{"paper_id":"a1921711-f68e-476c-94d3-5c56190de22e","body_text":"Preterm birth determinants among newborns at six public hospitals in Northeast Amhara, Ethiopia: unmatched case-control study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Preterm birth determinants among newborns at six public hospitals in Northeast Amhara, Ethiopia: unmatched case-control study Mesfin Shiferaw, Nigusie Birhan Tebeje, Habtamu Sewunet Mekonnen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-36781/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Preterm birth is a global public health problem. Worldwide, every year about 15 million newborns are born preterm and about 1.1 million of them die due to complications related to prematurity. More than 81% of preterm births were in Asia and Sub-Saharan Africa. Nowadays, preterm birth is the first leading cause of under-five mortality and is one of the limiting factors for achieving a reduction in under-five mortality rate. Therefore, the aim of this study was to identify the determinants of preterm birth at public hospitals in the Northeast Amhara, Ethiopia, 2019. Method: Hospital-based unmatched case-control study was conducted at public hospitals in Northeast Amhara, Ethiopia from March 15 th to April 25 th , 2019. Data were collected using interviewer-administered structured pre-tested tool and document review for cases and controls. Data were entered in to Epi Info version 7.2.2.6 and exported to SPSS version 23 software for data analysis. Bi-variable and multivariable binary logistic regression were used to measure the association between preterm birth and risk factors. Adjusted Odds ratio (AOR) at 95 % confidence interval (CI) was calculated and P value ≤0.05 declared statistically significant. Result: The study identified that ANC follow up less than four visits [AOR 2.267, Cl (1.205, 4.264)], pregnancy-induced hypertension [AOR 4.453, Cl (1.276, 15.536)], no iron/folic acid supplementation during pregnancy [AOR 4.175, Cl (1.632, 10.680)], anemia during pregnancy [AOR 4.776, Cl (1.492, 15.285)], and birth defect [AOR 6.160, Cl (l.896, 20.013)] were significantly associated determinants of preterm birth. Conclusion: Antenatal care follow up fewer than four visits, pregnancy induced hypertension, no iron/folic acid supplementation during pregnancy, being anemic during pregnancy, and birth defect were important determinants of preterm birth. Sexual & Reproductive Medicine Determinants preterm birth newborns public hospitals northeast Ethiopia Plain English Summary Preterm birth is a global public health problem. Nowadays, preterm birth is the first leading cause of under-five mortality and is one of the limiting factors for achieving a reduction in under-five mortality rate. The aim of this study was to identify the determinants of preterm birth at public hospitals in the Northeast Amhara, Ethiopia. Hospital-based unmatched case-control study was conducted from March 15th to April 25th, 2019. Data were collected using interviewer-administered structured pre-tested tool and document review for cases and controls. Antenatal care follow-up less than four visits, pregnancy-induced hypertension, no iron/folic acid supplementation during pregnancy, anemia during pregnancy, and birth defect were found significantly associated determinants of preterm birth. Health education and awareness creation about the benefit of adequate ANC follow, iron/folic acid consumption, and early prevention of pregnancy-related complications are paramount important during pregnancy. Background Preterm birth (PTB), commonly defined as births before 37 completed weeks of gestation or fewer than 259 days since the first day of a woman's last normal menstrual period (LNMP) ( 1 ). The magnitude of PTB shows variation among different parts of the world and is growing through time. Globally, it is estimated that 15 million babies are born too soon each year and the majority of PTBs were in Asia and Sub-Saharan Africa where health systems are weak in access and minimum health services utilization. Additionally, preterm complications account for more than one million neonatal deaths each year( 2 , 3 ). From 11 countries which have greater than 15% prevalence of prematurity, all countries are found in Sub-Saharan African except Pakistan and Indonesia. Besides, poor countries have a higher prevalence of preterm births than rich countries ( 4 ). In Ethiopia, the overall PTB rate is 12% and the prevalence of PTB in different parts of the country ranges from 4.4–25.9% ( 2 , 5 , 6 ) For the first time in history, PTB is the leading cause of death in children younger than 5 years worldwide covering 17% of under-five and 35% of neonatal deaths( 7 – 9 ). Premature infants are particularly vulnerable to complications due to impaired respiration, difficulty in feeding, poor body temperature regulation and high risk of infection. Babies who are born before they are physically ready to face the world encounter greater risks of serious health problems, including cerebral palsy, intellectual impairment, chronic lung disease, and vision and hearing loss( 4 , 10 ). Premature babies also face drastically different odds of death depending on where they are born. From those extremely preterm babies (< 28 weeks) born in developing countries, greater than 90% of them die, while greater than 90% of extremely preterm babies in developed countries survive( 4 ). Among the 10 countries with the highest numbers of babies dying from PTB complications, India is the first followed by Nigeria. Ethiopia takes the 8th place with 24,400 child deaths per year ( 9 ). Different interventions have been implemented to prevent and improve the survival of PTB which demands huge financial capacity imposing a huge economic burden for the family, the community and the nation at large ( 4 , 11 ). Every Women Every Child initiative set a target for 50% reduction in PTB deaths by 2025 which needs governments and the global community to take direct action to address deaths due to preterm birth, otherwise, the global total of preterm deaths will not reduce significantly, with around 900,000 premature babies continuing to die every year by 2025( 12 ). Several risk factors for PTB have been identified across the world. The potential risk factors for PTB includes, but not limited, maternal risk factors like past adverse birth outcomes(PTB, stillbirth, LBW), obstetric complications(PIH, PROM), maternal infections(HIV, malaria, UTI) and no ANC follow up while having ≥ 4 ANC checkup, IPI 24–36 months, maternal age 20–35 were found to be protective for PTB ( 13 – 15 ). However, risk factors for PTB are not always global; risk assessment should be carried out progressively at local, regional, and then global levels( 16 ). In poorer countries, like Ethiopia, where fewer resources are destined for healthcare, the direct, indirect, immediate and later repercussions of prematurity constitute a relevant public health issue. However, the great majority of scientific evidence accumulated on this subject is generated in developed countries. Besides, the potential risk factors are varied in different locations. Therefore, this study will have its own contribution to avert PTB and its consequences by identifying risk factors. Method Study design and period A facility-based unmatched case-control study was conducted at public hospitals in Northeast Amhara, Ethiopia from March 15th to April 25th, 2019. Study Area The study was conducted in Northeast part of Amhara region, Ethiopia. The study was employed in six randomly selected governmental hospitals (Debre Birhan Referral hospital, Tarmaber Primary Hospital, Kemisie General Hospital, Dessie Referral Hospital, Borumeda Hospital, and Woldia general Hospital). Source Population All newborns who were born in public hospitals of Northeast Amhara, Ethiopia. Study Population All newborns who were born in selected public hospitals of Northeast Amhara, during data collection time. Inclusion And Exclusion Criteria Inclusion criteria Cases All alive singleton preterm newborns (< 37 completed weeks of gestation) who was born in study hospitals of northeast Amhara. Controls All alive singleton term newborns (37 completed weeks to 42 weeks) at the same hospitals. Exclusion Criteria Newborns whose mothers did not remember their LMP. Newborns whose mothers had a serious illness or with difficulty of communication during the study period. Sample Size Determination Sample size was determined by using EPI info version 7.2.2.6 by a double-population proportion difference formula. Different variables that have a high contribution for PTB was used and the variable that yields the highest sample size was selected. The sample size was calculated considering the following assumptions: r = 3 (ratio of controls to cases), 80% power and 95% confidence level. ANC < 4 with P Control = 42%, P Case = 60.7% was selected. Based on this, the sample size was 323. By considering a 10% non-response rate, the final sample size was 356 with 89 cases and 267 controls. Sampling Technique The total sample size was allocated proportionately according to their caseloads for selected hospitals. Cases were preterm newborns (gestational age < 37 completed weeks) and controls were term newborns (gestational age of ≥ 37 weeks to 42 weeks). Systematic sampling technique was employed to select controls and cases every fifth intervals and every two intervals respectively. The first participant was selected by lottery method. Study Variables Dependent variable Preterm birth (Yes/No) Independent Variables Socioeconomic factors (including paternal age & education), obstetric history related factors, history of adverse birth outcomes, Obstetric complications, maternal medical conditions, behavioral factors, maternal anthropometrics, and infant characteristics. Operational Definition Preterm birth Newborns born before 37 completed weeks of gestation underweight Mothers with MUAC less than 23 cm ( 17 , 18 ) Alcohol drinking ( 19 , 20 ) A unit of alcohol It is equivalent to 17.7 ml or 14 g of pure strength alcohol Binge drinkers mothers who drink ≥ 3 drinks at a time/per seat Drinkers: mothers who use at least one unit of any alcohol during their pregnancy categorized into three levels: Passive smoking living with someone who smoked during the mother's pregnancy Data Collection Tools And Procedures Data was collected using interviewer-administered, structured pretested questionnaires and record review. The questionnaires include data on socio-demographic characteristics, obstetrics characteristics, infant characteristics, maternal anthropometrics, medical disorders, maternal infections, and behavioral factors. The questionnaires were prepared for this study by reviewing different kinds of literature from related studies and EDHS 2016, ( 14 , 15 , 21 – 23 ). Additional file Confirmation of Gestational age was taken from documentation and for non-recorded data, information was taken from mother’s report of LNMP. The data was collected by six trained midwifery graduates and supervised by MSc nurses. MUAC was measured by using flexible non- stretched tape taken at mid-point of left upper arm at relaxed position, without any clothing and with optimal tape tension between acromion process on shoulder blade and tip of olecranon process of ulna. Height was also measured using height Stadiometer with sliding head bar without shoes, standing erect, with shoulder blades, buttocks and heels touching the measuring board, looking straight ahead, shoulders relaxed, arms at sides, legs straight and knees together, feet flat and with heels together. Two height measurements were taken and average was used if readings are within 1 cm each other and record to the nearest 1 cm. Data Quality Control Data collectors and supervisor were trained for half a day on objective and methods of the study, study tools, how to approach the patient, and take anthropometric measures. In order to assess the validity and reliability of the tool and clarity of the variable, pre-test was done on 5% of the sample size newborns who were delivered out of the study hospitals two weeks ahead of data collection. During the data collection process, regular monitoring and supervision of the overall activity was done by the supervisors and principal investigator to check for completeness and ensure the quality of data. Besides, data cleaning and coding was carried out before analysis. The measurement tool was prepared in English and translated into local language (Amharic). Data Processing And Analysis The collected data were checked for completeness and entered into EPI info version 7.2.2.6 and exported to SPSS version 23 for analysis. Data were summarized using text, tables, and graphs. Model goodness of fit test was checked by Hosmer-Lemeshow test (P-value = 0.224) and fitted. Multicollinearity between independent variables was assessed by using VIF (1.1–1.27) and no problem detected. Binary logistic regression model was used to measure