parvez F, Uesugi KH, Shahriar M, Ahmed A, et al. Gravidity, parity, blood pressure and mortality among women in Bangladesh from the HEALS cohort. BMJ Open [Internet]. 2020 Aug 26 [cited 2021 Jan 1];10(8). Available from: /pmc/articles/PMC7451482/?report=abstract Stringhini S, Tabak AG, Akbaraly TN, Sabia S, Shipley MJ, Marmot MG, et al. Contribution of modifiable risk factors to social inequalities in type 2 diabetes: Prospective Whitehall II cohort study. BMJ [Internet]. 2012 Sep 22 [cited 2021 Jan 1];345(7875). Available from: http://www.bmj.com/ Jang M, Lee Y, Choi J, Kim B, Kang J, Kim Y, et al. Association between parity and blood pressure in Korean women: Korean National Health and Nutrition Examination Survey, 2010-2012. Korean J Fam Med. 2015;36(6):341–8. Maternal and Neonatal Health Standard Operating Procedures [Internet]. Dhaka, Bangladesh; 2017 [cited 2018 Sep 27]. Available from: https://drive.google.com/file/d/0B4bW0fmAqJeHRTRmV 0R3b292cjQ/view National Institute of Population Research and Training (NIPORT) and ICF. Bangladesh Health Facility Survey 2017 [Internet]. Dhaka, Bangladesh; 2019 [cited 2021 Jan 2]. Available from: https://dhsprogram.com/pubs/pdf/SPA28/SPA28.pdf Moucheraud C. Service Readiness For Noncommunicable Diseases was Low in Five Countries in 2013-15. Glob Heal Policy [Internet]. 2018 [cited 2018 Dec 6];37(8):1321–30. Available from: https://www-healthaffairs-org-ezp-welch-jhmi-edu.proxy1.library.jhu.edu/doi/pdf/ 10.1377/hlthaff.2018.0151 Hogerzeil H V, Liberman J, Wirtz VJ, Kishore SP, Selvaraj S, Kiddell-Monroe R, et al. Promotion of access to essential medicines for non-communicable diseases: practical implications of the UN political declaration. Lancet [Internet]. 2013 [cited 2018 Dec 6];381:680–9. Available from: http://dx.doi.org/10.1016/ Daar AS, Singer PA, Leah Persad D, Pramming SK, Matthews DR, Beaglehole R, et al. Grand challenges in chronic non-communicable diseases. Nature [Internet]. 2007 Nov 22 [cited 2018 Dec 6];450(7169):494–6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18033288 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-964052","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":59503050,"identity":"66729da0-7276-4a1b-89ef-49f5103a2025","order_by":0,"name":"Allyson P. Bear","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYDACCRBRwZYAZxOp5QzJWhjbGEjQwj+7+eDnynl8eQYHmA/e5iHKkjvHkiXPbmMrNjjAlmxNlBaGGzkGko3b2BI3HOAxkyZKi/yN/M8/G+eAtPB/I06LwY0cNsnGBrAtbMRpMbxzzMyy4Rhb4szDbMaWc4jRIne7+fHNhppjiX3Hmx/eeEOMFig4xsDATIJyEKghUf0oGAWjYBSMKAAAxmAy+bj9vEIAAAAASUVORK5CYII=","orcid":"","institution":"Johns Hopkins Bloomberg School of Public Health","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Allyson","middleName":"P.","lastName":"Bear","suffix":""},{"id":59503051,"identity":"9510f914-a84e-47ce-92eb-dfd364cfa7fd","order_by":1,"name":"Wendy L. Bennett","email":"","orcid":"","institution":"Johns Hopkins School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wendy","middleName":"L.","lastName":"Bennett","suffix":""},{"id":59503052,"identity":"bb3cdf85-b4c5-4500-9c66-e373c5764e3c","order_by":2,"name":"Joanne Katz","email":"","orcid":"","institution":"Johns Hopkins Bloomberg School of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Joanne","middleName":"","lastName":"Katz","suffix":""},{"id":59503055,"identity":"80c23a76-2b8e-459f-b126-7aafa8e0b21b","order_by":3,"name":"Kyu Han Lee","email":"","orcid":"","institution":"Johns Hopkins Bloomberg School of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kyu","middleName":"Han","lastName":"Lee","suffix":""},{"id":59503053,"identity":"401957d8-9768-4066-80e2-9d9f49fa3a01","order_by":4,"name":"Atique Iqbal Chowdhury","email":"","orcid":"","institution":"International Centre for Diarrhoeal Disease Research","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Atique","middleName":"Iqbal","lastName":"Chowdhury","suffix":""},{"id":59503054,"identity":"e3845e47-c6a9-4f32-8bb2-57f8aad50010","order_by":5,"name":"Sanwarul Bari","email":"","orcid":"","institution":"International Centre for Diarrhoeal Disease Research","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sanwarul","middleName":"","lastName":"Bari","suffix":""},{"id":59503056,"identity":"c170a69f-341a-4445-8bee-d236a868e9ed","order_by":6,"name":"Shams El Arifeen","email":"","orcid":"","institution":"International Centre for Diarrhoeal Disease Research","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shams","middleName":"El","lastName":"Arifeen","suffix":""},{"id":59503057,"identity":"10065860-b707-46b7-a1db-0d288c6d7cda","order_by":7,"name":"Emily S. Gurley","email":"","orcid":"","institution":"Johns Hopkins Bloomberg School of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Emily","middleName":"S.","lastName":"Gurley","suffix":""}],"badges":[],"createdAt":"2021-10-11 18:29:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-964052/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-964052/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":15160970,"identity":"680109b4-4a3b-45d9-924f-a1bb1cc68abe","added_by":"auto","created_at":"2021-11-02 20:24:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89659,"visible":true,"origin":"","legend":"Analytic cohort of women in study, Baliakandi, Bangladesh, 2019","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-964052/v1/7296c90b1faa65c04ed39251.png"},{"id":16135505,"identity":"d49bbb07-3058-4eff-bb2d-05403a62c843","added_by":"auto","created_at":"2021-12-03 08:59:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":450812,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-964052/v1/d6ae9987-5fde-4570-9683-722d022eb38d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSelf-Reported Diabetes Or Hypertension Diagnoses And Antenatal Care Among Child-Bearing Women In Rural Bangladesh: A Cross-Sectional Study\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eThe prevalence of diabetes and hypertension is rising in low- and middle-income countries. Globally, the number of people living with diabetes has risen from 151 million in 2000 to 463 million in 2019; of these, 79% live in low- or middle-income countries.(1) The number of people living with hypertension has risen from 932 million in 2000 to 1.4 billion in 2010.(2,3) Growing urbanization, changing lifestyle habits, and genetic factors are some of the reasons for this increase in low- and middle-income countries.(1\u0026ndash;3) South Asia accounts for 60% of the global diabetes and 23% of the global hypertension burdens, and these health conditions play increasing roles in pregnancy-related morbidity and mortality.(4\u0026ndash;9) Hyperglycemia, including pre-existing and gestational diabetes, is estimated to complicate 17% of all pregnancies globally; 9 out of 10 of these cases occur in less developed countries.