Rural Living Residence is Associated with Risk Factors of Gestational Diabetes: A Retrospective Comparative Cross-sectional Study in Central Uganda

Rural Living Residence is Associated with Risk Factors of Gestational Diabetes: A Retrospective Comparative Cross-sectional Study in Central Uganda | 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 Rural Living Residence is Associated with Risk Factors of Gestational Diabetes: A Retrospective Comparative Cross-sectional Study in Central Uganda William Lumu, Susan Nakireka, Ronald Kasoma Mutebi, Richard Billy Ndiwalana, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8775786/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Gestational diabetes (GDM) is a major public health problem. The risk factors of GDM are not the same in gravida residing in different geographical areas due to rural-urban differences in maternal metabolic health and risk factors of diabetes. The study aimed to assess the association between rural living residence and GDM risk factors in Central Uganda. Methods This is a retrospective comparative cross-sectional study in which data from 600 women with GDM from 2016–2018 was extracted, with 300 from Luwero district(rural) and 300 from Mengo hospital(urban). Data on socio-demographics, clinical characteristics and risk factors was collected. Diagnosis of GDM was based on the IADSPG/ADA one step approach using a 75g OGTT (Oral Glucose Tolerance Test). The risk factors for GDM were defined using standard methods. The socio-demographic and clinical characteristics were described appropriately depending on their distribution. The Modified Poisson regression analysis model was used to explore the association between rural living residence and GDM risk factors expressed as adjusted prevalence ratios with their 95% confidence intervals and p-values. Statistical significance was set at < 0.05. Results Overall,600 women with GDM were included (n = 300 urban, n = 300 rural). The median age was 27years (IQR 21–32), with rural women younger than urban ones(P < 0.001). Rural women were shorter (≤ 150cm) P < 0.001, had family history of diabetes (P < 0.001), and parity (≥ 5) P < 0.001 compared to their urban counterparts. Rural living residence was positively and significantly associated with history of hypertension(APR = 1.343,95% CI 1.164–1.55,P < 0.001),history of GDM (APR = 1.391,95% C.I 1.208-1.60,P < 0.001),family history of diabetes(APR = 1.343,95% C.I 1.158–1.557,P < 0.001).Women with parity ≥ 5 were 31.6% more likely to be rural (APR = 1.316,95% CI 1.261–1.584,P < 0.001). The likelihood of having a height < 150cm was more than 38% with rural women than urban ones (APR = 1.381,95% C.I 1.228–1.612, P < 0.001). Women in rural areas were 31% less likely to be older than 35years APR = 0.687 (0.558– 0.847, P < 0.001). Conclusion We found that rural living residence is associated with risk factors of gestational diabetes. With increasing burden of diabetes in rural areas, universal screening for GDM may provide a window of opportunity to detect and manage glucose abnormalities including intermediate hyperglycemia and type 2 diabetes in rural clinical settings. Gestational diabetes risk factors rural area Mengo hospital Central Uganda Figures Figure 1 Background Gestational diabetes (GDM) is described as elevated blood glucose levels initially identified during pregnancy at concentrations lower than those of evident diabetes( 1 ).It affects approximately 9–14% of pregnancies worldwide; the variation in its prevalence is due to differences in risk factors and screening and diagnosis methods. It is rising alongside type 2 diabetes(T2D) and obesity( 2 ). Gestational diabetes continues to be a neglected maternal health problem despite its linkage with several pregnancy complications and its associated risk of T2D and cardiovascular disease (CVD) for both the mother and the unborn child later in life. Indeed, many middle-and low-income countries including Uganda have suboptimal screening rates for GDM, and routine prenatal care rarely includes GDM screening. This lack of political priority for GDM on the international scene is a missed opportunity to improve maternal health and accelerate progress towards the most off-track Millenium Development Goal number 5( 3 ) ( 4 ) Pregnant women who have a number of predetermined or chosen risk factors should be evaluated for GDM according to Uganda Clinical Guidelines ( 5 ). However, this is not routinely done in public health facilities. In some cases, screening is carried out on an individual basis using the clinician’s discretion and a random blood glucose is used in place of the Oral Glucose Tolerance test( 5 ). Moreover, selective screening fails to identify more than 50% of women with GDM, according to a study conducted in rural West Nile( 4 ).Therefore, the management and follow up of women with GDM in this part of the country is limited as a result of selective and inconsistent testing. It is imperative to understand the prevalence and burden of risk factors in the Ugandan context in order to conduct focused screening based on predetermined or selected risk factors. There is research evidence that the risk factors and pregnancy related outcomes of GDM differ among gravida living in different geographic locations. This is because maternal metabolic health and diabetes risk factors differ between rural and urban areas( 6 ).Additionally, pregnant women in rural areas have been shown to have higher body mass index(BMI) and more chronic comorbid conditions, such as hypertension, than those in urban areas ( 6 ).The disparity in risk factors in the rural areas is further exacerbated by lower educational attainment, insufficient insurance coverage ( 6 , 7 ) ,and a dearth of subspeciality diabetes-focused care( 8 ). Moreover, rural hospitals have more resources and doctors for specialized obstetric care than rural medical centers ( 6 – 8 ). According to the most recent 2023 National Non-Communicable Disease (NCD) steps survey, the prevalence of diabetes in Uganda has more than doubled over the past 10 years ( 9 ).Although, the prevalence of diabetes in rural and urban areas of the country does not differ significantly, there was a notable increase in prevalence in rural areas between 2014 and 2023. This is as a result of the rise in risk factors including overweight and sedentariness in rural areas( 9 ). This may or may not be congruent with GDM. Usually, the prevalence of GDM reflects that of T2D in the general population ( 10 ). Furthermore, whether GDM risk factors change between urban and rural areas in our context is unknown. The disparity between the burden of risk factors in Uganda’s rural and urban areas has not been thoroughly investigated. The association between the risk factors and living in a rural area is also unclear. Understanding the association could help with the development of context-specific GDM screening and management procedures in a nation with low screening rates. Thus, the current study set out to determine the association between risk factors for gestational diabetes and rural living residence. Methods Study design This was a retrospective comparative cross-sectional study. Setting The study involved women diagnosed and managed for GDM grouped in terms of living residence i.e. rural and urban which was also the area of location of the health facilities. We extracted data from the files of women who were screened for GDM in Mengo hospital and 23 health facilities in Luwero district in the Gestational Diabetes in Central Uganda (GICU) program from 2016 to 2018. The program was implemented by Uganda Diabetes Association (UDA) and Reproductive Health Uganda (RHU). The total number of 10,000 women attending routine antenatal care clinics in 23 health facilities in Luwero and Mengo Hospital were enrolled into the program. Five thousand (5,000) women were enrolled in 23 health units in Luwero while 5,000 were enrolled in Mengo hospital (Fig. 1 ) . In Luwero District, screening for GDM was provided at the 23 public and private health clinics providing antenatal services from Health Centre II to Health Centre IV and at one RHU clinic, which is a private provider/Non-Governmental Organization. Within the facilities, the protocol for performing the OGTT included instructing women to fast for 8 hours and then come to the facilities. After a 15-minute rest, their fasting blood glucose was measured using a calibrated ACCUCHECK active glucometer. After measurement of their fasting blood glucose, they were then given 75grams of glucose dissolved in 300mls of clean drinking water and asked to drink the solution for three minutes. One and two hours later, their blood glucose levels were once more assessed. The diagnosis of GDM was based on the current IADSPG/ADA screening guidelines that use a one-step approach of a 75g OGTT (Oral Glucose Tolerance Test) when one of the following values were met or surpassed: 0-hr (fasting) ≥ 92mg/dl 1-hr≥180mg/dl 2-hr≥153mg/dl ( 2 ). All women diagnosed with GDM were advised to deliver from a health facility. Women with GDM in Luwero were linked to the hospitals which had medical officers, operating theatres and a nursery for premature births. The community women’s groups followed these women after delivery for support after pregnancy in terms of nutrition and adherence to life style modification advice and following the recommendations on follow up screening to prevent subsequent T2D. Furthermore, those who delivered at a health facility had their baby monitored and managed for adverse neonatal outcomes such as hypoglycaemia, respiratory distress etc before discharge while the mother was given post-natal follow up and re-testing for diabetes 6–12 weeks after delivery. All mothers underwent a non-pregnant OGTT. Those diagnosed with impaired fasting glucose, impaired glucose tolerance and T2D were referred for further medical treatment of their conditions. All women irrespective of their results on OGTT were encouraged to continue with lifestyle modifications. All mothers diagnosed with GDM were screened for diabetes with a non-pregnant OGTT. Those diagnosed with impaired glucose tolerance and T2D were referred for further medical treatment of their conditions. Sample size The formula for comparative cross-sectional studies, which compare two groups, was utilized to get the sample size( 11 ). The following formula was used to obtain the number of the participants in each group i.e. urban and rural. $$\:n=\frac{r+1}{r}{*\:\left(Z\alpha\:/2+Z\beta\:\right)}^{2}*P*\frac{(1-P)}{P1-P2}$$ Where: n=Sample size needed per group r=ratio of participants in urban to rural facilities (r = 1, study had equal participants in each group) Zα/2: Z score of the desired confidence level (1.96 for 95% Confidence Interval) Zβ: Z-score of desired power of 80% power. (i.e.0.84 for a power of 80% P1: Expected proportion(prevalence) in rural health facilities. P2: Expected proportion(prevalence) in urban health facilities. P: Average proportions(prevalence) (P1 + P2)/2 The data from a Tanzanian study ( 12 ) where the prevalence (P1) of GDM was 20.5% in rural areas while the prevalence (P2) of GDM was 31.6% in urban areas was used to calculate the sample size. $$\:n=\frac{1+1}{1}{*\:\left(1.96+0.84\right)}^{2}*0.205+0.316/2*\frac{(1-0.2605)}{0.205-0.316}$$ The calculated sample size was 245 per group. The sample size was increased by 20% to cater for the dropping of files with more than 75% of