the association between preterm birth and risk factors. Variables that had P-value ≤ 0.2 on bi-variable regression analysis and had association with preterm birth in previous studies were fitted into the multivariable logistic regression model to assess their association with the outcome variable. Adjusted Odds ratio (AOR) with 95% confidence interval (CI) was calculated and P value ≤ 0.05 declared statistically significant. Result Socio‑demographic characteristics of respondents In this study, 356 participants were included. From these participants, 89 were preterm newborns (cases) and 267 were term newborns as controls with 100% response rate. The median age of mothers was 25 ± 8 (IQR) years in case group and 26 ± 7 (IQR) years in control group with a minimum of 17 and a maximum of 45 years. The paternal median age was 31 ± 7 (IQR) years ranges from 20 to 60 years. Regarding religion, 50 (56.2%) cases and 129 (48.3%) controls were Orthodox Christian. Majority of the respondents, 69 (77.5%) cases and 220 (82.4%) controls were married and living together. Concerning the educational level of parents, 34 (38.2%) mothers and 27 (30.4%) fathers in the case group had no formal education while 108 (44.4%) mothers and 89 (41.1%) fathers in the control group. About occupation, 46 (51.7%) cases and 132 (49.4%) controls were housewives. Forty-eight (53.9%) newborns in case-group and 148 (55.4%) newborns in control-group were male in sex. (Table 1 ) Table 1 Distribution of socio-demographic characteristics among mothers who gave birth at public hospitals of Northeast Amhara, Ethiopia, 2019 Variable Category Cases (%) Controls (%) Total Residence Rural 42 (47.2%) 98 (36.7%) 140 (39.3%) Urban 47 (52.8%) 169 (63.3%) 216 (60.7%) Age of mother < 20 6 (6.7%) 10 (3.7%) 16 (4.5%) 20–34 68 (76.4%) 226 (84.6%) 294 (82.6%) ≥ 35 15 (16.9% 31 (11.6%) 46 (12.9%) Educational status Not able to read and write 20 (22.5%) 58 (21.7%) 78 (21.9%) Able to read and write 14 (15.7%) 50 (18.7%) 64 (18.0%) Primary education 19 (21.3%) 58 (21.7%) 77 (21.6%) Secondary education 23 (25.8%) 60 (22.5%) 83 (23.3%) Higher education 13 (14.6%) 41 (15.4%) 54 (15.2%) Marital status Married 69 (77.5%) 220 (82.4%) 289 (81.2%) Separated 16 (18.0%) 30 (11.2%) 46 (12.9%) Single/Divorced/Widowed 4 (4.5%) 17 (6.4%) 21 (5.9%) Religion Orthodox 50 (56.2%) 129 (48.3%) 179 (50.3%) Muslim 33 (37.1%) 120 (44.9%) 153 (43.0%) Protestant 5 (5.6%) 13 (4.9%) 18 (5.1%) Catholic 1 (1.1%) 5 (1.9%) 6 (1.7%) Occupational status Housewife 46 (51.7%) 132 (49.4%) 178 (50.0%) Farmer 10 (11.2%) 30 (11.2%) 40 (11.2%) Merchant 14 (15.7%) 40 (15.0%) 54 (15.2%) Government/NOGs employee 19 (21.3%) 65 (24.3%) 84 (23.6%) Educational status of father Not able to read and write 11 (12.4%) 45 (16.9%) 56 (15.7%) Able to read and write 16 (18.0%) 54 (20.2%) 70 (19.7%) Primary education 21 (23.6%) 49 (18.4%) 70 (19.7%) Secondary education 25 (28.1%) 62 (23.2%) 87 (24.4%) Higher education 16 (18.0%) 57 (21.3%) 73 (20.5%) Age of father < 25 5 (5.6%) 10 (3.7%) 15 (4.2%) 25–29 30 (33.7%) 63 (23.6%) 93 (26.1%) 30–34 25 (28.1%) 95 (35.6%) 120 (33.7%) 35–39 16 (18.0%) 49 (18.4%) 65 (18.3%) ≥ 40 13 (14.6%) 50 (18.7%) 63 (17.7%) Income (monthly) < 3000 Birr 52 (58.4%) 130 (48.7%) 182 (51.1%) ≥ 3000 Birr 37 (41.6%) 137 (51.3%) 174 (48.9%) Infant sex Male 48 (53.9%) 148 (55.4%) 196 (55.1%) Female 41 (46.1%) 119 (44.6%) 160 (44.9%) Maternal Obstetric Related Factors In this study, 66 (74.2%) cases and 113 (42.3%) controls had fewer than four ANC visits. Among these, 25 (28.1%) cases and 13 (4.9%) controls had no ANC follow up. Regarding Iron/folic acid supplementation, 44 (49.4%) cases and 45 (16.9%) controls had used no Iron/folic acid. About 13 (14.6%) cases and 7 (2.6%) controls had experienced anemia during pregnancy. The majority, 66 (74.2%) newborns in case-group and 217 (81.3%) newborns in control-group were born through spontaneous vaginal delivery. Concerning, past adverse birth outcome, 8 (25%) cases and 18 (17.3%) controls had one or more past adverse birth outcomes. Past adverse birth outcomes encountered were LBW 5 (15.6%), Stillbirth 3 (9.4%), and Abortion 3 (9.4%) among cases and 6 (5.8%), 9 (8.7%), and 7 (6.7%) among controls respectively. (Table 2 ) Table 2 Maternal obstetric related factors among mothers who gave birth at public hospitals of Northeast Amhara, Ethiopia, 2019 Variable Category Cases (%) Controls (%) Total ANC Follow up No 25 (28.1%) 13 (4.9%) 38 (10.7%) < 4 41 (46.1%) 100 (37.5%) 141 (39.6%) ≥ 4 23 (25.8%) 154 (57.7%) 177 (49.7%) Time of ANC initiation First trimester 23 (35.9%) 97 (38.2%) 120 (37.7%) Second trimester 40 (62.5%) 150 (59.1%) 190 (59.7%) Third trimester 1 (1.6%) 7 (2.8%) 8 (2.5%) Birth interval No previous birth 57 (64.0%) 163 (61.0%) 220 (61.8% ) < 24 months 5 (5.6%) 19 (7.1%) 24 (6.7%) 24–36 months 20 (22.5%) 68 (25.5%) 88 (24.7%) ≥ 37 months 7 (7.9%) 17 (6.4%) 24 (6.7%) Parity Primipara 57 (64.0% ) 163 (61.0%) 220 (61.8%) Multipara 32 (36.0%) 104 (39.0%) 136 (38.2%) Iron/folic acid supplementation No 44 (49.4%) 45 (16.9% ) 89 (25.0%) 30–60 tabs 36 (40.4%) 144 (53.9%) 180 (50.6%) > 60 tabs 9 (10.1%) 78 (29.2%) 87 (24.4%) Anemia No 76 (85.4%) 260 (97.4%) 336 (94.4%) Yes 13 (14.6%) 7 (2.6%) 20 (5.6%) Hemoglobin Level < 11 g/dl 25 (28.1%) 35 (13.1%) 60 (16.9%) ≥ 11 g/dl 64 (71.9%) 232 (86.9% 296 (83.1%) Mode of delivery Spontaneous 66 (74.2%) 217 (81.3%) 283 (79.5%) Induction 18 (20.2%) 16 (6.0%) 34 (9.6%) C-section 5 (5.6%) 34 (12.7%) 39 (11.0%) Pregnancy status Unplanned 22 (24.7%) 33 (12.4%) 55 (15.4%) Planned 67 (75.3%) 234 (87.6%) 301 (84.6%) Maternal Rh factor Positive 59 (66.3%) 187 (70.0%) 246 (69.1%) Negative 25 (28.1%) 72 (27.0%) 97 (27.2%) Unknown 5 (5.6%) 8 (3.0%) 13 (3.7%) Maternal height < 150 20 (22.5%) 35 (13.1%) 55 (15.4%) ≥ 150 69 (77.5%) 232 (86.9%) 301 (84.6%) Maternal MUAC < 23 44 (49.4%) 88 (33.0%) 132 (37.1%) ≥ 23 45 (50.6%) 179 (67.0%) 224 (62.9%) History of adverse birth outcome No 24 (75.0% 86 (82.7%) 110 (80.9%) Yes 8 (25.0%) 18 (17.3%) 26 (19.1%) Type of adverse birth outcome LBW 5 (15.6%) 6 (5.8%) 11 (8.1%) Stillbirth 3 (9.4%) 9 (8.7%) 12 (8.8%) Abortion 3 (9.4%) 7 (6.7%) 10 (7.4%) Table 3 Maternal infection and chronic disease related factors among mothers who gave birth at public hospitals of Northeast Amhara, Ethiopia, 2019 Variable Category Cases (%) Controls (%) Total Chronic medical illness No 83 (93.3%) 253 (94.8%) 336 (94.4%) Yes 6 (6.7%) 14 (5.2%) 20 (5.6%) Type of medical illness HPN 3 (3.4%) 4 (1.5%) 7 (2.0%) DM/KD/LD?TD/ 4 (4.49%) 10 (3.74%) 14 (3.93%) Maternal infection UTI 10 (11.2%) 24 (9.0%) 34 (9.6%) Malaria 1 (1.1%) 4 (1.5%) 5 (1.4%) TB 1 (1.1%) 5 (1.9%) 6 (1.7%) HIV/AIDS 0 (%0) 8 (3.0%) 8 (2.2%) Key: DM = diabetes mellitus, KD = kidney disease, LD = liver disease, TD = thyroid disorder Table 4 Behavioral factors among mothers who gave birth at public hospitals of Northeast Amhara, Ethiopia, 2019 Variable Category Cases (%) Controls (%) Total Alcohol drinking during pregnancy No 62 (69.7%) 199 (74.5%) 261 (73.3%) Yes 27 (30.3%) 68 (25.5%) 95 (26.7%) Type of alcohol drunk Tella 19 (21.3%) 56 (21.0% ) 75 (21.1%) Areke 6 (6.7%) 7 (2.6%) 13 (3.7%) Beer 8 (9.0%) 24 (9.0%) 32 (9.0%) Wine/Tej 7 (7.9%) 7 (2.6%) 14 (3.9%) Frequency of drinking Daily/Weekly 9 (33.3%) 13 (19.1) 22 (23.2%) Fortnightly/Monthly 7 (25.9%) 13 (19.1%) 20 (21.0%) Occasionally 11 (40.7%) 42 (61.8%) 53 (55.8%) Unit of alcohol per week (recent month) No alcohol 65 (73.0%) 223 (83.5%) 288 (80.9%) 1–3 unit 21 (23.6%) 40 (15.0%) 61 (17.1%) ≥ 4 unit 3 (3.4%) 4 (1.5%) 7 (2.0%) Khat chewing during pregnancy No 84 (94.4%) 248 (92.9%) 332 (93.3%) Yes 5 (5.6%) 19 (7.1%) 24 (6.7%) Frequency of khat chewing Daily/Weekly 1 (20%) 4 (21.1%) 5 (20.8%) Fortnightly 0 (0%) 5 (26.3%) 5 (20.8%) Monthly 0 (0%) 1 (5.3%) 1 (4.2%) Occasionally 4 (80%) 9 (47.4%) 13 (54.2%) Passive Smoking No 87 (97.8%) 261 (97.8%) 348 (97.8%) Yes 2 (2.2%) 6 (2.2%) 8 (2.2%) Any medication during pregnancy No 74 (83.1%) 227 (85.0%) 301 (84.6%) Yes 15 (16.9%) 40 (15.0%) 55 (15.4%) Frequency of medication use Daily 12 (80.0%) 34 (85.0%) 46 (83.6%) Weekly 0 (0%) 1 (2.5%) 1 (1.8%) Occasionally 3 (20.0%) 5 (12.5%) 8 (14.5%) Table 5 Bi-variable and Multi-variable analysis of determinants associated with preterm birth among mothers who gave birth at public hospitals of Northeast Amhara, Ethiopia, 2019 Variables Category Cases (%) Controls (%) COR (95% CI) AOR (95% CI) Residence Rural 42 (47.2%) 98 (36.7%) 1.541 (0.949, 2.503) 0.998(0.521,1.912) Urban 47 (52.8%) 169 (63.3%) 1 1 Pregnancy status Unplanned 22 (24.7%) 33 (12.4%) 2.328 (1.273, 4.259) 0.995(0.442,2.242) Planned 67 (75.3%) 234 (87.6%) 1 1 ANC follow up < 4 ANC visit 66 (74.2%) 113 (42.3%) 3.911 (2.295, 6.665) 2.267(1.205,4.264)** ≥ 4 ANC visit 23 (25.8%) 154 (57.7%) 1 1 Age of mother in years < 19 6 (6.7%) 10 (3.7%) 1.240(0.379, 4.056) 1.326(0.290,6.060) 20–34 68 (76.4%) 226 (84.6%) 0.622(0.317, 1.219) 1.009(0.419,2.431) ≥ 35 15 (16.9%) 31 (11.6%) 1 1 PIH No 75 (84.3%) 261 (97.8%) 1 1 Yes 14 (15.7%) 6 (2.2%) 8.120 (3.016, 21.858) 4.453(1.276,15.536)** PROM No 77 (86.5%) 259 (97%) 1 1 Yes 12 (13.5%) 8 (3.0%) 5.045 (1.991, 12.789) 3.159(0.938,10.638) Congenital malformation No 74 (83.1%) 262 (98.1%) 1 1 Yes 15 (16.9% ) 5 (1.9%) 10.622 (3.737, 30.186) 6.160(1.896,20.013)* Iron/folic acid supplementation No iron use 44 (49.4%) 45 (16.9% ) 8.474(3.78718.962) 4.175(1.632,10.680)* 30–60 tabs 36 (40.4%) 144 (53.9%) 2.167(0.9934.730) 1.838(0.765,4.418) ≥ 60 tabs 9 (10.1%) 78 (29.2%) 1 1 Anemia during current pregnancy No 76 (85.4%) 260 (97.4%) 1 1 Yes 13 (14.6%) 7 (2.6%) 6.353 (2.448, 16.489) 4.776 (1.492, 15.285)* Hemoglobin level < 11 mg/dl 25 (28.1%) 35 (13.1%) 2.589 (1.445, 4.639) 1.880(0.925,3.821) ≥ 11 mg/dl 64 (71.9%) 232 (86.9% 1 1 Height of Mothers < 150 cm 20 (22.5%) 35 (13.1%) 1.921 (1.042, 3.542) 1.112(0.513, 2.411) ≥ 150 cm 69 (77.5%) 232 (86.9%) 1 1 MUAC < 23 cm 44 (49.4%) 88 (33.0%) 1.989 (1.221, 3.239) 1.481(0.812,2.699) ≥ 23 cm 45 (50.6%) 179 (67.0%) 1 1 Income (monthly) < 3000 EBR 52 (58.4%) 130 (48.7%) 1.481 (0.912, 2.406) 1.388(0.738,2.608) ≥ 3000 EBR 37 (41.6%) 137 (51.3%) 1 1 Key: COR = crude odds ratio, AOR = adjusted odds ratio, CI = confidence interval, *=P-value < 0.01, **=p-value ≤ 0.05 Fetal Related Factors The mean gestational age among PTBs and term births were 34 ± 1.94 weeks and 39.02 ± 1.27 weeks, respectively. In the case group, 17 (19.1%) newborns were small for gestational age whereas 12 (4.5%) newborns in control group were low birth weight. Concerning fetal distress, 5 (5.6%) cases and 15 (5.6%) controls had fetal distress. On the other hand, 15 (16.9%) preterm newborns and 5 (1.9%) term newborns had a visible congenital/birth defect. Current Obstetric Complications During current pregnancy, about 23 (25.8%) cases and 27 (10.1%) controls had one or more of pregnancy-related complications. Pregnancy-related complications occurred among cases were APH 3 (3.4%), Pre-eclampsia 2 (2.2%), PROM 12 (13.5%), PIH 14 (15.7%), and Hyperemesis gravidarum 4 (4.5%) whereas 1 (0.4%), 3 (1.1%), 8 (3.0%), 6 (2.2%), and 11 (4.1%) controls had the same obstetric complications in the same order. Maternal Infection And Chronic Disease Related Factors About 6 (6.7%) cases and 14 (5.2%) controls had a chronic medical illness. Chronic hypertension was observed in 3 (3.4%) cases and 4 (1.5%) controls. Concerning maternal infections, UTI among 10 (11.2%) cases and 24 (9%) controls, and HIV/AIDS 8 (3.0%) among controls were found. Table-3 Behavioral And Lifestyle Related Factors Twenty-seven (30.3%) cases and 68 (25.5%) controls were drunk alcohol. The alcoholic beverage drunk comprised of “Tella”, “Tej”, “Areke”, Beer, and Wine. The majority, 19 (21.3%) cases and 56 (21.0%) controls drunk “Tella”. Regarding Khat chewing during pregnancy, 5 (5.6%) cases and 19 (7.1%) controls were chewed khat. About 15 (16.9%) cases and 40 (15.0%) of their counterparts were used medication other than minerals, vitamins, and iron/folic acid prescribed or from the counter. (Table-4) Determinants Associated With Preterm Birth In bi-variable logistic regression analysis at 95% level of confidence, ANC follow up, pregnancy status, PIH, PROM, Congenital malformation, Iron/folic acid supplementation, Anemia and hemoglobin level during current pregnancy, Height of mothers, and MUAC were statistically significant with PTB. In multivariable logistic regression analysis at P-value ≤ 0.05, ANC follow up, PIH, Congenital malformation, Iron/folic acid consumption, and Anemia during current pregnancy remained significantly associated determinants of PTB. The chance of being preterm among newborns whose mothers had had fewer than four ANC follow up was about 2 times more than newborns whose mothers had had four or more ANC follow up [AOR 2.267, Cl (1.205, 4.264)]. Newborns of mothers with PIH were four and half folds higher to be preterm [AOR 4.453 Cl (1.276, 15.536)] compared to newborns born from mothers with no PIH. Similarly, newborns whose mothers had not used iron/folic acid