(5) In underdeveloped health care systems, the risk of perinatal mortality is 2.5\u0026ndash;5 times higher for women with pre-existing diabetes, and an estimated 50% of neonates born to women with the condition require admission to intensive care units.(10\u0026ndash;13) Hypertensive disorders of pregnancy are estimated to complicate 5\u0026ndash;10% of all pregnancies globally and are responsible for an estimated 16% of stillbirths and 10% of all early neonatal deaths.(9,14\u0026ndash;16) These two conditions can also create detrimental synergies; for example, mothers with pre-existing diabetes are also at a higher risk of hypertensive disorders during pregnancy, including a nine-times greater risk of developing pre-eclampsia.(6,11,17,18)\u003c/p\u003e\n\u003cp\u003eDiabetes and hypertension are the major causes of morbidity and mortality in Bangladesh, including maternal mortality, 24% of which is attributable to pre-eclampsia or eclampsia.(19\u0026ndash;21) From 2011 to 2018, hypertension prevalence increased from 32% to 45% among women over 35 years of age and was estimated to be 12.5% among women 18\u0026ndash;34 years of age in 2018.(22) The burden of pregnancy-induced or primary hypertension in pregnancy is less well understood, as is its impact on pregnancy outcomes other than maternal mortality. Similarly, the prevalence of diabetes is also increasing in Bangladesh; from 2011 to 2018, it increased from 12% to 14% among women over 35 years of age and was estimated to be 5% among women 18\u0026ndash;34 years of age.(22) An estimated 13% of women in rural Bangladesh develop gestational diabetes mellitus during pregnancy.(23) National guidelines for maternity care in Bangladesh include screening for both hypertension and diabetes as part of routine antenatal care, but the extent to which these services are provided to women in pregnancy is not well-documented.(24) The objective of this study was to describe the self-reported prevalence of screening and diagnoses of diabetes and hypertension among recently pregnant women in a rural area of Bangladesh and the antenatal care received by women with self-reported diabetes and hypertension during their pregnancies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study was conducted at the Child Health and Mortality Prevention Surveillance (CHAMPS) project site in the Baliakandi sub-district of Bangladesh. CHAMPS Bangladesh began active population-based demographic surveillance in the sub-district on approximately 220,000 people in September 2017.(25,26)\u003c/p\u003e\n\u003cp\u003eFrom April to August 2019, we conducted a survey of married women of reproductive age to ascertain prior screening for and diagnosis of hypertension and diabetes. All married women of reproductive age living in households with a child (living or dead) under five years of age or pregnant or recently pregnant women were eligible to participate. One week prior to the start of data collection in each block of the demographic surveillance system, a listing of households that met the eligibility criteria was generated using the CHAMPS data on pregnancies and children under five years of age. Data collectors visited each household and conducted face-to-face interviews. Written informed consent was obtained from the woman, or, in the event that the woman was under the age of 18 years, informed assent was taken and witnessed by a guardian from the household. If a woman eligible for participation was not at home during the initial visit, data collectors conducted up to nine follow-up visits to complete the data collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe cross-sectional survey contained two modules used in this study: maternal hypertension and diabetes, and antenatal care. The data collection tool was based on questionnaires developed by the Demographic and Health Survey (DHS) Program and the WHO STEPwise approach to surveillance.(27,28)\u0026nbsp;The questions were translated into Bengali and validated through prior national surveys.(29\u0026ndash;31)\u0026nbsp;Using structured questions, the data collectors asked eligible women about previous screening and diagnoses of diabetes and hypertension,\u0026nbsp;the timings of diagnoses, and having received each of the following antenatal services at least once at any point in their pregnancy: height, weight, and blood pressure measurements; urine tests (unspecified); blood tests (unspecified); calcium supplements; iron supplements; and tetanus toxoid vaccinations. No medical records were available to confirm the self-reported information.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eData Analysis\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eUsing the demographic surveillance information available as of February 26, 2020, we retrospectively identified pregnancy outcomes that occurred within the 12 months prior to the date of the cross-sectional survey for each respondent. We extracted demographic, socio-economic, and pregnancy history information from the demographic surveillance database for each survey respondent by linking unique identification numbers.\u003c/p\u003e\n\u003cp\u003eUsing summary statistics and chi-squared tests, we examined the following variables for each eligible respondent: socio-economic characteristics, including age (\u0026lt; 20, 20\u0026ndash;29, 30\u0026ndash;39, and 40+ years of age), wealth quintile, and educational attainment (none, primary, secondary, and post-secondary); health history variables, including gravidity (the total number of lifetime pregnancies), diabetes, and hypertension; and care-seeking in pregnancy, including the number of antenatal care visits (0, \u0026lt; 4, 4\u0026ndash;8, and 9+) and elements of antenatal care. The wealth quintile was constructed using the DHS wealth index score.(32,33) Based on a literature review of known risk factors for hypertension, hypertensive disorders of pregnancy, diabetes, and hyperglycemia in pregnancy, we controlled for age, wealth quintile, educational attainment, and gravidity as potential confounders in the analysis.