missing data resulting into 49 women added to each group (i.e. 294) which was rounded to 300. Therefore, the total sample was 600 for both rural and urban health facilities. Sampling All the files of the parturient women were eligible as long as they contained data on OGTT and risk factor profile. The files of women who transferred out of these facilities during the two-year program were excluded. The files were sorted consecutively before data was extracted. From Luwero district, there were three Health Centre IVs, seventeen Health Centre IIIs and three Health Center IIs. Women were enrolled into the program in a ratio of 14:1.5:1 respectively. The same ratio was used to select the number of charts from the different levels of health facilities. Therefore, 14/16.5x300 = 255 files were selected from Health Center IVs i.e.85 from each facility. For Health Centre IIIs,1.5/16.5X300 = 27 files were selected i.e. 9 charts from each facility. Regarding Health Centre IIs, 1/16.5x300 = 18 files were selected i.e. 6 files from each facility. To obtain the desired sample size of 300, we selected files in Mengo Hospital consecutively. Data collection Research assistants were trained by the Principal Investigator to extract data from mothers’ files who were screened and managed for GDM in 23 health facilities in Luwero and Mengo hospital from 2016 to 2018 and record it in the data extraction form (Supplementary file 1: Data Extraction Form). The training was held in Mengo Hospital for 2 days. It was based on the research protocol. The trained Research assistants collected data. The data was checked daily by the Principal Investigator for completeness prior to entry into the computer. Data extracted from patient files was coded and entered into epi data manager and exported into STATA version 16 (Statacorp LLC, College Station, Texas, United States of America) for analysis. Extracted data was stored in a computer that was locked by a password known only to the Principal Investigator. Using the 3-2-1 backup technique, we maintained three copies of our data: two backups and the original. These copies were kept on two distinct media types: cloud storage devices, external storage, and internal hard drives. Variables The study evaluated the association between rural living residence and risk factors of GDM. The following data was collected. Independent (Predictor) Variable This was comprised of the living residence of the pregnant woman. Living residence was either urban or rural as indicated on the woman’s file. There were 23 health facilities in Luwero district, which is 70 kilometers from Kampala. The women from these health facilities were thought to reside in the country's rural areas. These areas lack metropolitan hubs, developed roads, schools, and are less populated than the main city. Women who attended Mengo Hospital and lived in Kampala and its surrounding areas made up the urban. Dependent (Outcome) Variables Dependent (Outcome) Variables The dependent variables included information on socio-demographics (age,occupation,marital status and education attainment), medical history (family history of diabetes and hypertension), past obstetric history of GDM, parity, history of twin pregnancy, history of macrosomia ≥ 4 kg, baseline fasting blood glucose, blood pressure, weight, height, and body mass index. Fasting blood glucose Following an 8-hour fast and a 15-minute rest period, the women's fasting blood glucose levels were assessed using a calibrated ACCUCHECK active glucometer. Anthropometric measurements Midwives in these facilities followed standard procedures to measure blood pressure( 13 )weight and height( 14 ). Briefly, OMRON digital blood pressure machine was used to take three measurements an average of which was recorded in the mother’s file. Regarding weight, mothers were instructed to take off any jewelry, heavy clothing, and shoes or slippers before taking their weights. Height was measured with a handheld stadiometer. Both the weight and height were recorded to the nearest 0.1measurements. With the Body Mass Index (BMI) computation, the midwives divided the square of the height in meters by the weight in kilograms. The mothers were categorized using standard BMI cut-offs as underweight (BMI < 18.5 Kg/m2), normal (BMI ≥ 18.5 25 to 30 Kg/m2)( 13 ). Definition of variables The rural living residence was defined as living in Luwero, a rural part of the country located 70Km from Kampala and outside municipalities characterized by a low population, large amounts of undeveloped land and a livelihood heavily dependent on peasant farming( 15 ).All women attending health facilities in Luwero were considered to be rural living. The urban residence was comprised of women who attended Mengo hospital residing in Kampala city and its suburbs. The GDM risk factors were defined as follows: Age≥35years, parity ≥ 5, body mass index ≥ 30Kg/m 2 , height<150cm, history of hypertension, history of poor pregnancy outcomes (abortion, fetal loss), history of delivering a baby≥4Kg, history of previous GDM, history of pregnancy induced hypertension, history of delivering twins( 16 ) and history of smoking( 17 ). Data analysis Data extracted from patient charts/files was coded and entered into epi data manager and exported into STATA version 16 (Statacorp LLC, College Station, Texas, United States of America) for analysis. The socio-demographic and clinical characteristics were described in terms of proportions, percentages, median and interquartile ranges. The Mann-Whitney test was used to compare continuous variables while the Chi-square test was used to compare categorical ones between urban and rural living residences. We employed a Modified Poisson regression analysis model to investigate the relationship between GDM risk factors and rural living residence since the prevalence of the risk factors was not uncommon (i.e., > 20%). Bi-variable analysis was used to determine the unadjusted prevalence ratios. The multi-variable model was used to include the variables with p ≤ 0.20. Adjusted prevalence ratios were calculated along with their p-values and 95% CIs. At the two-tailed level, the results were considered statistically significant when the p-value was less than 0.05 Results Socio-demographic and clinical characteristics of the women with GDM in urban and rural health facilities in Central Uganda A total of 600 women with GDM were included in this study and their median age was 27(IQR 21-32). Urban women were older than their rural counterparts(p<0.001). Women in rural areas were less educated than urban ones(p<0.001). Regarding occupation, rural women were mainly peasant farmers, house wives or not employed at all compared to those in urban areas(p<0.001) ( Table 1 ). Table 1: Social-demographic & clinical characteristics of women with GDM in urban and rural health facilities in Central Uganda Variable Urban(n=300) Rural(n=300) All(N=600) P-value Median Age in years (IQR) 29(25-33) 22(19-30) 27(21-32) <0.001 Education level No formal education 6(10.7%) 50(89.3%) 56(9.3%) Primary 28(13.7%) 177(86.3%) 205(34.2%) O-level 83(58.9%) 58(41.4%) 141(23.5%) A-level 39(81.2%) 9(18.8%) 48(8.0%) Tertiary 144(96.0%) 6(4.0%) 150(25.0%) <0.001 Occupation Farmer 16(12.9%) 108(87.1%) 124(20.7%) Business 113(84.3%) 21(15.7%) 134(22.35) Teacher 37(82.2%) 8(17.8%) 45(7.5%) House wife 70(45.8%) 83(54.2%) 153(25.5%) None 8(9.1%) 80(90.9%) 88(14.7%) Other 46(82.14%) 10(17.86%) 56(9.3%) <0.001 Marital status Single 25(20.0%) 100(80.0%) 125(20.8%) Married 270(57.7%) 198(42.3%) 468(78.0%) Separated 5(71.4%) 2(28.6%) 7(1.2%) <0.001 Baseline blood glucose <5.5mmol/l 204(41.8%) 284(58.2%) 488(81.3%) 5.5-6.99mmol/l 87(85.3%) 15(14.7%) 102(17.0%) ≥7mmol/l 9(90.0%) 1(10.0%) 10(1.7%) <0.001 IQR Interquartile range, n frequency Rural women were less heavy and shorter than their urban counter parts (p<0.001). Correspondingly, rural women tended to be more overweight than urban ones. The median fasting blood glucose, 1-hour blood glucose and 2-hour blood glucose values were 5.8mmol/l (IQR5.5-6.3),10.6mmol/l (IQR 9.4-10.5) and 8.7mmol/l (IQR 8-9.1) respectively ( Table 2). Table 2: Medians for clinical characteristics of women with GDM in urban and rural health facilities in Central Uganda Variable (Units) Urban(n=300) Rural(n=300) All(N=600) P-value Median (IQR) Median (IQR) Median (IQR) Parity 2(1-3) 3(3-6) 2(1-4) <0.001 Weight (Kg) 69(60-74) 59(54-65) 62(56-71) <0.001 Height(cm) 167(162-170) 153(147.75-159.9) 160(152-168) <0.001 BMI(Kg/m 2 ) 24(20.73-27.8) 25.15(23-27.25) 24.66(22-27.36) 0.003 SBP (mmHg) 113(103.5-121) 110(100-120) 110(101-120) 0.008 DBP (mmHg) 69.5(63-76) 67(60-72.5) 69(61-75) <0.001 Baseline FBG (mmol/l) 5.1(4.5-5.8) 4.4(3.9-4.9) 4.7(4.2-5.3) <0.001 FBG at OGTT (24-28 WOA) mmo/l 5.9(5.5-6.5) 5.8(5.4-6.1) 5.8(5.5-6.3) 0.002 1-hr BG at OGTT (24-28 WOA) mmol/l 10.8(9.8-10.9) 10.3(9.4-10.8) 10.6(9.4-10.9) <0.001 2-hr BG at OGTT (24-28 WOA) mmo/l 8.9(8.1-9.4) 8.3(7.8-8.9) 8.7(8-9.1) 0.001 IQR Interquartile range, n frequency, Kg Kilogram, BMI Body Mass Index, SBP Systolic Blood Pressure, DBP Diastolic Blood Pressure Fasting Blood Glucose, BG Blood Glucose, OGTT Oral Glucose Tolerance Test, WOA Weeks of Amenorrhea Differences in proportions of risk factors of GDM among women with GDM in urban and rural health facilities in Central Uganda In this study, urban women tended to be 35years and older 53(61.6%) compared to their rural counterparts 33(38.4%), p=0.02. Regarding parity, rural women 96(67.6%) delivered 5 children and more compared to urban women 46(32.4%), p<0. 001.However, obesity was higher among urban women than among rural women 46(64.8%) vs 25(35.2%) respectively. A short stature of less than 150cm was more prevalent among rural women than among urban ones (125(93.3%) vs 9 (6.7%) respectively, p<0.001. Family history of diabetes was reported more among rural women 222(55.5%) than urban ones 178(44.5%), p<0. 