supplementation during current pregnancy were 4 times increased the risk to be born preterm [AOR 4.175, (1.632, 10.680)] compared to their counterparts. The odds of being preterm in newborns with a birth defect were 6 times higher [AOR 6.160, Cl (1.896, 20.013)] compared to newborns without a birth defect. Additionally, those newborns that born from mothers who had had anemia during current pregnancy were about 5 times higher to be born preterm [AOR 4.776 (1.492, 15.285)] than newborns whose mothers had no anemia. (Table-5) Discussion This study was intended to identify determinants for PTB in order to tackle the burden of the disease and its associated problems. The study has tried to look at the determinants of PTB by incorporating numerous risk factors as possible. This study revealed that newborns whose mothers followed ANC less than four times were about two times more likely to be preterm as compared to their counterparts. This finding is consistent with facility-based case-control studies done in Ethiopia; Jimma (AOR: 4.8), and Central Tigray (AOR: 2.15), North-Eastern Tanzania (AOR 3.25), and Nigeria (AOR 6.13). This might be due to that lack of/ inadequate ANC follow up decreases the chance of identifying risks of PTB and providing appropriate interventions for its prevention( 14 , 21 ). In this study, current PIH showed statically significant association with PTB. PIH increased the odds of PTB by four and half folds in newborns of mothers having PIH than newborns of mothers without PIH. Similarly, significant associations have been reported from studies done in Ethiopia, Gondar (AOR = 5.36), Jimma (AOR 6), and Central Tigray (AOR 3.24), and other studies from Kenya (7.81), and Iran (AOR 7.30). This might be due to hypertension increases the resistance of blood to uterine vessels reducing the utero-placental blood flow that may affect the exchange of nutrients and oxygen between mother and fetus which in turn results intrauterine growth restriction that causes PTB. Additionally, complications of PIH may cause vascular damage to the placenta, which induces the oxytocin receptors, leads to preterm labor and delivery( 14 , 24 – 26 ). Even though this may not be causal in nature, PIH is a common reason for indicated preterm deliveries and this may explain its association with PTB. This study also showed that anemia during current pregnancy were another independent determinant for PTB. Newborns whose mothers had anemia during current pregnancy were at increased risk to be born preterm compared to those newborns of mothers without anemia. This finding is in line with the studies conducted in Ethiopia, Jimma (AOR 2.7), Systematic review and meta-analysis in low- and middle-income countries (RR: 1.63) and Korea (AOR 1.53). This might be due to that anemia and iron deficiency by causing hypoxia and maternal infections induce maternal and fetal stress which leads to the formation of corticotrophin-releasing hormone. Excess corticotrophin-releasing hormone is the major risk factor for preterm labor, PIH, PROM and inhibits fetal growth by stimulating cortisol production. Another possible explanation is that iron deficiency may cause oxidative damage to the erythrocytes and the feto-placental unit affecting placental function( 27 , 28 ). The present study revealed that the chance of being preterm among neonates having birth defect was about six times more significant compared to those neonates without any visible birth defect. This finding agrees with the studies done in Tigray, Ethiopia (AOR 3.20) and Missouri U. S. A (AOR 3.20). There are plausible but need to be proven explanations that birth defects may result PTB. Among these, certain birth defects associated with abnormal connective tissue may increase the risk of preterm delivery, PTB and birth defects may have shared a common risk factors, and even not observed in our case, prenatal diagnosis of birth defect may lead to deliberately planned delivery through induction or cesarean section before 37 weeks of gestation( 29 , 30 ). As stated by the study done in Ethiopia, this might be due to the interaction of environmental and genetic risk factors that contribute to PTB( 15 ). As observed in this study, it may be due to that most mothers with birth defect newborns had no iron/folic acid supplementation during current pregnancy. Lack of iron/folic acid supplementation during current pregnancy also another significant factor for PTB. It increases the chance of PTB by four folds. A study from Kenya also showed 80% protective effect of prescribed iron/folic acid during pregnancy with PTB (AOR OR = 0.20). Another study from Sudan reported that folic acid level was associated with a lower risk of PTB (OR = 0.64) and there was a significant positive correlation between gestational age and folic acid level. This finding also supported by findings from China. The reason, in part, may be due to iron/folic acid supplementation prevents anemia and its effect. However, this finding contradicts the report from Bangladesh that iron/folic acid supplementation was a risk factor for PTB. We cannot definitively elucidate the disagreements between the studies in the association between maternal iron/folic acid supplementation and PTB, but it has been reported that the effects of hemoglobin concentration on birth outcomes could differ by ethnicity ( 31 ). It needs further interventional study. Conclusion The study indicated that ANC follow up fewer than four visits, PIH, no iron/folic acid supplementation during current pregnancy, being anemic and birth defect were significantly associated determinants of preterm birth. Health education and awareness creation about the benefit of adequate ANC follow, iron/folic acid consumption, and early prevention of pregnancy-related complications are very important during pregnancy. It is also advisable to prevent and treat maternal anemia during pregnancy. Strengthening patient tracing mechanism through phone call or home visit when mothers interrupt their follow up is advisable. Monitoring and evaluation of the effectiveness of maternal and child health/MCH services is also better. Abbreviations ANC: antenatal care; AOR: adjusted odd ratio; CI: confidence interval; COR: crudes odd ratio; EDHS: Ethiopian demographic and heath survey ; HIV/ADIS: Human Immunodeficiency virus/ Acquired immunodeficiency syndrome; IQR: interquartile range ; LBW: low birth weight; LMP: last menstrual period; LNMP: Last normal menstrual period; MSc: Master of science; MUAC: mid upper arm circumference; PIH: pregnancy induced hypertension; PROM: premature rupture of membrane; PTB: preterm birth; SPSS: Statistical Package for Social Sciences; UTI: urinary tract infection; VIF: variance inflation factor Declarations Ethics approval and consent to participate The Ethical clearance was obtained from the Ethical Review Committee of School of Nursing on behalf of University of Gondar Ethical Review Board. A permission letter was also obtained from the study Hospitals’ quality control office. After explanation had given about the purpose of the study, written consent was obtained from parent or legal guardian of the children. For parents or legal guardian who could not read and write, a thumbprint was used in place of the participant’s signature. Anonymous questioner was used to protect the identity and confidentiality of the information obtained. Besides, once the information was gathered from the patient, it was put under a locked cupboard and in a password protected computer. Consent to publish: Not applicable. Availability of data and material : all relevant data are available in the main document. Competing interest The authors declared that we have no any competing interests. Funding The study was funded by Amhara Regional State Health Bureau but the funder has no role on publication. Authors’ contribution MS wrote the proposal, participated in data collection, analyzed the data and drafted the manuscript. NBT and HSM approved the proposal with revisions, participated in data collection, data analysis and revised subsequent drafts of the manuscript. All authors read and approved the final manuscript. Acknowledgments We would like to thank study participants, data collectors, and supervisors for their willingness, cooperation, and commitment during data collection. References Dbstet A. WHO: recommended definitions, terminology and format for statistical tables related to the perinatal period and use of a new certificate for cause of perinatal deaths. Acta Obstet Gynecol Scand. 1977;56(3):247-53. Acta Obstet Gynecol Scand. 1977;56(3):247–53. Chawanpaiboon S, Vogel JP, Moller A-B, Lumbiganon P, Petzold M, Hogan D, et al. Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis. Lancet Glob Health [Internet]. 2018 Oct 30 [cited 2018 Nov 11];0(0). Lawn JE, Davidge R, Paul VK, Xylander S von, de Graft Johnson J, Costello A, et al. Born Too Soon: Care for the preterm baby. Reprod Health. 2013 Nov 15;10(Suppl 1):S5. March of Dimes, PMNCH, Save the Children, WHO. Born Too Soon: The Global Action Report on Preterm Birth. Eds CP Howson, MV Kinney, JE Lawn. World Health Organization. Geneva, 2012. 2012; Gebreslasie K. Preterm Birth and Associated Factors among Mothers Who Gave Birth in Gondar Town Health Institutions. Adv Nurs. 2016; Bekele I*, Demeke T and Dugna K. Prevalence of Preterm Birth and its Associated Factors among Mothers Delivered in Jimma University Specialized Teaching and Referral Hospital, Jimma Zone, Oromia Regional State, South West Ethiopia. J Womens Health Care. 2017 Li Liu, Shefali Oza, Dan Hogan, Yue Chu, Jamie Perin, Jun Zhu, Joy E Lawn, Simon Cousens, Colin Mathers, Robert E Black. Global, regional, and national causes of under-5 mortality in 2000–15: an updated systematic analysis with implications for the Sustainable Development Goals. 2016 Nov 10; You D, Hug L, Ejdemyr S, Beise J. Levels and trends in child mortality. Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (IGME). Report 2015. Lawn JE, Kinney M. Lawn JE, Kinney M. Preterm birth: now the leading cause of child death worldwide. American Association for the Advancement of Science; 2014. Sci Transl Med. 2014 Nov 19;6(263):263ed21-263ed21. Pike USNL of M 8600 R, Usa. WHO Recommendations on Interventions to Improve Preterm Birth Outcomes. [Internet]. World Health Organization; 2015 [cited 2018 Nov 12]. WHO, UNICEF Mathers, C. Global strategy for women’s, children’s and adolescents’ health (2016-2030) [Internet]. WHO. [cited 2018 Nov 15]. Lawn JE, Kinney MV, Belizan JM, Mason EM, McDougall L, Larson J, et al. Born Too Soon: Accelerating actions for prevention and care of 15 million newborns born too soon. Reprod Health. 2013 Nov 15;10(Suppl 1):S6. Oftedal A-M, Busterud K, Irgens LM, Haug K, Rasmussen S. Socio-economic risk factors for preterm birth in Norway 1999–2009: Scand J Public Health [Internet]. 2016 Jun 15 [cited 2018 Nov 15]; Mohammed Abaraya1 Sheka Shemsi Seid2 Shemsedin Amme Ibro2. Determinants of preterm birth at Jimma University Medical Center, southwest Ethiopia [Internet]. ResearchGate. [cited 2018 Nov 15]. Girmay Teklay1* , Tsega Teshale2, Hagos Tasew1, Teklewoini Mariye1, Hagos Berihu1 and Teklay Zeru1. Risk factors of preterm birth among mothers who gave birth in public hospitals of central zone, Tigray, Ethiopia: unmatched case–control study. BMC. 2018 Aug; Menon R. Preterm birth: a global burden on maternal and child health. Pathog Glob Health. 2012 Jul;106(3):139–40. Ververs M, Antierens A, Sackl A, Staderini N, Captier V. Which Anthropometric Indicators Identify a Pregnant Woman as Acutely Malnourished and Predict Adverse Birth Outcomes in the Humanitarian Context? PLoS Curr [Internet]. 2013 Jun 7 [cited 2018 Dec 13];5. Alice M. Tang, Mei Chung, Kimberly Dong, Norma Terrin, Andrew Edmonds, Nega Assefa, Terusha Chetty, Roshan Ramlal, Parul Christian, Keith West, Naveed Janjua, Christine Wanke, Megan Deitchler, Zeina Maalouf-Manasseh. FANTA Project III. USAID.Determining a Global Mid-Upper Arm Circumference Cutoff to Assess Malnutrition in Pregnant Women. 2016; Præstegaard C, Kesmodel PS, Kesmodel US. Is TWEAK a valid screening questionnaire to identify alcohol risk drinkers among pregnant women in Denmark? Acta Obstet Gynecol Scand. 2018;97(4):483–90. US Department of Health Human Services. Dietary guidelines for Americans 2015-2020: Skyhorse Publishing Inc.; 2017. :144. Tigist B, Abdela A, Kahsay ZG*. Preterm Birth and Associated Factors among Mothers Who gave Birth in Debremarkos Town Health Institutions, 2013 Institutional Based Cross Sectional Study. 2015; Dorka W. Abiy S. Goitom G.: Association of maternal anthropometrics with preterm birth and determine other risk factors for preterm birth among mothers who delivered in Gandhi Memorial Hospital and Tikur Anbessa Hospital, 2016/17. CSA. Ethiopia Demographic and Health Survey 2016. Addis Ababa, Ethiopia, and Rockville, Maryland, USA: CSA and ICF. 2017; Wagura P, Wasunna A, Laving A, Wamalwa D, Ng’ang’a P. Prevalence and factors associated with preterm birth at kenyatta national hospital. BMC Pregnancy Childbirth [Internet]. Butali A, Ezeaka C, Ekhaguere O, Weathers N, Ladd J, Fajolu I, et al. Characteristics and risk factors of preterm births in a tertiary center in Lagos, Nigeria. Pan Afr Med J [Internet]. 