(34\u0026ndash;40) We used logistic regression to estimate the associations between diabetes or hypertension screening and selected background characteristics and adjusted for known confounders. We then used logistic regression to estimate the associations between diabetes or hypertension diagnoses and selected background characteristics among women who had been previously screened, adjusting for the same confounders. We used chi-squared tests and logistic regression to examine the associations between previous diabetes or hypertension diagnoses and the measured elements of antenatal care, adjusting for the same confounders. All variables were analyzed categorically. A value of p \u0026lt; 0.05 was considered statistically significant for all analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAmong 59,180 married women of reproductive age, we identified 5,314 women with a pregnancy outcome within one year prior to the survey (Figure 1). Of these, 622 women were excluded from the analysis: 87 (2%) were misclassified and no pregnancy information was collected, and 535 (10%) could not be located. It is common in this population for women to relocate to their natal home to give birth and return to their marital home several months later. A total of 4,692 women were included in the analysis (Figure 1). Approximately 46% (2,163 out of 4,692) of respondents reported previously having been screened for diabetes, compared to nearly all having been previously screened for hypertension (97%). Of those screened, 3% (75 out of 2,163) reported previous diagnoses of diabetes, and 10% (434 out of 4,552) reported previous diagnoses of hypertension (Figure 1).\u003c/p\u003e\n\u003cp\u003eMost recently pregnant women (78%) were under 30 years of age, and 35% had recently completed their first pregnancy (Table 1). We observed a prominent generational difference in educational status among the women surveyed: 72% of women over 40 years of age reported primary school completion or lower, while 89% of women under 20 years of age reported secondary school completion or higher.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;1. Socio-economic and health history characteristics of the study population\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll women\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003eN = 4,692 n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (Years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e925 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003e20\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e2,707 (58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e1,021 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e39 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation (Completed)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e202 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e873 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e2,406 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003ePost-Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e1,211 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Wealth Quintile\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003eLowest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e909 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003eSecond\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e921 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e949 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003eFourth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e981 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003eHighest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e932 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGravidity (the total number of lifetime pregnancies)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e1,651 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003e2\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e2,765 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"62.517680339462515%\"\u003e\n \u003cp\u003e5+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"37.482319660537485%\"\u003e\n \u003cp\u003e276 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cu\u003eCharacteristics associated with diabetes and hypertension screening\u003c/u\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eIn crude and adjusted analyses, young women and women with no education had the lowest odds of ever having been screened for diabetes compared to other groups (Table 2). After adjusting for age, education, wealth, and gravidity, primigravid or multigravida (5+) women below the fourth wealth quintile had significantly lower odds of ever having been screened for diabetes compared to wealthier women and women reporting a lifetime total of 2\u0026ndash;4 pregnancies (Table 2).\u003c/p\u003e\n\u003cp\u003eAll women over 40 years of age reported having previously been screened for hypertension at least once in their lives. Women who had completed post-secondary education were two-fold more likely to report having been previously screened for hypertension (OR 2.21, 95% CI 1.02, 4.81), and this association strengthened after controlling for age, wealth, and gravidity (aOR 2.46, 95% CI 1.06, 5.70) (Table 1). Among the respondents, 3% (140 of 4,692) reported having never been screened for hypertension; 41% (57 out of 140) were primigravida. Overall, having never been screened was associated with having very little interaction with the health care system during pregnancy; 46% (65 out of 140) reported either receiving no antenatal care or having an ultrasound as their only antenatal care during pregnancy.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eDiabetes and hypertension diagnoses\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWhile higher educational attainment and increased wealth were associated with an increased likelihood of ever having been screened for diabetes (Table 3), these characteristics were not associated with higher odds of reporting a diagnosis of diabetes in adjusted analyses (Table 3). Membership in the highest wealth quintile (aOR 1.70, 95% CI 1.18, 2.44) was the only statistically significant socio-economic factor associated with increased risk for hypertension in fully adjusted analyses.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2. Association between diabetes and hypertension screening and selected background characteristics using logistic regression\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal number screened/not screened\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2,163/2,529\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e4,552/140\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e%^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCrude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eAdjusted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e%^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eCrude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eAdjusted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (Years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026lt; 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e20\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.39*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.19, 1.62)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.23*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.03, 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.71*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.14, 2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.92, 2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.79*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.49, 2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.80*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.44, 2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.82, 2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.70, 2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.33*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.21, 4.47)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.93*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.44, 5.