001.Regarding other risk factors, history of diabetes in the mother(p<0.001), previous GDM history(p=0.001) were more among urban women than rural ones (Table 3). Table 3: Proportions of risk factors for GDM among women in urban & rural health facilities in Central Uganda Variable (Units) Urban Rural All P-value n=300 n=300 N=600 Age≥35years 53(61.6%) 33(38.4%) 86(14.3%) 0.02 Parity≥5 46(32.4%) 96(67.6%) 142(23.7%) <0.001 BMI(≥30Kg/m2) 46(64.8%) 25(35.2%) 71(11.8%) 0.008 Height <150cm 9(6.7%) 125(93.3%) 134(22.3%) <0.001 History of hypertension 171(61.5%) 107(38.5%) 278(46.3%) <0.001 History of poor pregnancy outcomes 161(48.2%) 173(51.8%) 334(55.7%) 0.324 History of delivery of a baby ≥ 4Kg 121(49.8%) 122(50.2%) 243(40.5%) 0.934 Previous GDM 114(60.0%) 76(40.0%) 190(31.7%) 0.001 History of pre-eclampsia 68(56.2%) 53(43.8%) 121(20.2%) 0.127 History of twin pregnancy 135(54.4%) 113(45.6%) 248(41.3%) 0.068 Family history of DM 178(44.5%) 222(55.5%) 400(66.7%) <0.001 Smoking 31(57.4%) 23(42.6%) 54(9.0%) 0.254 n frequency, Kg Kilogram, BMI Body Mass Index, GDM Gestational Diabetes, DM Diabetes Mellitus There were no urban-rural disparities regarding the following risk factors; history of poor pregnancy outcomes(p=0.324), history of delivering a baby ≥ 4Kg(p=0.934), history of pre-eclampsia(p=0.127), history of twin pregnancy(p=0.068) and smoking(p=0.254). Gestational Diabetes risk factors significantly associated with rural living residence From the Modified Poisson regression model for multi-variable analysis (Table 4) ,rural living residence was positively and significantly associated with history of hypertension(APR=1.343,95% CI 1.164-1.55,P<0.001),history of previous gestational diabetes(APR=1.391,95%C.I 1.208-1.60,P<0.001),family history of diabetes(APR=1.343,95% C.I 1.158- 1.557,P<0.001).Furthermore, women with parity ≥5 were 31.6% more likely to be rural (APR=1.316,95% CI 1.261-1.584,P<0.001) while the likelihood of having a height <150cm was more than 38% with rural women than urban ones (APR=1.381,95% C.I 1.228-1.612,P<0.001). Women in the rural areas were 31% less likely to be 35years and older (APR=0.687 (0.558– 0.847, P<0.001). There was no association between rural living residence and BMI, history of delivering a big baby, history of poor pregnancy outcomes, twin pregnancy and pre-eclampsia. Table 4: Gestational Diabetes risk factors associated with rural living residence among women with GDM on Modified Poisson Regression Analysis. Variable CPR (95% CI) P-value APR (95% CI) P-value Maternal age (years) <35 1 – 1 – ≥35 0.584 (0.454 – 0.751) <0.001 0.687 (0.558– 0.847) <0.001 Parity <5 1 – 1 – ≥5 1.481(1.301 – 1.624) <0.001 1.316 (1.261 – 1.584) <0.001 Maternal height (cm) ≥150 1 1 – <150 1.349 (1.201- 1.512) <0.001 1.381 (1.228 – 1.612) <0.001 Hypertension No 1 – 1 – Yes 1.535 (1.305 – 1.807) <0.001 1.343 (1.164 – 1.550) <0.001 Previous GDM No 1 – 1 – Yes 1.323 (1.130 – 1.548) 0.001 1.391 (1.208 – 1.600) <0.001 Family history of diabetes No 1 – 1 – Yes 1.371 (1.173 – 1.602) <0.001 1.343 (1.158 – 1.557) <0.001 Body Mass Index (Kg/m 2 ) <30 1 – – – ≥30 1.349 (1.112 – 1.637) 0.002 – – History of big baby≥4Kg No 1 – – – Yes 0.993 (0.843 – 1.169) 0.934 – – History of preeclampsia No 1 – – – Yes 1.160 (0.967 – 1.393) 0.11 – – Twin pregnancy No 1 – – – Yes 1.000 (0.788 – 1.269) 1 – – History of maternal poor outcome Yes 1 – – – No 1.084 (0.924 – 1.272) 0.323 – – CPR Crude Prevalence Ratio, APR Adjusted Prevalence Ratio, CI Confidence Interval, GDM Gestational Diabetes Discussion The current study was aimed at establishing the association between rural living residence and risk factors of GDM. History of hypertension, previous gestational diabetes, family history of diabetes, parity ≥ 5, and height<150cm were positively and significantly associated with rural living residence. We also showed that age ≥ 35years was less likely associated with rurality. Our study was conducted in Luwero district, a rural area that was formerly ravaged by the Bush war from 1981–1986 ( 18 ).Some of the problems of civil conflicts are severe malnutrition and famine exposure( 19 ).Participants in our study are grand offsprings of women who were exposed to inutero and childhood malnutrition during the civil conflict. We found that our participants from the rural area were smaller and shorter with higher BMIs than their urban counterparts. This finding could be explained by the Developmental Origins of Heath and Disease (DoHad) hypothesis which links prenatal malnutrition with Non-Communicable Disease risk ( 20 ).Similarly, a community based Ethiopian survey in rural Ethiopia mooted that chronic malnutrition as a result of prolonged famine in that area led to a relatively high prevalence of GDM ( 21 ). Furthermore, rural women were less educated and were involved in less professional jobs (i.e. peasant farming, house wifehood). It has been shown that a low education level, non-professional occupation and early age at first birth are associated with a high fertility rate (high parity) ( 22 , 23 ) Lei Y et al 2025 showed that educational level, employment status, career advancement aspirations, and age-related anxiety were significantly associated with delayed childbearing ( 24 ). This could explain why our rural study participants had a higher parity and were less likely to be older than 35 years of age. In the current study, we showed that history of hypertension, previous GDM and family history of diabetes were associated with rural living. Our findings collate the recently conducted WHO NCD STEPS survey of 2023 which showed that there were more significant increases in the prevalence of some risk factors in rural areas than urban ones in the last decade namely sedentariness p < 0.001, high blood glucose p < 0.001 and overweight/obesity p < 0.001( 9 ).The same survey showed that the prevalence of hypertension in rural and urban areas is still high and has not changed in the last 10 years. The highly prevalent NCD risk factors in the rural areas are potentially explained by the increasing urbanization and westernization of rural communities in middle and low-income countries including Uganda ( 25 ). Urbanization is associated with consumption of unhealthy diets, excessive alcohol and sedentary life style that predispose to central obesity leading to insulin resistance ( 26 – 28 ). Insulin resistance underpins the development of hypertension, type 2 diabetes( 27 ) and gestational diabetes ( 29 ).Conversely, overweight,(BMI ≥ 25Kg/M 2 ,family history of diabetes, non-white ethnicity, multiparity and older maternal age are significant risk factors for progression to diabetes after GDM( 30 ).All these determinants are prevalent among our rural participants, this could partly explain why diabetes has increased in the rural areas in Uganda as depicted by the 2023 WHO NCD STEPS survey ( 9 ).It is known that intrauterine exposure to the metabolic environment of maternal diabetes, or GDM, is linked with increased risk of altered glucose homeostasis(impaired fasting glucose, impaired glucose tolerance, and T2D) in the offspring, starting in childhood leading to a higher prevalence of diabetes in the next generation( 10 ).With erratic selective GDM screening in the country, there is a missed opportunity to diagnose and treat T2D among post GDM women. Similar to our findings, several studies have shown that women living in rural areas give birth at a young age and have lower education status ( 31 – 34 )and higher BMIs( 6 ). Additionally, a study done by Graham et al 2007 showed a higher proportion of rural women with pre-existing and or hypertension and or diabetes compared to urban ones (p < 0.001). Whereas some studies have investigated the prevalence of GDM and its determinants in rural areas ( 4 , 35 – 38 ), they have not shown whether these are associated with rural living residence as is the case for our study. The current study highlights GDM risk factors that are associated with rural living residence. They resonate with the NCD risk factors that have increased in rural Uganda in the last 10 years. Typically, the incidence of GDM mirrors the incidence of T2D in the background population ( 10 ).Therefore, universal screening of all expectant women for GDM provides a window of opportunity to screen, diagnose and manage non-communicable diseases including T2D and other cardiovascular disease conditions early. Our findings may potentially underpin change in policy, i.e. from selective to universal GDM screening. The strengths of our study are worth mentioning, to our knowledge this is the first study in Uganda to evaluate the association between GDM risk factors and rural living residence. Secondly, we had robust data on risk factors of GDM to delineate their association with rurality. However, being a retrospective study, not all relevant information and data could be collected for all participants. Nevertheless, our study yielded pertinent data on the GDM risk factors and their association with rural living residence. Conclusion The current study has shown that rural living residence is associated with GDM risk factors i.e. history of hypertension, previous gestational diabetes, family history of diabetes, parity ≥ 5, and height<150cm. It has also shown that age ≥ 35years is less likely associated with rurality. Our study has provided data that may be potentially useful in designing context specific screening and management protocols for GDM in the country already faced with low screening rates. Furthermore, these factors being determinants of progression to T2D post GDM, our study findings highlight the opportunity to screen and manage T2D and other non-communicable diseases in our rural settings where diabetes has increased significantly in the last 10 years. Abbreviations ADA: American Diabetes Association APR: Adjusted Prevalence Ratio BG: Blood Glucose BMI: Body Mass Index CPR: Crude Prevalence Ratio DBP: Diastolic Blood Pressure DM: Diabetes Mellitus DOHad: Developmental Origins of Heath and Disease FBG: Fasting Blood Glucose GICU: Gestational Diabetes in Central Uganda IADSPG: International Association of Diabetes and Pregnancy Study Groups IQR: Interquartile Range NCD: Non-Communicable Diseases OGTT: Oral Glucose Tolerance Test RHU: Reproductive Health Uganda SBP: Systolic Blood Pressure UDA: Uganda Diabetes Association WHO: World Health Organization WOA: Weeks of Amenorrhea Declarations Ethics approval and consent of participants In this retrospective comparative cross-sectional study, we obtained a waiver of informed consent and approval from Mengo Hospital Research EthicsCommittee (approvalnumber MH 136/07-2025). We de-identified selected files and used codes to ensure participants’ confidentiality. The study was conducted in observance of the Declaration of Helsinki. Consent for publication Not applicable Availability of data and materials The data sets used and or analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding No funding was obtained to carry out this study. Authors contributions WL, SN, RKM, RBN, SPN conceptualized the study and designed the methodology. RS, RBN, and WL developed the statistical plan for the study. RKM, with the help of RS and WL, analyzed the data. WL, SN, RKM, RBN, DM, GN, HN, RM, SN, EN, RS, AK and SPN designed the manuscript and revised and approved its final version. Acknowledgements We wish to extend our appreciation to the Department of Endocrinology and Non-Communicable Diseases, the Hospital Management Committee of Mengo Hospital and the research assistants for their support during this study. 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Thanikachalam M, Fuller CH, Lane KJ, Sunderarajan J, Harivanzan V, Brugge D, et al. Urban environment as an independent predictor of insulin resistance in a South Asian population. Int J Health Geogr. 2019 Feb;18(1):5. Schuster DP. Obesity and the development of type 2 diabetes: the effects of fatty tissue inflammation. Diabetes Metab Syndr Obes. 2010 Jul; 3:253–62. Kurniawan F, Manurung MD, Harbuwono DS, Yunir E, Tsonaka R, Pradnjaparamita T, et al. Urbanization and Unfavorable Changes in Metabolic Profiles: A Prospective Cohort Study of Indonesian Young Adults. Nutrients [Internet]. 2022;14(16). Available from: https://www.mdpi.com/2072-6643/14/16/3326 Catalano PM, Kirwan JP, Haugel-de Mouzon S, King J. Gestational diabetes and insulin resistance: role in short- and long-term implications for mother and fetus. J Nutr. 2003 May;133(5 Suppl 2):1674S-1683S. Rayanagoudar G, Hashi AA, Zamora J, Khan KS, Hitman GA, Thangaratinam S. Quantification of the type 2 diabetes risk in women with gestational diabetes: a systematic review and meta-analysis of 95,750 women. Diabetologia. 