2016 Alijahan R, Hazrati S, Mirzarahimi M, Pourfarzi F, Ahmadi Hadi P. Prevalence and risk factors associated with preterm birth in Ardabil, Iran. Iran J Reprod Med. 2014 Jan;12(1):47–56. Zhang Q, Ananth CV, Li Z, Smulian JC. Maternal anaemia and preterm birth: a prospective cohort study. Int J Epidemiol. 2009 Oct 1;38(5):1380–9. Allen LH. Biological mechanisms that might underlie iron’s effects on fetal growth and preterm birth. J Nutr. 2001;131(2S-2):581S-589S. Dolan SM, Gross SJ, Merkatz IR, Faber V, Sullivan LM, Malone FD, et al. The Contribution of Birth Defects to Preterm Birth and Low Birth Weight: Obstet Gynecol. 2007 Aug;110(2, Part 1):318–24. Honein MA, Kirby RS, Meyer RE, Xing J, Skerrette NI, Yuskiv N, et al. The Association Between Major Birth Defects and Preterm Birth. Matern Child Health J. 2009 Mar;13(2):164–75. Yi S-W, Han Y-J, Ohrr H. Anemia before pregnancy and risk of preterm birth, low birth weight and small-for-gestational-age birth in Korean women. Eur J Clin Nutr. 2013 Apr;67(4):337–42. Supplementary File File name: Additional file Description : English Version Questionnaire Supplementary Files Additionalfile.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About In Review Editorial Policies 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-36781\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research article\",\"associatedPublications\":[],\"authors\":[{\"id\":745627,\"identity\":\"3b2a24f6-cc07-433b-a42f-80908054ea35\",\"order_by\":0,\"name\":\"Mesfin Shiferaw\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Debre Berhan Health Science College\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mesfin\",\"middleName\":\"\",\"lastName\":\"Shiferaw\",\"suffix\":\"\"},{\"id\":745628,\"identity\":\"b0f715a6-5c9f-46f2-bae9-7540bb06482e\",\"order_by\":1,\"name\":\"Nigusie Birhan Tebeje\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Gondar College of Medicine and Health Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Nigusie\",\"middleName\":\"Birhan\",\"lastName\":\"Tebeje\",\"suffix\":\"\"},{\"id\":745629,\"identity\":\"6dce0ca3-a340-4630-aa4c-a345a8928ec0\",\"order_by\":2,\"name\":\"Habtamu Sewunet Mekonnen\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACAwYeMC0HIg48IFILYwOQNgZrSSBFSyKIYCBKizn72eMPf1QcTt9w7fBDoC12croNBLRY9uQlNvOcOZy74XaaAVBLsrHZAUIOO5Bj2MzYdhuoJQGk5UDiNoJazr8xbPz573a6we30D0RquZFj2MDbALTidg6xttx4lzib59h/w5m3cwoOJBgQ45fzuQc+/qhJk+e7nb75w4cKOzmCWuBAAazSgFjlICDfQIrqUTAKRsEoGFEAAAGmTxKXZoh/AAAAAElFTkSuQmCC\",\"orcid\":\"https://orcid.org/0000-0001-9396-7437\",\"institution\":\"University of Gondar College of Medicine and Health Sciences\",\"correspondingAuthor\":true,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Habtamu\",\"middleName\":\"Sewunet\",\"lastName\":\"Mekonnen\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2020-06-19 22:14:23\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-36781/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-36781/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":13548057,\"identity\":\"b4b0b1ea-27b3-4173-9996-448f79c401c5\",\"added_by\":\"auto\",\"created_at\":\"2021-09-17 02:16:11\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":590083,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-36781/v1/c8175058-783a-4b43-a2ff-cf319c501a6a.pdf\"},{\"id\":1475996,\"identity\":\"26d40e88-4be4-4aa7-8228-639eb66d4a85\",\"added_by\":\"auto\",\"created_at\":\"2020-07-02 10:27:28\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":190818,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Additionalfile.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-36781/v1/Additionalfile.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Preterm birth determinants among newborns at six public hospitals in Northeast Amhara, Ethiopia: unmatched case-control study\",\"fulltext\":[{\"header\":\"Plain English Summary\",\"content\":\" \\u003cp\\u003ePreterm birth is a global public health problem. Nowadays, preterm birth is the first leading cause of under-five mortality and is one of the limiting factors for achieving a reduction in under-five mortality rate. The aim of this study was to identify the determinants of preterm birth at public hospitals in the Northeast Amhara, Ethiopia. Hospital-based unmatched case-control study was conducted from March 15th to April 25th, 2019. Data were collected using interviewer-administered structured pre-tested tool and document review for cases and controls. Antenatal care follow-up less than four visits, pregnancy-induced hypertension, no iron/folic acid supplementation during pregnancy, anemia during pregnancy, and birth defect were found significantly associated determinants of preterm birth. Health education and awareness creation about the benefit of adequate ANC follow, iron/folic acid consumption, and early prevention of pregnancy-related complications are paramount important during pregnancy.\\u003c/p\\u003e \"},{\"header\":\"Background\",\"content\":\" \\u003cp\\u003ePreterm birth (PTB), commonly defined as births before 37 completed weeks of gestation or fewer than 259 days since the first day of a woman's last normal menstrual period (LNMP) (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe magnitude of PTB shows variation among different parts of the world and is growing through time. Globally, it is estimated that 15\\u0026nbsp;million babies are born too soon each year and the majority of PTBs were in Asia and Sub-Saharan Africa where health systems are weak in access and minimum health services utilization. Additionally, preterm complications account for more than one million neonatal deaths each year(\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). From 11 countries which have greater than 15% prevalence of prematurity, all countries are found in Sub-Saharan African except Pakistan and Indonesia. Besides, poor countries have a higher prevalence of preterm births than rich countries (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e). In Ethiopia, the overall PTB rate is 12% and the prevalence of PTB in different parts of the country ranges from 4.4\\u0026ndash;25.9% (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eFor the first time in history, PTB is the leading cause of death in children younger than 5\\u0026nbsp;years worldwide covering 17% of under-five and 35% of neonatal deaths(\\u003cspan additionalcitationids=\\\"CR8\\\" citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). Premature infants are particularly vulnerable to complications due to impaired respiration, difficulty in feeding, poor body temperature regulation and high risk of infection. Babies who are born before they are physically ready to face the world encounter greater risks of serious health problems, including cerebral palsy, intellectual impairment, chronic lung disease, and vision and hearing loss(\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003ePremature babies also face drastically different odds of death depending on where they are born. From those extremely preterm babies (\\u0026lt;\\u0026thinsp;28 weeks) born in developing countries, greater than 90% of them die, while greater than 90% of extremely preterm babies in developed countries survive(\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAmong the 10 countries with the highest numbers of babies dying from PTB complications, India is the first followed by Nigeria. Ethiopia takes the 8th place with 24,400 child deaths per year (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). Different interventions have been implemented to prevent and improve the survival of PTB which demands huge financial capacity imposing a huge economic burden for the family, the community and the nation at large (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eEvery Women Every Child initiative set a target for 50% reduction in PTB deaths by 2025 which needs governments and the global community to take direct action to address deaths due to preterm birth, otherwise, the global total of preterm deaths will not reduce significantly, with around 900,000 premature babies continuing to die every year by 2025(\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSeveral risk factors for PTB have been identified across the world. The potential risk factors for PTB includes, but not limited, maternal risk factors like past adverse birth outcomes(PTB, stillbirth, LBW), obstetric complications(PIH, PROM), maternal infections(HIV, malaria, UTI) and no ANC follow up while having\\u0026thinsp;\\u0026ge;\\u0026thinsp;4 ANC checkup, IPI 24\\u0026ndash;36 months, maternal age 20\\u0026ndash;35 were found to be protective for PTB (\\u003cspan additionalcitationids=\\\"CR14\\\" citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e). However, risk factors for PTB are not always global; risk assessment should be carried out progressively at local, regional, and then global levels(\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn poorer countries, like Ethiopia, where fewer resources are destined for healthcare, the direct, indirect, immediate and later repercussions of prematurity constitute a relevant public health issue. However, the great majority of scientific evidence accumulated on this subject is generated in developed countries. Besides, the potential risk factors are varied in different locations. Therefore, this study will have its own contribution to avert PTB and its consequences by identifying risk factors.\\u003c/p\\u003e \"},{\"header\":\"Method\",\"content\":\" \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy design and period\\u003c/h2\\u003e \\u003cp\\u003eA facility-based unmatched case-control study was conducted at public hospitals in Northeast Amhara, Ethiopia from March 15th to April 25th, 2019.\\u003c/p\\u003e \\u003c/div\\u003e \\n\\u003ch2\\u003eStudy Area\\u003c/h2\\u003e\\n \\u003cp\\u003eThe study was conducted in Northeast part of Amhara region, Ethiopia. The study was employed in six randomly selected governmental hospitals (Debre Birhan Referral hospital, Tarmaber Primary Hospital, Kemisie General Hospital, Dessie Referral Hospital, Borumeda Hospital, and Woldia general Hospital).\\u003c/p\\u003e \\n\\u003ch2\\u003eSource Population\\u003c/h2\\u003e\\n \\u003cp\\u003eAll newborns who were born in public hospitals of Northeast Amhara, Ethiopia.\\u003c/p\\u003e \\n\\u003ch2\\u003eStudy Population\\u003c/h2\\u003e\\n \\u003cp\\u003eAll newborns who were born in selected public hospitals of Northeast Amhara, during data collection time.\\u003c/p\\u003e \\n\\u003ch2\\u003eInclusion And Exclusion Criteria\\u003c/h2\\u003e\\n \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eInclusion criteria\\u003c/h2\\u003e \\u003cp\\u003e \\u003cstrong\\u003eCases\\u003c/strong\\u003e \\u003cp\\u003eAll alive singleton preterm newborns (\\u0026lt;\\u0026thinsp;37 completed weeks of gestation) who was born in study hospitals of northeast Amhara.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eControls\\u003c/strong\\u003e \\u003cp\\u003eAll alive singleton term newborns (37 completed weeks to 42 weeks) at the same hospitals.\\u003c/p\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch2\\u003eExclusion Criteria\\u003c/h2\\u003e\\n \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003eNewborns whose mothers did not remember their LMP.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eNewborns whose mothers had a serious illness or with difficulty of communication during the study period.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e \\n\\u003ch2\\u003eSample Size Determination\\u003c/h2\\u003e\\n \\u003cp\\u003eSample size was determined by using EPI info version 7.2.2.6 by a double-population proportion difference formula. Different variables that have a high contribution for PTB was used and the variable that yields the highest sample size was selected. The sample size was calculated considering the following assumptions: r\\u0026thinsp;=\\u0026thinsp;3 (ratio of controls to cases), 80% power and 95% confidence level. ANC\\u0026thinsp;\\u0026lt;\\u0026thinsp;4 with P Control\\u0026thinsp;=\\u0026thinsp;42%, P Case\\u0026thinsp;=\\u0026thinsp;60.7% was selected. Based on this, the sample size was 323. By considering a 10% non-response rate, the final sample size was 356 with 89 cases and 267 controls.\\u003c/p\\u003e \\n\\u003ch2\\u003eSampling Technique\\u003c/h2\\u003e\\n \\u003cp\\u003eThe total sample size was allocated proportionately according to their caseloads for selected hospitals. Cases were preterm newborns (gestational age\\u0026thinsp;\\u0026lt;\\u0026thinsp;37 completed weeks) and controls were term newborns (gestational age of \\u0026ge;\\u0026thinsp;37 weeks to 42 weeks). Systematic sampling technique was employed to select controls and cases every fifth intervals and every two intervals respectively. The first participant was selected by lottery method.