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation (Completed)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.53*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.10, 2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.71*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.23, 2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.54, 2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.58, 2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.72*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.27, 2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.15*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.56, 2.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.74, 3.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.85, 3.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePost-Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.66*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.94, 3.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.27*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(2.32, 4.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.21*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.02, 4.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.46*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.06, 5.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Wealth Quintile\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLowest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSecond\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.95, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.96, 1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.54, 1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.54, 1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.95, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.91, 1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.48, 1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.44, 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFourth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.37*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.15, 1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.26*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.04, 1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.63, 1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.56, 1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHighest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.75*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.45, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.38*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.14, 1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.74, 2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.57, 1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGravidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.22*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.08, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.20*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(1.03, 1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.94, 1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.83, 2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.24*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.96, 1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.86, 1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.45, 1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e(0.41, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAdjusted model includes age, education, wealth, and gravidity.\u003c/p\u003e\n\u003cp\u003e*Denotes significance at the p \u0026lt; 0.05 level\u003cbr\u003e\u003cbr\u003e^Percent screened out of the total number of women in the category.\u003c/p\u003e\n\u003cp\u003eTable 3. Association between diabetes and hypertension diagnoses and selected background characteristics using logistic regression\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" width=\"24.687144482366325%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal number diagnosed/not diagnosed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"37.20136518771331%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"38.111490329920365%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"49.39577039274924%\"\u003e\n \u003cp\u003e\u003cstrong\u003e75/2,140\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"50.60422960725076%\"\u003e\n \u003cp\u003e\u003cstrong\u003e434/4,552\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.687144482366325%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.185437997724687%\"\u003e\n \u003cp\u003e%^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"17.747440273037544%\"\u003e\n \u003cp\u003eCrude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"16.26848691695108%\"\u003e\n \u003cp\u003eAdjusted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.09556313993174%\"\u003e\n \u003cp\u003e%^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"16.26848691695108%\"\u003e\n \u003cp\u003eCrude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"17.747440273037544%\"\u003e\n \u003cp\u003eAdjusted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (Years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026lt; 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003e20\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003e4.90*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(1.17,20.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.84,17.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e1.56*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(1.13,2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003e1.44*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e(1.01,2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003e13.77*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(3.30,57.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e8.19*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(1.74,38.