2016 Jul;59(7):1403–11. Abdel-Latif ME, Bajuk B, Oei J, Vincent T, Sutton L, Lui K. Does rural or urban residence make a difference to neonatal outcome in premature birth? A regional study in Australia. Arch Dis Child Fetal Neonatal Ed. 2006 Jul;91(4): F251-6. Graham S, Pulver LRJ, Wang YA, Kelly PM, Laws PJ, Grayson N, et al. The urban-remote divide for Indigenous perinatal outcomes. Med J Aust. 2007 May;186(10):509–12. Powers JR, Loxton DJ, O’Mara AT, Chojenta CL, Ebert L. Regardless of where they give birth, women living in non-metropolitan areas are less likely to have an epidural than their metropolitan counterparts. Women Birth. 2013 Jun;26(2): e77-81. Hennegan J, Kruske S, Redshaw M. Remote access and care: A comparison of Queensland women’s maternity care experience according to area of residence. Women Birth. 2014 Dec;27(4):281–91. Kiiza F, Kayibanda D, Tumushabe P, Kyohairwe L, Atwine R, Kajabwangu R, et al. Frequency and Factors Associated with Hyperglycaemia First Detected during Pregnancy at Itojo General Hospital, South Western Uganda: A Cross-Sectional Study. J Diabetes Res. 2020; 2020:4860958. Kahimakazi I, Tornes YF, Tibaijuka L, Kanyesigye H, Kiptoo J, Kayondo M, et al. Prevalence of gestational diabetes mellitus and associated factors among women receiving antenatal care at a tertiary hospital in South-Western Uganda. Pan Afr Med J. 2023; 46:50. Natamba BK, Namara AA, Nyirenda MJ. Burden, risk factors and maternal and offspring outcomes of gestational diabetes mellitus (GDM) in sub-Saharan Africa (SSA): a systematic review and meta-analysis. BMC Pregnancy Childbirth [Internet]. 2019;19(1):450. Available from: https://doi.org/10.1186/s12884-019-2593-z Nakabuye B, Bahendeka S, Byaruhanga R. Prevalence of hyperglycaemia first detected during pregnancy and subsequent obstetric outcomes at St. Francis Hospital Nsambya. BMC Res Notes. 2017 May;10(1):174. Additional Declarations No competing interests reported. Supplementary Files DataExtractionForm.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviewers invited by journal 10 Mar, 2026 Editor assigned by journal 09 Mar, 2026 Editor invited by journal 15 Feb, 2026 Submission checks completed at journal 15 Feb, 2026 First submitted to journal 15 Feb, 2026 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. 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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-8775786","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604790137,"identity":"804f576c-2094-46ab-b364-a669a9e3d27c","order_by":0,"name":"William 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12:25:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8775786/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8775786/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104596156,"identity":"f4d5e085-6e30-42db-9ed6-969029c7819e","added_by":"auto","created_at":"2026-03-13 18:24:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64169,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart showing enrollment of study participants.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8775786/v1/a650cbc87536c86808ca67c9.png"},{"id":104786094,"identity":"48f3720d-8256-46ec-a8a7-8ed1f425ab7a","added_by":"auto","created_at":"2026-03-17 08:14:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1556741,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8775786/v1/b0031604-be1d-4a59-bdf1-c06207d0e42d.pdf"},{"id":104781446,"identity":"0814921a-d890-4ca4-8701-aae7f3878e30","added_by":"auto","created_at":"2026-03-17 07:55:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":23678,"visible":true,"origin":"","legend":"","description":"","filename":"DataExtractionForm.docx","url":"https://assets-eu.researchsquare.com/files/rs-8775786/v1/9c8117cda60f0d7dddf4e357.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rural Living Residence is Associated with Risk Factors of Gestational Diabetes: A Retrospective Comparative Cross-sectional Study in Central Uganda","fulltext":[{"header":"Background","content":"\u003cp\u003eGestational diabetes (GDM) is described as elevated blood glucose levels initially identified during pregnancy at concentrations lower than those of evident diabetes(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).It affects approximately 9\u0026ndash;14% of pregnancies worldwide; the variation in its prevalence is due to differences in risk factors and screening and diagnosis methods. It is rising alongside type 2 diabetes(T2D) and obesity(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGestational diabetes continues to be a neglected maternal health problem despite its linkage with several pregnancy complications and its associated risk of T2D and cardiovascular disease (CVD) for both the mother and the unborn child later in life. Indeed, many middle-and low-income countries including Uganda have suboptimal screening rates for GDM, and routine prenatal care rarely includes GDM screening. This lack of political priority for GDM on the international scene is a missed opportunity to improve maternal health and accelerate progress towards the most off-track Millenium Development Goal number 5(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003ePregnant women who have a number of predetermined or chosen risk factors should be evaluated for GDM according to Uganda Clinical Guidelines (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, this is not routinely done in public health facilities. In some cases, screening is carried out on an individual basis using the clinician\u0026rsquo;s discretion and a random blood glucose is used in place of the Oral Glucose Tolerance test(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Moreover, selective screening fails to identify more than 50% of women with GDM, according to a study conducted in rural West Nile(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).Therefore, the management and follow up of women with GDM in this part of the country is limited as a result of selective and inconsistent testing.\u003c/p\u003e \u003cp\u003eIt is imperative to understand the prevalence and burden of risk factors in the Ugandan context in order to conduct focused screening based on predetermined or selected risk factors. There is research evidence that the risk factors and pregnancy related outcomes of GDM differ among gravida living in different geographic locations. This is because maternal metabolic health and diabetes risk factors differ between rural and urban areas(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).Additionally, pregnant women in rural areas have been shown to have higher body mass index(BMI) and more chronic comorbid conditions, such as hypertension, than those in urban areas (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).The disparity in risk factors in the rural areas is further exacerbated by lower educational attainment, insufficient insurance coverage (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) ,and a dearth of subspeciality diabetes-focused care(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Moreover, rural hospitals have more resources and doctors for specialized obstetric care than rural medical centers (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to the most recent 2023 National Non-Communicable Disease (NCD) steps survey, the prevalence of diabetes in Uganda has more than doubled over the past 10 years (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).Although, the prevalence of diabetes in rural and urban areas of the country does not differ significantly, there was a notable increase in prevalence in rural areas between 2014 and 2023. This is as a result of the rise in risk factors including overweight and sedentariness in rural areas(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This may or may not be congruent with GDM. Usually, the prevalence of GDM reflects that of T2D in the general population (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Furthermore, whether GDM risk factors change between urban and rural areas in our context is unknown. The disparity between the burden of risk factors in Uganda\u0026rsquo;s rural and urban areas has not been thoroughly investigated. The association between the risk factors and living in a rural area is also unclear. Understanding the association could help with the development of context-specific GDM screening and management procedures in a nation with low screening rates.\u003c/p\u003e \u003cp\u003eThus, the current study set out to determine the association between risk factors for gestational diabetes and rural living residence.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis was a retrospective comparative cross-sectional study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSetting\u003c/h3\u003e\n\u003cp\u003eThe study involved women diagnosed and managed for GDM grouped in terms of living residence i.e. rural and urban which was also the area of location of the health facilities. We extracted data from the files of women who were screened for GDM in Mengo hospital and 23 health facilities in Luwero district in the Gestational Diabetes in Central Uganda (GICU) program from 2016 to 2018. The program was implemented by Uganda Diabetes Association (UDA) and Reproductive Health Uganda (RHU). The total number of 10,000 women attending routine antenatal care clinics in 23 health facilities in Luwero and Mengo Hospital were enrolled into the program. Five thousand (5,000) women were enrolled in 23 health units in Luwero while 5,000 were enrolled in Mengo hospital (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. In Luwero District, screening for GDM was provided at the 23 public and private health clinics providing antenatal services from Health Centre II to Health Centre IV and at one RHU clinic, which is a private provider/Non-Governmental Organization.\u003c/p\u003e \u003cp\u003eWithin the facilities, the protocol for performing the OGTT included instructing women to fast for 8 hours and then come to the facilities. After a 15-minute rest, their fasting blood glucose was measured using a calibrated ACCUCHECK active glucometer. After measurement of their fasting blood glucose, they were then given 75grams of glucose dissolved in 300mls of clean drinking water and asked to drink the solution for three minutes. One and two hours later, their blood glucose levels were once more assessed.\u003c/p\u003e \u003cp\u003e The diagnosis of GDM was based on the current IADSPG/ADA screening guidelines that use a one-step approach of a 75g OGTT (Oral Glucose Tolerance Test) when one of the following values were met or surpassed:\u003c/p\u003e \u003cp\u003e0-hr (fasting) \u0026ge; 92mg/dl\u003c/p\u003e \u003cp\u003e1-hr\u0026ge;180mg/dl\u003c/p\u003e \u003cp\u003e2-hr\u0026ge;153mg/dl (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll women diagnosed with GDM were advised to deliver from a health facility. Women with GDM in Luwero were linked to the hospitals which had medical officers, operating theatres and a nursery for premature births. The community women\u0026rsquo;s groups followed these women after delivery for support after pregnancy in terms of nutrition and adherence to life style modification advice and following the recommendations on follow up screening to prevent subsequent T2D.