\\u003c/p\\u003e \\n\\u003ch2\\u003eStudy Variables\\u003c/h2\\u003e\\n \\u003cp\\u003e \\u003cstrong\\u003eDependent variable\\u003c/strong\\u003e \\u003cp\\u003ePreterm birth (Yes/No)\\u003c/p\\u003e \\u003c/p\\u003e \\n\\u003ch2\\u003eIndependent Variables\\u003c/h2\\u003e\\n \\u003cp\\u003eSocioeconomic factors (including paternal age \\u0026amp; education), obstetric history related factors, history of adverse birth outcomes, Obstetric complications, maternal medical conditions, behavioral factors, maternal anthropometrics, and infant characteristics.\\u003c/p\\u003e \\n\\u003ch2\\u003eOperational Definition\\u003c/h2\\u003e\\n \\u003cp\\u003e \\u003cstrong\\u003ePreterm birth\\u003c/strong\\u003e \\u003cp\\u003eNewborns born before 37 completed weeks of gestation\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eunderweight\\u003c/strong\\u003e \\u003cp\\u003eMothers with MUAC less than 23\\u0026nbsp;cm (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eAlcohol drinking\\u003c/strong\\u003e \\u003cp\\u003e(\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eA unit of alcohol\\u003c/strong\\u003e \\u003cp\\u003eIt is equivalent to 17.7\\u0026nbsp;ml or 14\\u0026nbsp;g of pure strength alcohol\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eBinge drinkers\\u003c/strong\\u003e \\u003cp\\u003emothers who drink\\u0026thinsp;\\u0026ge;\\u0026thinsp;3 drinks at a time/per seat\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003eDrinkers: mothers who use at least one unit of any alcohol during their pregnancy categorized into three levels:\\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003ePassive smoking\\u003c/strong\\u003e \\u003cp\\u003eliving with someone who smoked during the mother's pregnancy\\u003c/p\\u003e \\u003c/p\\u003e \\n\\u003ch2\\u003eData Collection Tools And Procedures\\u003c/h2\\u003e\\n \\u003cp\\u003eData was collected using interviewer-administered, structured pretested questionnaires and record review. The questionnaires include data on socio-demographic characteristics, obstetrics characteristics, infant characteristics, maternal anthropometrics, medical disorders, maternal infections, and behavioral factors. The questionnaires were prepared for this study by reviewing different kinds of literature from related studies and EDHS 2016, (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR22\\\" citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e). \\u003cb\\u003eAdditional file\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eConfirmation of Gestational age was taken from documentation and for non-recorded data, information was taken from mother\\u0026rsquo;s report of LNMP. The data was collected by six trained midwifery graduates and supervised by MSc nurses.\\u003c/p\\u003e \\u003cp\\u003eMUAC was measured by using flexible non- stretched tape taken at mid-point of left upper arm at relaxed position, without any clothing and with optimal tape tension between acromion process on shoulder blade and tip of olecranon process of ulna. Height was also measured using height Stadiometer with sliding head bar without shoes, standing erect, with shoulder blades, buttocks and heels touching the measuring board, looking straight ahead, shoulders relaxed, arms at sides, legs straight and knees together, feet flat and with heels together. Two height measurements were taken and average was used if readings are within 1\\u0026nbsp;cm each other and record to the nearest 1\\u0026nbsp;cm.\\u003c/p\\u003e \\n\\u003ch2\\u003eData Quality Control\\u003c/h2\\u003e\\n \\u003cp\\u003eData collectors and supervisor were trained for half a day on objective and methods of the study, study tools, how to approach the patient, and take anthropometric measures. In order to assess the validity and reliability of the tool and clarity of the variable, pre-test was done on 5% of the sample size newborns who were delivered out of the study hospitals two weeks ahead of data collection. During the data collection process, regular monitoring and supervision of the overall activity was done by the supervisors and principal investigator to check for completeness and ensure the quality of data. Besides, data cleaning and coding was carried out before analysis. The measurement tool was prepared in English and translated into local language (Amharic).\\u003c/p\\u003e \\n\\u003ch2\\u003eData Processing And Analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eThe collected data were checked for completeness and entered into EPI info version 7.2.2.6 and exported to SPSS version 23 for analysis. Data were summarized using text, tables, and graphs. Model goodness of fit test was checked by Hosmer-Lemeshow test (P-value\\u0026thinsp;=\\u0026thinsp;0.224) and fitted. Multicollinearity between independent variables was assessed by using VIF (1.1\\u0026ndash;1.27) and no problem detected. Binary logistic regression model was used to measure the association between preterm birth and risk factors. Variables that had P-value\\u0026thinsp;\\u0026le;\\u0026thinsp;0.2 on bi-variable regression analysis and had association with preterm birth in previous studies were fitted into the multivariable logistic regression model to assess their association with the outcome variable. Adjusted Odds ratio (AOR) with 95% confidence interval (CI) was calculated and P value\\u0026thinsp;\\u0026le;\\u0026thinsp;0.05 declared statistically significant.\\u003c/p\\u003e \"},{\"header\":\"Result\",\"content\":\" \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSocio‑demographic characteristics of respondents\\u003c/h2\\u003e \\u003cp\\u003eIn this study, 356 participants were included. From these participants, 89 were preterm newborns (cases) and 267 were term newborns as controls with 100% response rate. The median age of mothers was 25\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8 (IQR) years in case group and 26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7 (IQR) years in control group with a minimum of 17 and a maximum of 45\\u0026nbsp;years. The paternal median age was 31\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7 (IQR) years ranges from 20 to 60\\u0026nbsp;years. Regarding religion, 50 (56.2%) cases and 129 (48.3%) controls were Orthodox Christian. Majority of the respondents, 69 (77.5%) cases and 220 (82.4%) controls were married and living together. Concerning the educational level of parents, 34 (38.2%) mothers and 27 (30.4%) fathers in the case group had no formal education while 108 (44.4%) mothers and 89 (41.1%) fathers in the control group. About occupation, 46 (51.7%) cases and 132 (49.4%) controls were housewives. Forty-eight (53.9%) newborns in case-group and 148 (55.4%) newborns in control-group were male in sex. (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDistribution of socio-demographic characteristics among mothers who gave birth at public hospitals of Northeast Amhara, Ethiopia, 2019\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCategory\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCases (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eControls (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eResidence\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRural\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e42 (47.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e98 (36.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e140 (39.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUrban\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e47 (52.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e169 (63.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e216 (60.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eAge of mother\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6 (6.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10 (3.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e16 (4.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e20\\u0026ndash;34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e68 (76.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e226 (84.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e294 (82.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15 (16.9%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e31 (11.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e46 (12.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eEducational status\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNot able to read and write\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20 (22.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e58 (21.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e78 (21.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAble to read and write\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14 (15.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e50 (18.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e64 (18.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrimary education\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e19 (21.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e58 (21.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e77 (21.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSecondary education\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23 (25.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e60 (22.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e83 (23.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHigher education\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13 (14.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e41 (15.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e54 (15.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eMarital status\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMarried\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e69 (77.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e220 (82.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e289 (81.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSeparated\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16 (18.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30 (11.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e46 (12.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSingle/Divorced/Widowed\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4 (4.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17 (6.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e21 (5.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eReligion\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOrthodox\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e50 (56.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e129 (48.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e179 (50.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMuslim\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e33 (37.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e120 (44.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e153 (43.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eProtestant\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 (5.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13 (4.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e18 (5.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCatholic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1 (1.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5 (1.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6 (1.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eOccupational status\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHousewife\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e46 (51.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e132 (49.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e178 (50.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFarmer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10 (11.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30 (11.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e40 (11.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMerchant\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14 (15.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e40 (15.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e54 (15.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGovernment/NOGs employee\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e19 (21.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e65 (24.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e84 (23.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eEducational status of father\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNot able to read and write\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11 (12.