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e3.42*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(2.45,4.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003e3.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e(2.00,4.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e4.50*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(1.97,10.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003e3.37*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e(1.36,8.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation (Completed)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.19,1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.23,1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.50,1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e(0.58,1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.17,1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.26,1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.44,1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e(0.62,1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003ePost-Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.17,1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.14,1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.48,1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e(0.65,1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Wealth Quintile\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003eLowest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003eSecond\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.43,1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.45,2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.92,1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e(0.96,1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.34,1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.37,1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.82,1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e(0.86,1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003eFourth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.33,1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.41,2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.84,1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e(0.89,1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003eHighest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.58,2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.72,3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e1.73*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(1.26,2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003e1.73*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e(1.24,2.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGravidity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003e2\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003e3.36*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(1.65,6.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.74,3.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e1.49*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(1.19,1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e(0.82,1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"24.715261958997722%\"\u003e\n \u003cp\u003e5+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"3.1890660592255125%\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.061503416856492%\"\u003e\n \u003cp\u003e6.99*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(2.84,17.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(0.84,6.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"4.100227790432802%\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"5.694760820045558%\"\u003e\n \u003cp\u003e3.00*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.592255125284739%\"\u003e\n \u003cp\u003e(2.08,4.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.378132118451025%\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.389521640091116%\"\u003e\n \u003cp\u003e(0.99,2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAdjusted model includes age, education, wealth, and gravidity. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003c/p\u003e\n\u003cp\u003e^Percent diagnosed out of total number of women ever screened in the category.\u003c/p\u003e\n\u003cp\u003e* denotes significance at the p\u0026lt;0.05 level \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the fully adjusted analyses, a higher age was significantly associated with higher odds of diagnoses of both hypertension and diabetes compared to a lower age of \u0026lt;\u0026nbsp;20 years. Women 30\u0026ndash;39 years of age had significantly higher odds of hypertension (aOR 3.02, 95% CI 2.00,\u0026nbsp;4.56) and diabetes (aOR 8.19, 95% CI 1.74, 38.48) diagnoses compared to women under 20 years of age (Table 3). Among the 39 recently pregnant women over 40 years of age (Table 1), 23 (59%) had ever been screened for diabetes, and none reported a history of diabetes diagnosis. Women over 40 years of age had the highest odds of hypertension diagnosis (aOR 3.37, 95% CI 1.36,\u0026nbsp;8.31) compared to women under 20 years of age. The number of total lifetime pregnancies was not associated with higher odds of hypertension or diabetes diagnoses in the adjusted analyses (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAntenatal care services among women with diabetes and hypertension\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAmong women with reported diabetes diagnoses, 53% (40 out of 75) occurred before and 47% (35 out of 75) occurred during or after the index pregnancy (Table 4). Women with any diabetes diagnosis were more likely to have four or more antenatal care contacts compared to women who were never diagnosed (48% vs. 36%, p = 0.04). Women with any diabetes diagnosis were significantly more likely to report having blood tests during antenatal care compared to women who were never diagnosed (83% vs. 66%, p \u0026lt; 0.01) (Table 4). A greater proportion of women with any diabetes diagnosis reported receiving calcium and iron folate supplements, any urine test, and having their weight and blood pressure measured compared to women who have never been diagnosed, but these differences were not statistically significant (Table 4). Among women with any diabetes diagnosis, 17% (13 out of 75) received all seven measured elements of antenatal care, including 15% (8 out of 53) of women diagnosed with diabetes prior to the index pregnancy.