\u003c/p\u003e \u003cp\u003eFurthermore, those who delivered at a health facility had their baby monitored and managed for adverse neonatal outcomes such as hypoglycaemia, respiratory distress etc before discharge while the mother was given post-natal follow up and re-testing for diabetes 6\u0026ndash;12 weeks after delivery. All mothers underwent a non-pregnant OGTT. Those diagnosed with impaired fasting glucose, impaired glucose tolerance and T2D were referred for further medical treatment of their conditions. All women irrespective of their results on OGTT were encouraged to continue with lifestyle modifications. All mothers diagnosed with GDM were screened for diabetes with a non-pregnant OGTT. Those diagnosed with impaired glucose tolerance and T2D were referred for further medical treatment of their conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eThe formula for comparative cross-sectional studies, which compare two groups, was utilized to get the sample size(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe following formula was used to obtain the number of the participants in each group i.e. urban and rural.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:n=\\frac{r+1}{r}{*\\:\\left(Z\\alpha\\:/2+Z\\beta\\:\\right)}^{2}*P*\\frac{(1-P)}{P1-P2}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003en=Sample size needed per group\u003c/p\u003e \u003cp\u003er=ratio of participants in urban to rural facilities (r\u0026thinsp;=\u0026thinsp;1, study had equal participants in each group)\u003c/p\u003e \u003cp\u003eZα/2: Z score of the desired confidence level (1.96 for 95% Confidence Interval)\u003c/p\u003e \u003cp\u003eZβ: Z-score of desired power of 80% power. (i.e.0.84 for a power of 80%\u003c/p\u003e \u003cp\u003eP1: Expected proportion(prevalence) in rural health facilities.\u003c/p\u003e \u003cp\u003eP2: Expected proportion(prevalence) in urban health facilities.\u003c/p\u003e \u003cp\u003eP: Average proportions(prevalence) (P1\u0026thinsp;+\u0026thinsp;P2)/2\u003c/p\u003e \u003cp\u003eThe data from a Tanzanian study (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) where the prevalence (P1) of GDM was 20.5% in rural areas while the prevalence (P2) of GDM was 31.6% in urban areas was used to calculate the sample size.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:n=\\frac{1+1}{1}{*\\:\\left(1.96+0.84\\right)}^{2}*0.205+0.316/2*\\frac{(1-0.2605)}{0.205-0.316}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe calculated sample size was 245 per group. The sample size was increased by 20% to cater for the dropping of files with more than 75% of missing data resulting into 49 women added to each group (i.e. 294) which was rounded to 300. Therefore, the total sample was 600 for both rural and urban health facilities.\u003c/p\u003e\n\u003ch3\u003eSampling\u003c/h3\u003e\n\u003cp\u003eAll the files of the parturient women were eligible as long as they contained data on OGTT and risk factor profile. The files of women who transferred out of these facilities during the two-year program were excluded.\u003c/p\u003e \u003cp\u003eThe files were sorted consecutively before data was extracted. From Luwero district, there were three Health Centre IVs, seventeen Health Centre IIIs and three Health Center IIs. Women were enrolled into the program in a ratio of 14:1.5:1 respectively. The same ratio was used to select the number of charts from the different levels of health facilities. Therefore, 14/16.5x300\u0026thinsp;=\u0026thinsp;255 files were selected from Health Center IVs i.e.85 from each facility. For Health Centre IIIs,1.5/16.5X300\u0026thinsp;=\u0026thinsp;27 files were selected i.e. 9 charts from each facility. Regarding Health Centre IIs, 1/16.5x300\u0026thinsp;=\u0026thinsp;18 files were selected i.e. 6 files from each facility. To obtain the desired sample size of 300, we selected files in Mengo Hospital consecutively.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eResearch assistants were trained by the Principal Investigator to extract data from mothers\u0026rsquo; files who were screened and managed for GDM in 23 health facilities in Luwero and Mengo hospital from 2016 to 2018 and record it in the data extraction form \u003cb\u003e(Supplementary file 1: Data Extraction Form).\u003c/b\u003e The training was held in Mengo Hospital for 2 days. It was based on the research protocol. The trained Research assistants collected data. The data was checked daily by the Principal Investigator for completeness prior to entry into the computer. Data extracted from patient files was coded and entered into epi data manager and exported into STATA version 16 (Statacorp LLC, College Station, Texas, United States of America) for analysis. Extracted data was stored in a computer that was locked by a password known only to the Principal Investigator. Using the 3-2-1 backup technique, we maintained three copies of our data: two backups and the original. These copies were kept on two distinct media types: cloud storage devices, external storage, and internal hard drives.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVariables\u003c/h2\u003e \u003cp\u003eThe study evaluated the association between rural living residence and risk factors of GDM. The following data was collected.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIndependent (Predictor) Variable\u003c/h3\u003e\n\u003cp\u003eThis was comprised of the living residence of the pregnant woman.\u003c/p\u003e \u003cp\u003eLiving residence was either urban or rural as indicated on the woman\u0026rsquo;s file. There were 23 health facilities in Luwero district, which is 70 kilometers from Kampala. The women from these health facilities were thought to reside in the country's rural areas. These areas lack metropolitan hubs, developed roads, schools, and are less populated than the main city. Women who attended Mengo Hospital and lived in Kampala and its surrounding areas made up the urban.\u003c/p\u003e\n\u003ch3\u003eDependent (Outcome) Variables\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eDependent (Outcome) Variables\u003c/div\u003e \u003cp\u003eThe dependent variables included information on socio-demographics (age,occupation,marital status and education attainment), medical history (family history of diabetes and hypertension), past obstetric history of GDM, parity, history of twin pregnancy, history of macrosomia\u0026thinsp;\u0026ge;\u0026thinsp;4 kg, baseline fasting blood glucose, blood pressure, weight, height, and body mass index.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFasting blood glucose\u003c/h2\u003e \u003cp\u003eFollowing an 8-hour fast and a 15-minute rest period, the women's fasting blood glucose levels were assessed using a calibrated ACCUCHECK active glucometer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAnthropometric measurements\u003c/h2\u003e \u003cp\u003eMidwives in these facilities followed standard procedures to measure blood pressure(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)weight and height(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Briefly, OMRON digital blood pressure machine was used to take three measurements an average of which was recorded in the mother\u0026rsquo;s file. Regarding weight, mothers were instructed to take off any jewelry, heavy clothing, and shoes or slippers before taking their weights. Height was measured with a handheld stadiometer. Both the weight and height were recorded to the nearest 0.1measurements.\u003c/p\u003e \u003cp\u003eWith the Body Mass Index (BMI) computation, the midwives divided the square of the height in meters by the weight in kilograms. The mothers were categorized using standard BMI cut-offs as underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 Kg/m2), normal (BMI\u0026thinsp;\u0026ge;\u0026thinsp;18.5\u0026thinsp;\u0026lt;\u0026thinsp;25 Kg/m2), overweight (BMI\u0026thinsp;\u0026gt;\u0026thinsp;25 to \u0026lt;\u0026thinsp;30 Kg/m2), and obese (BMI\u0026thinsp;\u0026gt;\u0026thinsp;30 Kg/m2)(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of variables\u003c/h2\u003e \u003cp\u003eThe rural living residence was defined as living in Luwero, a rural part of the country located 70Km from Kampala and outside municipalities characterized by a low population, large amounts of undeveloped land and a livelihood heavily dependent on peasant farming(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).All women attending health facilities in Luwero were considered to be rural living. The urban residence was comprised of women who attended Mengo hospital residing in Kampala city and its suburbs.\u003c/p\u003e \u003cp\u003eThe GDM risk factors were defined as follows: Age\u0026ge;35years, parity\u0026thinsp;\u0026ge;\u0026thinsp;5, body mass index \u0026ge;\u0026thinsp;30Kg/m\u003csup\u003e2\u003c/sup\u003e, height\u0026lt;150cm, history of hypertension, history of poor pregnancy outcomes (abortion, fetal loss), history of delivering a baby\u0026ge;4Kg, history of previous GDM, history of pregnancy induced hypertension, history of delivering twins(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) and history of smoking(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eData extracted from patient charts/files was coded and entered into epi data manager and exported into STATA version 16 (Statacorp LLC, College Station, Texas, United States of America) for analysis. The socio-demographic and clinical characteristics were described in terms of proportions, percentages, median and interquartile ranges. The Mann-Whitney test was used to compare continuous variables while the Chi-square test was used to compare categorical ones between urban and rural living residences. We employed a Modified Poisson regression analysis model to investigate the relationship between GDM risk factors and rural living residence since the prevalence of the risk factors was not uncommon (i.e., \u0026gt;\u0026thinsp;20%). Bi-variable analysis was used to determine the unadjusted prevalence ratios. The multi-variable model was used to include the variables with p\u0026thinsp;\u0026le;\u0026thinsp;0.20. Adjusted prevalence ratios were calculated along with their p-values and 95% CIs. At the two-tailed level, the results were considered statistically significant when the p-value was less than 0.05\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSocio-demographic and clinical characteristics of the women with GDM in urban and rural health facilities in Central Uganda\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 600 women with GDM were included in this study and their median age was 27(IQR 21-32). Urban women were older than their rural counterparts(p\u0026lt;0.001). Women in rural areas were less educated than urban ones(p\u0026lt;0.001). Regarding occupation, rural women were mainly peasant farmers, house wives or not employed at all compared to those in urban areas(p\u0026lt;0.001) (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Social-demographic \u0026amp; clinical characteristics of women with GDM in urban and rural health facilities in Central Uganda\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban(n=300)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural(n=300)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll(N=600)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;P-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eMedian Age in years (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e29(25-33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e22(19-30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e27(21-32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e6(10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e50(89.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e56(9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e28(13.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e177(86.