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e45 (16.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e56 (15.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAble to read and write\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16 (18.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e54 (20.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e70 (19.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrimary education\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21 (23.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e49 (18.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e70 (19.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSecondary education\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25 (28.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e62 (23.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e87 (24.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHigher education\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16 (18.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e57 (21.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e73 (20.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eAge of father\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 (5.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10 (3.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e15 (4.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e25\\u0026ndash;29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30 (33.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e63 (23.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e93 (26.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e30\\u0026ndash;34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25 (28.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e95 (35.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e120 (33.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e35\\u0026ndash;39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16 (18.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e49 (18.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e65 (18.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13 (14.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e50 (18.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e63 (17.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eIncome (monthly)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;3000 Birr\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52 (58.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e130 (48.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e182 (51.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;3000 Birr\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e37 (41.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e137 (51.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e174 (48.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eInfant sex\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e48 (53.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e148 (55.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e196 (55.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e41 (46.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e119 (44.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e160 (44.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cbr\\u003e\\n\\u003ch2\\u003eMaternal Obstetric Related Factors\\u003c/h2\\u003e\\n \\u003cp\\u003eIn this study, 66 (74.2%) cases and 113 (42.3%) controls had fewer than four ANC visits. Among these, 25 (28.1%) cases and 13 (4.9%) controls had no ANC follow up. Regarding Iron/folic acid supplementation, 44 (49.4%) cases and 45 (16.9%) controls had used no Iron/folic acid. About 13 (14.6%) cases and 7 (2.6%) controls had experienced anemia during pregnancy. The majority, 66 (74.2%) newborns in case-group and 217 (81.3%) newborns in control-group were born through spontaneous vaginal delivery. Concerning, past adverse birth outcome, 8 (25%) cases and 18 (17.3%) controls had one or more past adverse birth outcomes. Past adverse birth outcomes encountered were LBW 5 (15.6%), Stillbirth 3 (9.4%), and Abortion 3 (9.4%) among cases and 6 (5.8%), 9 (8.7%), and 7 (6.7%) among controls respectively. (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eMaternal obstetric related factors among mothers who gave birth at public hospitals of Northeast Amhara, Ethiopia, 2019\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCategory\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCases (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eControls (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eANC Follow up\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25 (28.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13 (4.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e38 (10.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e41 (46.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e100 (37.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e141 (39.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23 (25.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e154 (57.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e177 (49.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eTime of ANC initiation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFirst trimester\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23 (35.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e97 (38.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e120 (37.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSecond trimester\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e40 (62.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e150 (59.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e190 (59.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eThird trimester\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1 (1.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7 (2.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8 (2.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eBirth interval\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo previous birth\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e57 (64.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e163 (61.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e220 (61.8% )\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;24 months\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 (5.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e19 (7.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e24 (6.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e24\\u0026ndash;36 months\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20 (22.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e68 (25.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e88 (24.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;37 months\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7 (7.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17 (6.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e24 (6.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eParity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrimipara\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e57 (64.0% )\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e163 (61.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e220 (61.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMultipara\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e32 (36.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e104 (39.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e136 (38.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eIron/folic acid supplementation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e44 (49.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e45 (16.9% )\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e89 (25.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e30\\u0026ndash;60 tabs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e36 (40.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e144 (53.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e180 (50.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;60 tabs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9 (10.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e78 (29.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e87 (24.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eAnemia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e76 (85.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e260 (97.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e336 (94.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13 (14.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7 (2.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e20 (5.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eHemoglobin\\u003c/p\\u003e \\u003cp\\u003eLevel\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;11\\u0026nbsp;g/dl\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25 (28.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35 (13.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e60 (16.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;11\\u0026nbsp;g/dl\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e64 (71.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e232 (86.9%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e296 (83.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eMode of delivery\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSpontaneous\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e66 (74.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e217 (81.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e283 (79.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eInduction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18 (20.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16 (6.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e34 (9.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC-section\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 (5.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e34 (12.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e39 (11.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePregnancy status\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUnplanned\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22 (24.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e33 (12.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e55 (15.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePlanned\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e67 (75.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e234 (87.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e301 (84.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eMaternal Rh factor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePositive\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e59 (66.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e187 (70.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e246 (69.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNegative\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25 (28.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e72 (27.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e97 (27.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUnknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 (5.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8 (3.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e13 (3.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eMaternal height\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;150\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20 (22.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35 (13.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e55 (15.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;150\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e69 (77.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e232 (86.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e301 (84.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eMaternal MUAC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e44 (49.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e88 (33.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e132 (37.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e45 (50.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e179 (67.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e224 (62.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eHistory of adverse birth outcome\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24 (75.0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e86 (82.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e110 (80.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8 (25.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18 (17.