\u003c/p\u003e\n\u003cp id=\"isPasted\"\u003eTable 4. Timing of diagnoses of diabetes or hypertension and antenatal care services\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.147100424328148%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"24.611032531824613%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.881188118811881%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"23.62093352192362%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.73974540311174%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.186968838526912%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEver diagnosed\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.597733711048159%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906515580736544%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.898016997167138%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.747875354107649%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.906515580736544%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e4,617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e4,258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTiming of diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003eBefore pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e40 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e158 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003eDuring pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e29 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e195 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003eAfter pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e6 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e81 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of antenatal care contacts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u0026lt;0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; None\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e535 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e6 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e502 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e39 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e2,427 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e33 (44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e2,257 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e203 (47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;4\u0026ndash;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e1,507 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e30 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e1,360 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e177 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;9+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e148 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e6 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e139 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e15 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003e\u003cstrong\u003eElements of antenatal care\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003eWeight Taken\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e3,486 (76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e63 (84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e3,206 (75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e343 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003eBlood Pressure Taken\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e3,648 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e65 (87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e3,343 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e370 (85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u0026lt;0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003eAny Urine Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e2,857 (62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e51 (68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e2,599 (61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e309 (71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u0026lt;0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003eAny Blood Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e3,070 (66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e62 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e\u0026lt;0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e2,814 (66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e318 (73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e\u0026lt;0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003eTetanus Toxoid Vaccine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e2,092 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e27 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e1,946 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e173 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003eAny Iron Folate Supplement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e3,202 (69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e55 (73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e2,944 (69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e313 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003eCalcium Supplement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e3,107 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e54 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e2,863 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e298 (69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.186968838526912%\"\u003e\n \u003cp\u003eReceived all seven measured elements of care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.597733711048159%\"\u003e\n \u003cp\u003e1051 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e13 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.898016997167138%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.747875354107649%\"\u003e\n \u003cp\u003e97 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.906515580736544%\"\u003e\n \u003cp\u003e97 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.756373937677054%\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe p-value compares women ever diagnosed to women never diagnosed with the disease.\u003c/p\u003e\n\u003cp\u003e* denotes significance at the p \u0026lt; 0.05 level\u003c/p\u003e\n\u003cp\u003eAmong women with reported hypertension diagnoses, 36% (158 out of 434) occurred before and 64% (276 out of 434) occurred during or after the index pregnancy (Table 4). Women with any hypertension diagnosis were more likely to have four or more antenatal care contacts compared to women who have never been diagnosed (44% vs. 35%, p\u0026nbsp;\u0026lt;\u0026nbsp;0.01). Women with any hypertension diagnosis were significantly more likely to receive calcium supplements and tetanus toxoid vaccination, report having their weight and blood pressure measured, and have any blood test during pregnancy compared to women who have never been diagnosed (Table 4). Among women with any hypertension diagnosis, 22% (97 out of 434) received all seven measured elements of antenatal care, including 15% (28 out of 158) of women diagnosed with hypertension prior to the index pregnancy.