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e205(34.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eO-level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e83(58.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e58(41.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e141(23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eA-level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e39(81.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e9(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e48(8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eTertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e144(96.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e6(4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e150(25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eFarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e16(12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e108(87.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e124(20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eBusiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e113(84.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e21(15.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e134(22.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eTeacher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e37(82.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e8(17.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e45(7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eHouse wife\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e70(45.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e83(54.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e153(25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e8(9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e80(90.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e88(14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e46(82.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e10(17.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e56(9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e25(20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e100(80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e125(20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e270(57.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e198(42.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e468(78.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e5(71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2(28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e7(1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline blood glucose\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026lt;5.5mmol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e204(41.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e284(58.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e488(81.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e5.5-6.99mmol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e87(85.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e15(14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e102(17.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026ge;7mmol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e9(90.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1(10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e10(1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIQR Interquartile range, n frequency\u003c/p\u003e\n\u003cp\u003eRural women were less heavy and shorter than their urban counter parts (p\u0026lt;0.001). Correspondingly, rural women tended to be more overweight than urban ones. The median fasting blood glucose, 1-hour blood glucose and 2-hour blood glucose values were 5.8mmol/l (IQR5.5-6.3),10.6mmol/l (IQR 9.4-10.5) and 8.7mmol/l (IQR 8-9.1) respectively (\u003cstrong\u003eTable 2).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Medians for clinical characteristics of women with GDM in urban and rural health facilities in Central Uganda\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable (Units)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban(n=300)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural(n=300)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll(N=600)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;P-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2(1-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e3(3-6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2(1-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eWeight (Kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e69(60-74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e59(54-65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e62(56-71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eHeight(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e167(162-170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e153(147.75-159.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e160(152-168)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eBMI(Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e24(20.73-27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e25.15(23-27.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e24.66(22-27.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e113(103.5-121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e110(100-120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e110(101-120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e69.5(63-76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e67(60-72.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e69(61-75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eBaseline FBG (mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e5.1(4.5-5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e4.4(3.9-4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e4.7(4.2-5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;FBG at OGTT (24-28 WOA) mmo/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.9(5.5-6.5)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.8(5.4-6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.8(5.5-6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;1-hr BG at OGTT (24-28 WOA) mmol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e10.8(9.8-10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e10.3(9.4-10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e10.6(9.4-10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e2-hr BG at OGTT (24-28 WOA) mmo/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e8.9(8.1-9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e8.3(7.8-8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e8.7(8-9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;IQR Interquartile range, n frequency, Kg Kilogram, BMI Body Mass Index, SBP Systolic Blood Pressure, DBP Diastolic Blood Pressure Fasting Blood Glucose, BG Blood Glucose, OGTT Oral Glucose Tolerance Test, WOA Weeks of Amenorrhea\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferences in proportions of risk factors of GDM among women with GDM in urban and rural health facilities in Central Uganda\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, urban women tended to be 35years and older 53(61.6%) compared to their rural counterparts 33(38.4%), p=0.02. Regarding parity, rural women 96(67.6%) delivered 5 children and more compared to urban women 46(32.4%), p\u0026lt;0. 001.However, obesity was higher among urban women than among rural women 46(64.8%) vs 25(35.2%) respectively. A short stature of less than 150cm was more prevalent among rural women than among urban ones (125(93.3%) vs 9 (6.7%) respectively, p\u0026lt;0.001. Family history of diabetes was reported more among rural women 222(55.5%) than urban ones 178(44.5%), p\u0026lt;0. 001.Regarding other risk factors, history of diabetes in the mother(p\u0026lt;0.001), previous GDM history(p=0.001) were more among urban women than rural ones\u003cstrong\u003e\u0026nbsp;(Table 3).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Proportions of risk factors for GDM among women in urban \u0026amp; rural health facilities in Central Uganda\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable (Units)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 125px;\"\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 valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en=300\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en=300\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=600\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eAge\u0026ge;35years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e53(61.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e33(38.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e86(14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eParity\u0026ge;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e46(32.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e96(67.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e142(23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eBMI(\u0026ge;30Kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e46(64.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e25(35.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e71(11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eHeight \u0026lt;150cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e9(6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e125(93.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e134(22.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eHistory of hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e171(61.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e107(38.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e278(46.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eHistory of poor pregnancy outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e161(48.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e173(51.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e334(55.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eHistory of delivery of a baby \u0026ge; 4Kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e121(49.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e122(50.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e243(40.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003ePrevious GDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e114(60.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e76(40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e190(31.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eHistory of pre-eclampsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e68(56.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e53(43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e121(20.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eHistory of twin pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e135(54.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e113(45.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e248(41.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eFamily history of DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e178(44.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e222(55.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e400(66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e31(57.