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e26 (19.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eType of adverse\\u003c/p\\u003e \\u003cp\\u003ebirth outcome\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLBW\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 (15.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6 (5.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e11 (8.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eStillbirth\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 (9.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9 (8.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e12 (8.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbortion\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 (9.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7 (6.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e10 (7.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eMaternal infection and chronic disease related factors among mothers who gave birth at public hospitals of Northeast Amhara, Ethiopia, 2019\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCategory\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCases (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eControls (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eChronic medical illness\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e83 (93.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e253 (94.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e336 (94.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6 (6.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14 (5.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e20 (5.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eType of medical illness\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHPN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 (3.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4 (1.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7 (2.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDM/KD/LD?TD/\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4 (4.49%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10 (3.74%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14 (3.93%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eMaternal infection\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUTI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10 (11.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e24 (9.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e34 (9.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMalaria\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1 (1.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4 (1.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5 (1.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1 (1.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5 (1.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6 (1.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHIV/AIDS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0 (%0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8 (3.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8 (2.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003eKey: DM\\u0026thinsp;=\\u0026thinsp;diabetes mellitus, KD\\u0026thinsp;=\\u0026thinsp;kidney disease, LD\\u0026thinsp;=\\u0026thinsp;liver disease, TD\\u0026thinsp;=\\u0026thinsp;thyroid disorder\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBehavioral factors among mothers who gave birth at public hospitals of Northeast Amhara, Ethiopia, 2019\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCategory\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCases (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eControls (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eAlcohol drinking during pregnancy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e62 (69.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e199 (74.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e261 (73.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27 (30.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e68 (25.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e95 (26.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eType of alcohol drunk\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTella\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e19 (21.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e56 (21.0% )\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e75 (21.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAreke\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6 (6.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7 (2.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e13 (3.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBeer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8 (9.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e24 (9.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e32 (9.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWine/Tej\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7 (7.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7 (2.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14 (3.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eFrequency of drinking\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDaily/Weekly\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9 (33.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13 (19.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e22 (23.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFortnightly/Monthly\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7 (25.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13 (19.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e20 (21.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOccasionally\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11 (40.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e42 (61.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e53 (55.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eUnit of alcohol per week (recent month)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo alcohol\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e65 (73.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e223 (83.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e288 (80.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;3 unit\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21 (23.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e40 (15.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e61 (17.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;4 unit\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 (3.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4 (1.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7 (2.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eKhat chewing during pregnancy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e84 (94.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e248 (92.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e332 (93.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 (5.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e19 (7.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e24 (6.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eFrequency of khat chewing\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDaily/Weekly\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1 (20%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4 (21.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5 (20.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFortnightly\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0 (0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5 (26.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5 (20.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMonthly\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0 (0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1 (5.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1 (4.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOccasionally\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4 (80%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9 (47.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e13 (54.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePassive Smoking\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e87 (97.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e261 (97.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e348 (97.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2 (2.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6 (2.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8 (2.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eAny medication during pregnancy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e74 (83.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e227 (85.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e301 (84.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15 (16.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e40 (15.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e55 (15.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eFrequency of medication use\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDaily\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12 (80.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e34 (85.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e46 (83.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWeekly\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0 (0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1 (2.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1 (1.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOccasionally\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3 (20.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5 (12.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8 (14.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBi-variable and Multi-variable analysis of determinants associated with preterm birth among mothers who gave birth at public hospitals of Northeast Amhara, Ethiopia, 2019\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariables\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCategory\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCases (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eControls (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eCOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eAOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eResidence\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003eRural\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e42 (47.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e98 (36.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.541 (0.949, 2.503)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.998(0.521,1.912)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003eUrban\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e47 (52.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e169 (63.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePregnancy status\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003eUnplanned\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22 (24.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e33 (12.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.328 (1.273, 4.259)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.995(0.442,2.242)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003ePlanned\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e67 (75.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e234 (87.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eANC follow up\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003e\\u0026lt;\\u0026thinsp;4 ANC visit\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e66 (74.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e113 (42.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.911 (2.295, 6.665)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.267(1.205,4.264)**\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003e\\u0026ge;\\u0026thinsp;4 ANC visit\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23 (25.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e154 (57.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eAge of mother in years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003e\\u0026lt;\\u0026thinsp;19\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6 (6.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10 (3.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.240(0.379, 4.056)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.326(0.290,6.060)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003e20\\u0026ndash;34\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e68 (76.