\u003c/p\u003e\n\u003cp\u003eAmong the respondents, 8% of diabetes and 19% of hypertension diagnoses occurred after delivery, indicating that the disease may not have been identified through routine antenatal care in pregnancy. Despite more antenatal contacts during pregnancy, after adjusting for age, education, wealth, and the total number of lifetime pregnancies, women with any diagnosis of diabetes (aOR 0.87, 95% CI 0.47, 1.60) or hypertension (aOR 1.05, 95% CI 0.82, 1.34) were no more likely to receive all seven measured elements of antenatal care services at least once in their pregnancy compared to never diagnosed women.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eNearly all the women in this study (97%) reported ever having been screened for hypertension, and nearly half (46%) reported ever having been screened for diabetes. Per the national guidelines for antenatal care, all study respondents should have been screened for hypertension, with a medical history of hypertension and diabetes taken at their first antenatal care visit followed by blood pressure readings at each subsequent visit and a blood glucose test between 24 and 28 weeks of pregnancy.(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) Among women in this study, 79% had their blood pressure checked at an antenatal care visit, and 67% reported having some type of blood test as part of their antenatal care during the index pregnancy. These rates of screening are higher than in other national surveys, which could suggest that antenatal care is a primary source of hypertension and diabetes screening for women in rural Bangladesh.(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) Our findings suggest that diabetes screening may be offered selectively, based on risk factors (such as age), patient advocacy, or the choice of facility, depending on the patient\u0026rsquo;s socio-economic status. This hypothesis merits further research. Nearly 9 out of 10 women interacted with the health system at least once to receive antenatal care during pregnancy, but our findings suggest that antenatal care is not being provided according to the national guidelines.\u003c/p\u003e \u003cp\u003eThe results of our study were consistent with previous studies in which higher age was significantly associated with diabetes or hypertension diagnoses.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) Previous research on the associations between hypertension or diabetes and educational attainment has yielded mixed findings.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) Our findings of no associations between educational attainment and hypertension or diabetes diagnoses contribute to the body of research attempting to better understand the nature of these relationships. Higher wealth has been consistently associated with hypertension and diabetes in previous studies.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) Our findings support these previous estimates for hypertension, but not for diabetes. The most plausible explanation for this inconsistency is that this study focuses on a sub-population for whom the nature of the disease is different. While we asked the women in our study population about the timing of their diagnosis, we did not ask if their previous diagnosis was during a prior pregnancy. If the primary source of diabetes and hypertension screening for these women is antenatal care, then many of the diagnosed cases reported in this study may be transient gestational disease as opposed to chronic underlying conditions.\u003c/p\u003e \u003cp\u003eThere are several limitations to this study. First, the absence of direct measurement or medical records to confirm self-reported information, coupled with low and unrepresentative screening coverage for diabetes, resulted in a high likelihood of misclassification of the women by disease status. Comparing the national data to our findings, up to half of the women with diabetes could have been misclassified in our study.(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) However, this study complements other studies that have estimated point prevalence with additional understanding about access to screening services and elements of antenatal care for women with known disease in rural contexts.(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) Second, women were asked about the timing of any diagnosis of hypertension or diabetes, but no further questions were asked to differentiate between gestationally and non-gestationally induced disease diagnoses. Self-reported diabetes diagnoses among respondents could have been due to gestational diabetes, type 1 diabetes, or type 2 diabetes. Self-reported hypertension diagnoses could have been associated with pregnancy-related hypertensive disorders like preeclampsia and gestational hypertension. Additional questions, if included in the survey, would have been subject to the same misclassification biases described above. From our experience and findings with this study, we conclude that differentiating between gestational and non-gestational diseases in this context would require a prospective study design. Third, the low rates of reported diabetes diagnoses made it difficult to detect differences in risk through our study. For example, diabetes disease prevalence ranged from 7.8% among women with no education to 2.2% among women with post-secondary education, suggesting a possible higher risk among women with no education, but that was not statistically significant in the analysis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHealth care system constraints are a global challenge in addressing the burden of diabetes and hypertension.(43\u0026ndash;45) This study contributes to the global evidence base on the burden of diabetes and hypertension among childbearing women in low-income countries. Antenatal care provides an important opportunity for hypertension and diabetes screening among childbearing women. Focused efforts to ensure that women receive the recommended number of antenatal care contacts, coupled with improved compliance with antenatal care guidelines (including universal screening for diabetes at 24\u0026ndash;28 weeks of pregnancy), would improve awareness of these diseases among women in their childbearing years in Bangladesh.