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e23(42.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e54(9.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;n frequency, Kg Kilogram, BMI Body Mass Index, GDM Gestational Diabetes, DM Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eThere were no urban-rural disparities regarding the following risk factors; history of poor pregnancy outcomes(p=0.324), history of delivering a baby \u0026ge; 4Kg(p=0.934), history of pre-eclampsia(p=0.127), history of twin pregnancy(p=0.068) and smoking(p=0.254).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGestational Diabetes risk factors significantly associated with rural living residence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom the Modified Poisson regression model for multi-variable analysis\u003cstrong\u003e(Table 4)\u003c/strong\u003e,rural living residence was \u0026nbsp;positively and significantly associated with history of hypertension(APR=1.343,95% CI 1.164-1.55,P\u0026lt;0.001),history of previous gestational diabetes(APR=1.391,95%C.I 1.208-1.60,P\u0026lt;0.001),family history of diabetes(APR=1.343,95% C.I 1.158- 1.557,P\u0026lt;0.001).Furthermore, women with \u0026nbsp;parity \u0026ge;5 were 31.6% more likely to be rural (APR=1.316,95% CI 1.261-1.584,P\u0026lt;0.001) while the likelihood of having a height \u0026nbsp;\u0026lt;150cm was more than 38% with rural women than urban ones (APR=1.381,95% C.I 1.228-1.612,P\u0026lt;0.001). Women in the rural areas were 31% less likely to be 35years and older (APR=0.687 (0.558\u0026ndash; 0.847, P\u0026lt;0.001). There was no association between rural living residence and BMI, history of delivering a big baby, history of poor pregnancy outcomes, twin pregnancy and pre-eclampsia.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 4: Gestational Diabetes risk factors associated with rural living residence among women with GDM on Modified Poisson Regression Analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCPR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\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 colspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eMaternal age (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026lt;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026ge;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.584 (0.454 \u0026ndash; 0.751)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.687 (0.558\u0026ndash; 0.847)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026ge;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.481(1.301 \u0026ndash; 1.624)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.316 (1.261 \u0026ndash; 1.584)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eMaternal height (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026ge;150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026lt;150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.349 (1.201- 1.512)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.381 (1.228 \u0026ndash; 1.612)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.535 (1.305 \u0026ndash; 1.807)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.343 (1.164 \u0026ndash; 1.550)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003ePrevious GDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.323 (1.130 \u0026ndash; 1.548)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.391 (1.208 \u0026ndash; 1.600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eFamily history of diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.371 (1.173 \u0026ndash; 1.602)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.343 (1.158 \u0026ndash; 1.557)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eBody Mass Index (Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026lt;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026ge;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.349 (1.112 \u0026ndash; 1.637)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eHistory of big baby\u0026ge;4Kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.993 (0.843 \u0026ndash; 1.169)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eHistory of preeclampsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.160 (0.967 \u0026ndash; 1.393)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eTwin pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.000 (0.788 \u0026ndash; 1.269)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eHistory of maternal poor outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.084 (0.924 \u0026ndash; 1.272)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCPR Crude Prevalence Ratio, APR Adjusted Prevalence Ratio, CI Confidence Interval, GDM Gestational Diabetes\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study was aimed at establishing the association between rural living residence and risk factors of GDM. History of hypertension, previous gestational diabetes, family history of diabetes, parity\u0026thinsp;\u0026ge;\u0026thinsp;5, and height\u0026lt;150cm were positively and significantly associated with rural living residence. We also showed that age \u0026ge;\u0026thinsp;35years was less likely associated with rurality.\u003c/p\u003e \u003cp\u003eOur study was conducted in Luwero district, a rural area that was formerly ravaged by the Bush war from 1981\u0026ndash;1986 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).Some of the problems of civil conflicts are severe malnutrition and famine exposure(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).Participants in our study are grand offsprings of women who were exposed to inutero and childhood malnutrition during the civil conflict. We found that our participants from the rural area were smaller and shorter with higher BMIs than their urban counterparts. This finding could be explained by the Developmental Origins of Heath and Disease (DoHad) hypothesis which links prenatal malnutrition with Non-Communicable Disease risk (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).Similarly, a community based Ethiopian survey in rural Ethiopia mooted that chronic malnutrition as a result of prolonged famine in that area led to a relatively high prevalence of GDM (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, rural women were less educated and were involved in less professional jobs (i.e. peasant farming, house wifehood). It has been shown that a low education level, non-professional occupation and early age at first birth are associated with a high fertility rate (high parity) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eLei Y et al 2025 showed that educational level, employment status, career advancement aspirations, and age-related anxiety were significantly associated with delayed childbearing (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). This could explain why our rural study participants had a higher parity and were less likely to be older than 35 years of age.\u003c/p\u003e \u003cp\u003eIn the current study, we showed that history of hypertension, previous GDM and family history of diabetes were associated with rural living. Our findings collate the recently conducted WHO NCD STEPS survey of 2023 which showed that there were more significant increases in the prevalence of some risk factors in rural areas than urban ones in the last decade namely sedentariness p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, high blood glucose p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and overweight/obesity p\u0026thinsp;\u0026lt;\u0026thinsp;0.001(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).The same survey showed that the prevalence of hypertension in rural and urban areas is still high and has not changed in the last 10 years. The highly prevalent NCD risk factors in the rural areas are potentially explained by the increasing urbanization and westernization of rural communities in middle and low-income countries including Uganda (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Urbanization is associated with consumption of unhealthy diets, excessive alcohol and sedentary life style that predispose to central obesity leading to insulin resistance (\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Insulin resistance underpins the development of hypertension, type 2 diabetes(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) and gestational diabetes (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).Conversely, overweight,(BMI \u0026ge;\u0026thinsp;25Kg/M\u003csup\u003e2\u003c/sup\u003e,family history of diabetes, non-white ethnicity, multiparity and older maternal age are significant risk factors for progression to diabetes after GDM(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).All these determinants are prevalent among our rural participants, this could partly explain why diabetes has increased in the rural areas in Uganda as depicted by the 2023 WHO NCD STEPS survey (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).It is known that intrauterine exposure to the metabolic environment of maternal diabetes, or GDM, is linked with increased risk of altered glucose homeostasis(impaired fasting glucose, impaired glucose tolerance, and T2D) in the offspring, starting in childhood leading to a higher prevalence of diabetes in the next generation(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).With erratic selective GDM screening in the country, there is a missed opportunity to diagnose and treat T2D among post GDM women.\u003c/p\u003e \u003cp\u003eSimilar to our findings, several studies have shown that women living in rural areas give birth at a young age and have lower education status (\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)and higher BMIs(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Additionally, a study done by Graham et al 2007 showed a higher proportion of rural women with pre-existing and or hypertension and or diabetes compared to urban ones (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Whereas some studies have investigated the prevalence of GDM and its determinants in rural areas (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), they have not shown whether these are associated with rural living residence as is the case for our study.\u003c/p\u003e \u003cp\u003eThe current study highlights GDM risk factors that are associated with rural living residence. They resonate with the NCD risk factors that have increased in rural Uganda in the last 10 years. Typically, the incidence of GDM mirrors the incidence of T2D in the background population (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).Therefore, universal screening of all expectant women for GDM provides a window of opportunity to screen, diagnose and manage non-communicable diseases including T2D and other cardiovascular disease conditions early. Our findings may potentially underpin change in policy, i.e. from selective to universal GDM screening.\u003c/p\u003e \u003cp\u003eThe strengths of our study are worth mentioning, to our knowledge this is the first study in Uganda to evaluate the association between GDM risk factors and rural living residence. Secondly, we had robust data on risk factors of GDM to delineate their association with rurality. However, being a retrospective study, not all relevant information and data could be collected for all participants. Nevertheless, our study yielded pertinent data on the GDM risk factors and their association with rural living residence.