\u003c/p\u003e"},{"header":"List Of Abbreviations","content":"\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.254582484725052%\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.74541751527495%\"\u003e\n \u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.254582484725052%\"\u003e\n \u003cp\u003eCHAMPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.74541751527495%\"\u003e\n \u003cp\u003eChild Health and Mortality Prevention Surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.254582484725052%\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.74541751527495%\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.254582484725052%\"\u003e\n \u003cp\u003eDHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.74541751527495%\"\u003e\n \u003cp\u003eDemographic and Health Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.254582484725052%\"\u003e\n \u003cp\u003eGDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.74541751527495%\"\u003e\n \u003cp\u003eGestational Diabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.254582484725052%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.74541751527495%\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.254582484725052%\"\u003e\n \u003cp\u003eSTEPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.74541751527495%\"\u003e\n \u003cp\u003eSTEPwise approach to Surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.254582484725052%\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.74541751527495%\"\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eEthics approval and consent to participate\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAll methods were carried out in accordance with relevant guidelines and regulations.\u0026nbsp;Ethical approval was provided by the Ethical Review Committee of\u0026nbsp;the International Centre for Diarrheal Disease Research, Bangladesh on April 24, 2019 under protocol number PR-19023. Written informed consent was obtained from the woman interviewed, or, in the event that the woman was under the age of 18 years, informed assent was taken and witnessed by a guardian from the household.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConsent for publication\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAvailability of data and materials\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Bill and Melinda Gates Foundation as a part of the CHAMPS project.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthors\u0026apos; contributions\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAPB, WB, JK and ESG conceptualized and designed the study. AC, SB, SEA and ESG oversaw data collection. KHL and APB curated the data. APB conducted data analysis and prepared the first draft. APB, WB, JK, and ESG\u0026nbsp;participated in critical revision of the manuscript and contributed to its intellectual improvement. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe study team is grateful to the Bill and Melinda Gates Foundation for their support to the CHAMPS study in Bangladesh which provided a strong foundation for this data collection. We are also grateful to the CHAMPS study team for their work in the data collection.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIDF Diabetes Atlas, 9th edition [Internet]. Brussels, Belgium; 2019 [cited 2021 Feb 5]. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/pubmed/18033288\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":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hypertension, Diabetes, Pregnancy, Bangladesh, Antenatal Care","lastPublishedDoi":"10.21203/rs.3.rs-964052/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-964052/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eHealth care systems in limited resource settings may not meet the needs of pregnant women where the burden of diabetes and hypertension is rapidly increasing. We described screening and diagnosis of diabetes or hypertension among recently pregnant women in rural Bangladesh and the antenatal care received.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe asked recently pregnant women about ever having been screened for or diagnosed with hypertension or diabetes and their antenatal care-seeking experiences in a cross-sectional survey in the Baliakandi, Bangladesh. We used chi-squared tests and logistic regression to test the associations between self-reported coverage of hypertension and diabetes screening, diagnoses, and elements of antenatal care by age, wealth, educational attainment, and gravidity. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong 4,692 respondents, 97% reported having been screened and 10% of screened women reported a diagnosis of hypertension. \u0026nbsp;Women 30–39 years of age (aOR 3.02, 95% CI 2.00, 4.56) or in the top wealth quintile (aOR 1.70, 95%\u0026nbsp;CI 1.18,\u0026nbsp;2.44) were more likely to be diagnosed with hypertension compared to reference groups. Any hypertension diagnosis was associated with reporting four or more antenatal care contacts (44% vs. 35%, p\u0026nbsp;\u0026lt;\u0026nbsp;0.01), blood pressure measurements (85% vs. 79%, p\u0026nbsp;\u0026lt;\u0026nbsp;0.01), and urine (71% vs. 61%, p\u0026nbsp;\u0026lt;\u0026nbsp;0.01) tests conducted during antenatal care visits.\u003c/p\u003e\u003cp\u003eFor diabetes, 46% of respondents reported having been screened and 3% of screened women reported a diagnosis. \u0026nbsp;Women 30–39 years of age were more likely to be diagnosed with diabetes (aOR 8.19, 95% CI 1.74, 38.48) compared to the reference group. Any diabetes diagnosis was associate with reporting four or more antenatal care contacts (48% vs. 36%, p\u0026nbsp;=\u0026nbsp;0.04) and having blood testing during pregnancy (83% vs. 66%, p\u0026nbsp;\u0026lt;\u0026nbsp;0.01). However, the frequency and quality of antenatal care was below the national guidelines among all groups.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eFocused efforts to ensure that women receive the recommended number of antenatal care contacts, coupled with improved compliance with antenatal care guidelines (including universal screening for diabetes at 24–28 weeks of pregnancy), would improve awareness of hypertension and diabetes among women in Bangladesh.\u003c/p\u003e","manuscriptTitle":"Self-Reported Diabetes Or Hypertension Diagnoses And Antenatal Care Among Child-Bearing Women In Rural Bangladesh: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-11-02 20:24:00","doi":"10.21203/rs.3.rs-964052/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"39182d4d-ee5e-44f4-b7a1-e25253b15200","owner":[],"postedDate":"November 2nd, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":8182610,"name":"Health Policy"}],"tags":[],"updatedAt":"2021-12-03T08:59:04+00:00","versionOfRecord":[],"versionCreatedAt":"2021-11-02 20:24:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-964052","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-964052","identity":"rs-964052","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}