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current study has shown that rural living residence is associated with GDM risk factors i.e. history of hypertension, previous gestational diabetes, family history of diabetes, parity\u0026thinsp;\u0026ge;\u0026thinsp;5, and height\u0026lt;150cm. It has also shown that age \u0026ge;\u0026thinsp;35years is less likely associated with rurality. Our study has provided data that may be potentially useful in designing context specific screening and management protocols for GDM in the country already faced with low screening rates. Furthermore, these factors being determinants of progression to T2D post GDM, our study findings highlight the opportunity to screen and manage T2D and other non-communicable diseases in our rural settings where diabetes has increased significantly in the last 10 years.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADA: American Diabetes Association\u003c/p\u003e\n\u003cp\u003eAPR: Adjusted Prevalence Ratio\u003c/p\u003e\n\u003cp\u003eBG: Blood Glucose\u003c/p\u003e\n\u003cp\u003eBMI: Body Mass Index\u003c/p\u003e\n\u003cp\u003eCPR: Crude Prevalence Ratio\u003c/p\u003e\n\u003cp\u003eDBP: Diastolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eDM: Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eDOHad: Developmental Origins of Heath and Disease\u003c/p\u003e\n\u003cp\u003eFBG: Fasting Blood Glucose\u003c/p\u003e\n\u003cp\u003eGICU: Gestational Diabetes in Central Uganda\u003c/p\u003e\n\u003cp\u003eIADSPG: International Association of Diabetes and Pregnancy Study Groups\u003c/p\u003e\n\u003cp\u003eIQR: Interquartile Range\u003c/p\u003e\n\u003cp\u003eNCD: Non-Communicable Diseases\u003c/p\u003e\n\u003cp\u003eOGTT: Oral Glucose Tolerance Test\u003c/p\u003e\n\u003cp\u003eRHU: Reproductive Health Uganda\u003c/p\u003e\n\u003cp\u003eSBP: Systolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eUDA: Uganda Diabetes Association\u003c/p\u003e\n\u003cp\u003eWHO: World Health Organization\u003c/p\u003e\n\u003cp\u003eWOA: Weeks of Amenorrhea\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent of participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this retrospective comparative cross-sectional study, we obtained a waiver of informed consent and approval from Mengo Hospital Research EthicsCommittee (approvalnumber MH 136/07-2025). We de-identified selected files and used codes to ensure participants’ confidentiality. The study was conducted in observance of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets used and or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;No funding was obtained to carry out this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWL, SN, RKM, RBN, SPN conceptualized the study and designed the methodology. RS, RBN, and WL developed the statistical plan for the study. RKM, with the help of RS and WL, analyzed the data. WL, SN, RKM, RBN, DM, GN, HN, RM, SN, EN, RS, AK and SPN designed the manuscript and revised and approved its final version.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to extend our appreciation to the Department of Endocrinology and Non-Communicable Diseases, the Hospital Management Committee of Mengo Hospital and the research assistants for their support during this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eManagement of Diabetes in Pregnancy: Standards of Care in Diabetes\u0026mdash;2024. Diabetes Care [Internet]. 2023 Dec 11;47(Supplement_1): S282\u0026ndash;94. Available from: https://doi.org/10.2337/dc24-S015\u003c/li\u003e\n\u003cli\u003eMetzger BE, Gabbe SG, Persson B, Buchanan TA, Catalano PA, Damm P, et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010 Mar;33(3):676\u0026ndash;82. \u003c/li\u003e\n\u003cli\u003eVeeraswamy S, Vijayam B, Gupta VK, Kapur A. Gestational diabetes: the public health relevance and approach. Diabetes Res Clin Pract. 2012 Sep;97(3):350\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eAbindu V, Hope D, Aleni M, Andru M, Ayiasi RM, Afayo V, et al. Missed Diagnosis of Gestational Diabetes Mellitus Due to Selective Screening: Evidence from a Cross-Sectional Study in the West Nile Sub-Region, Uganda. Diabetes Metab Syndr Obes. 2024; 17:1309\u0026ndash;19. \u003c/li\u003e\n\u003cli\u003eUganda Clinical Guidelines 2016: Kampala: Ministry of Health Uganda \u003c/li\u003e\n\u003cli\u003eVenkatesh KK, Huang X, Cameron NA, Petito LC, Joseph J, Landon MB, et al. 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Changes in the prevalence of the common risk factors for non-communicable diseases in Uganda between 2014 and 2023: Informed by nationally representative cross-sectional surveys. medRxiv [Internet]. 2024 Jan 1;2024.09.04.24313080. Available from: http://medrxiv.org/content/early/2024/09/05/2024.09.04.24313080.abstract\u003c/li\u003e\n\u003cli\u003eZeck W, McIntyre HD. Gestational diabetes in rural East Africa: a call to action. J Womens Health (Larchmt). 2008 Apr;17(3):403\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eCharan, J., \u0026amp; Biswas, T. (2013). How to calculate sample size for different study designs in medical research? Indian journal of psychological medicine, 35(2), 121\u0026ndash;126. doi:10.4103/0253-7176.116232No Title. \u003c/li\u003e\n\u003cli\u003eMdoe MB, Kibusi SM, Munyogwa MJ, Ernest AI. Prevalence and predictors of gestational diabetes mellitus among pregnant women attending antenatal clinic in Dodoma region, Tanzania: an analytical cross-sectional study. 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Women Birth. 2014 Dec;27(4):281\u0026ndash;91. \u003c/li\u003e\n\u003cli\u003eKiiza F, Kayibanda D, Tumushabe P, Kyohairwe L, Atwine R, Kajabwangu R, et al. Frequency and Factors Associated with Hyperglycaemia First Detected during Pregnancy at Itojo General Hospital, South Western Uganda: A Cross-Sectional Study. J Diabetes Res. 2020; 2020:4860958. \u003c/li\u003e\n\u003cli\u003eKahimakazi I, Tornes YF, Tibaijuka L, Kanyesigye H, Kiptoo J, Kayondo M, et al. Prevalence of gestational diabetes mellitus and associated factors among women receiving antenatal care at a tertiary hospital in South-Western Uganda. Pan Afr Med J. 2023; 46:50. \u003c/li\u003e\n\u003cli\u003eNatamba BK, Namara AA, Nyirenda MJ. Burden, risk factors and maternal and offspring outcomes of gestational diabetes mellitus (GDM) in sub-Saharan Africa (SSA): a systematic review and meta-analysis. BMC Pregnancy Childbirth [Internet]. 2019;19(1):450. 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BMC Res Notes. 2017 May;10(1):174. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gestational diabetes, risk factors, rural area, Mengo hospital, Central Uganda","lastPublishedDoi":"10.21203/rs.3.rs-8775786/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8775786/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGestational diabetes (GDM) is a major public health problem. The risk factors of GDM are not the same in gravida residing in different geographical areas due to rural-urban differences in maternal metabolic health and risk factors of diabetes. The study aimed to assess the association between rural living residence and GDM risk factors in Central Uganda.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis is a retrospective comparative cross-sectional study in which data from 600 women with GDM from 2016\u0026ndash;2018 was extracted, with 300 from Luwero district(rural) and 300 from Mengo hospital(urban). Data on socio-demographics, clinical characteristics and risk factors was collected. Diagnosis of GDM was based on the IADSPG/ADA one step approach using a 75g OGTT (Oral Glucose Tolerance Test). The risk factors for GDM were defined using standard methods. The socio-demographic and clinical characteristics were described appropriately depending on their distribution. The Modified Poisson regression analysis model was used to explore the association between rural living residence and GDM risk factors expressed as adjusted prevalence ratios with their 95% confidence intervals and p-values. Statistical significance was set at \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall,600 women with GDM were included (n\u0026thinsp;=\u0026thinsp;300 urban, n\u0026thinsp;=\u0026thinsp;300 rural). The median age was 27years (IQR 21\u0026ndash;32), with rural women younger than urban ones(P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Rural women were shorter (\u0026le;\u0026thinsp;150cm) P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, had family history of diabetes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and parity (\u0026ge;\u0026thinsp;5) P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 compared to their urban counterparts. Rural living residence was positively and significantly associated with history of hypertension(APR\u0026thinsp;=\u0026thinsp;1.343,95% CI 1.164\u0026ndash;1.55,P\u0026thinsp;\u0026lt;\u0026thinsp;0.001),history of GDM (APR\u0026thinsp;=\u0026thinsp;1.391,95% C.I 1.208-1.60,P\u0026thinsp;\u0026lt;\u0026thinsp;0.001),family history of diabetes(APR\u0026thinsp;=\u0026thinsp;1.343,95% C.I 1.158\u0026ndash;1.557,P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).Women with parity\u0026thinsp;\u0026ge;\u0026thinsp;5 were 31.6% more likely to be rural (APR\u0026thinsp;=\u0026thinsp;1.316,95% CI 1.261\u0026ndash;1.584,P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The likelihood of having a height \u0026lt;\u0026thinsp;150cm was more than 38% with rural women than urban ones (APR\u0026thinsp;=\u0026thinsp;1.381,95% C.I 1.228\u0026ndash;1.612, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Women in rural areas were 31% less likely to be older than 35years APR\u0026thinsp;=\u0026thinsp;0.687 (0.558\u0026ndash; 0.847, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe found that rural living residence is associated with risk factors of gestational diabetes. With increasing burden of diabetes in rural areas, universal screening for GDM may provide a window of opportunity to detect and manage glucose abnormalities including intermediate hyperglycemia and type 2 diabetes in rural clinical settings.\u003c/p\u003e","manuscriptTitle":"Rural Living Residence is Associated with Risk Factors of Gestational Diabetes: A Retrospective Comparative Cross-sectional Study in Central Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 18:24:46","doi":"10.21203/rs.3.rs-8775786/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-12T04:00:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294493835624730664886543763492190935069","date":"2026-03-12T03:00:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127172464845399962903136990674216906645","date":"2026-03-10T22:19:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-10T13:01:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-09T09:08:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-16T03:59:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-16T02:18:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2026-02-16T02:15:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e36f36e6-3e3d-4295-b9cc-e61a4018c405","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-13T18:24:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 18:24:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8775786","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8775786","identity":"rs-8775786","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}