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e226 (84.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.622(0.317, 1.219)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.009(0.419,2.431)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003e\\u0026ge;\\u0026thinsp;35\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15 (16.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e31 (11.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePIH\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003eNo\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e75 (84.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e261 (97.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003eYes\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14 (15.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6 (2.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8.120 (3.016, 21.858)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4.453(1.276,15.536)**\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePROM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003eNo\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e77 (86.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e259 (97%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003eYes\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12 (13.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8 (3.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.045 (1.991, 12.789)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.159(0.938,10.638)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCongenital malformation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003eNo\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e74 (83.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e262 (98.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003eYes\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15 (16.9% )\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5 (1.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e10.622 (3.737, 30.186)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e6.160(1.896,20.013)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eIron/folic acid supplementation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003eNo iron use\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e44 (49.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e45 (16.9% )\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8.474(3.78718.962)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4.175(1.632,10.680)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003e30\\u0026ndash;60 tabs\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e36 (40.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e144 (53.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.167(0.9934.730)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.838(0.765,4.418)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003e\\u0026ge;\\u0026thinsp;60 tabs\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9 (10.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e78 (29.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eAnemia during current pregnancy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003eNo\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e76 (85.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e260 (97.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003eYes\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13 (14.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7 (2.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.353 (2.448, 16.489)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4.776 (1.492, 15.285)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eHemoglobin level\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003e\\u0026lt;\\u0026thinsp;11\\u0026nbsp;mg/dl\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25 (28.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35 (13.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.589 (1.445, 4.639)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.880(0.925,3.821)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003e\\u0026ge;\\u0026thinsp;11\\u0026nbsp;mg/dl\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e64 (71.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e232 (86.9%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eHeight of\\u003c/p\\u003e \\u003cp\\u003eMothers\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003e\\u0026lt;\\u0026thinsp;150\\u0026nbsp;cm\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20 (22.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35 (13.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.921 (1.042, 3.542)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.112(0.513, 2.411)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003e\\u0026ge;\\u0026thinsp;150\\u0026nbsp;cm\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e69 (77.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e232 (86.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eMUAC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003e\\u0026lt;\\u0026thinsp;23\\u0026nbsp;cm\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e44 (49.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e88 (33.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.989 (1.221, 3.239)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.481(0.812,2.699)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003e\\u0026ge;\\u0026thinsp;23\\u0026nbsp;cm\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e45 (50.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e179 (67.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eIncome (monthly)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003e\\u0026lt;\\u0026thinsp;3000 EBR\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52 (58.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e130 (48.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.481 (0.912, 2.406)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.388(0.738,2.608)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003e\\u0026ge;\\u0026thinsp;3000 EBR\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e37 (41.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e137 (51.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eKey: COR\\u0026thinsp;=\\u0026thinsp;crude odds ratio, AOR\\u0026thinsp;=\\u0026thinsp;adjusted odds ratio, CI\\u0026thinsp;=\\u0026thinsp;confidence interval, *=P-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01, **=p-value\\u0026thinsp;\\u0026le;\\u0026thinsp;0.05\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cbr\\u003e\\n\\u003ch2\\u003eFetal Related Factors\\u003c/h2\\u003e\\n \\u003cp\\u003eThe mean gestational age among PTBs and term births were 34\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.94 weeks and 39.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.27 weeks, respectively. In the case group, 17 (19.1%) newborns were small for gestational age whereas 12 (4.5%) newborns in control group were low birth weight. Concerning fetal distress, 5 (5.6%) cases and 15 (5.6%) controls had fetal distress. On the other hand, 15 (16.9%) preterm newborns and 5 (1.9%) term newborns had a visible congenital/birth defect.\\u003c/p\\u003e \\n\\u003ch2\\u003eCurrent Obstetric Complications\\u003c/h2\\u003e\\n \\u003cp\\u003eDuring current pregnancy, about 23 (25.8%) cases and 27 (10.1%) controls had one or more of pregnancy-related complications. Pregnancy-related complications occurred among cases were APH 3 (3.4%), Pre-eclampsia 2 (2.2%), PROM 12 (13.5%), PIH 14 (15.7%), and Hyperemesis gravidarum 4 (4.5%) whereas 1 (0.4%), 3 (1.1%), 8 (3.0%), 6 (2.2%), and 11 (4.1%) controls had the same obstetric complications in the same order.\\u003c/p\\u003e \\n\\u003ch2\\u003eMaternal Infection And Chronic Disease Related Factors\\u003c/h2\\u003e\\n \\u003cp\\u003eAbout 6 (6.7%) cases and 14 (5.2%) controls had a chronic medical illness. Chronic hypertension was observed in 3 (3.4%) cases and 4 (1.5%) controls. Concerning maternal infections, UTI among 10 (11.2%) cases and 24 (9%) controls, and HIV/AIDS 8 (3.0%) among controls were found. Table-3\\u003c/p\\u003e \\n\\u003ch2\\u003eBehavioral And Lifestyle Related Factors\\u003c/h2\\u003e\\n \\u003cp\\u003eTwenty-seven (30.3%) cases and 68 (25.5%) controls were drunk alcohol. The alcoholic beverage drunk comprised of \\u0026ldquo;Tella\\u0026rdquo;, \\u0026ldquo;Tej\\u0026rdquo;, \\u0026ldquo;Areke\\u0026rdquo;, Beer, and Wine. The majority, 19 (21.3%) cases and 56 (21.0%) controls drunk \\u0026ldquo;Tella\\u0026rdquo;. Regarding Khat chewing during pregnancy, 5 (5.6%) cases and 19 (7.1%) controls were chewed khat. About 15 (16.9%) cases and 40 (15.0%) of their counterparts were used medication other than minerals, vitamins, and iron/folic acid prescribed or from the counter. (Table-4)\\u003c/p\\u003e \\n\\u003ch2\\u003eDeterminants Associated With Preterm Birth\\u003c/h2\\u003e\\n \\u003cp\\u003eIn bi-variable logistic regression analysis at 95% level of confidence, ANC follow up, pregnancy status, PIH, PROM, Congenital malformation, Iron/folic acid supplementation, Anemia and hemoglobin level during current pregnancy, Height of mothers, and MUAC were statistically significant with PTB. In multivariable logistic regression analysis at P-value\\u0026thinsp;\\u0026le;\\u0026thinsp;0.05, ANC follow up, PIH, Congenital malformation, Iron/folic acid consumption, and Anemia during current pregnancy remained significantly associated determinants of PTB.\\u003c/p\\u003e \\u003cp\\u003eThe chance of being preterm among newborns whose mothers had had fewer than four ANC follow up was about 2 times more than newborns whose mothers had had four or more ANC follow up [AOR 2.267, Cl (1.205, 4.264)]. Newborns of mothers with PIH were four and half folds higher to be preterm [AOR 4.453 Cl (1.276, 15.536)] compared to newborns born from mothers with no PIH. Similarly, newborns whose mothers had not used iron/folic acid supplementation during current pregnancy were 4 times increased the risk to be born preterm [AOR 4.175, (1.632, 10.680)] compared to their counterparts. The odds of being preterm in newborns with a birth defect were 6 times higher [AOR 6.160, Cl (1.896, 20.013)] compared to newborns without a birth defect. Additionally, those newborns that born from mothers who had had anemia during current pregnancy were about 5 times higher to be born preterm [AOR 4.776 (1.492, 15.285)] than newborns whose mothers had no anemia. (Table-5)\\u003c/p\\u003e \"},{\"header\":\"Discussion\",\"content\":\" \\u003cp\\u003eThis study was intended to identify determinants for PTB in order to tackle the burden of the disease and its associated problems. The study has tried to look at the determinants of PTB by incorporating numerous risk factors as possible.\\u003c/p\\u003e \\u003cp\\u003eThis study revealed that newborns whose mothers followed ANC less than four times were about two times more likely to be preterm as compared to their counterparts. This finding is consistent with facility-based case-control studies done in Ethiopia; Jimma (AOR: 4.8), and Central Tigray (AOR: 2.15), North-Eastern Tanzania (AOR 3.25), and Nigeria (AOR 6.13). This might be due to that lack of/ inadequate ANC follow up decreases the chance of identifying risks of PTB and providing appropriate interventions for its prevention(\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn this study, current PIH showed statically significant association with PTB. PIH increased the odds of PTB by four and half folds in newborns of mothers having PIH than newborns of mothers without PIH. Similarly, significant associations have been reported from studies done in Ethiopia, Gondar (AOR\\u0026thinsp;=\\u0026thinsp;5.36), Jimma (AOR 6), and Central Tigray (AOR 3.24), and other studies from Kenya (7.81), and Iran (AOR 7.30). This might be due to hypertension increases the resistance of blood to uterine vessels reducing the utero-placental blood flow that may affect the exchange of nutrients and oxygen between mother and fetus which in turn results intrauterine growth restriction that causes PTB. Additionally, complications of PIH may cause vascular damage to the placenta, which induces the oxytocin receptors, leads to preterm labor and delivery(\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR25\\\" citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e). Even though this may not be causal in nature, PIH is a common reason for indicated preterm deliveries and this may explain its association with PTB.\\u003c/p\\u003e \\u003cp\\u003eThis study also showed that anemia during current pregnancy were another independent determinant for PTB. Newborns whose mothers had anemia during current pregnancy were at increased risk to be born preterm compared to those newborns of mothers without anemia. This finding is in line with the studies conducted in Ethiopia, Jimma (AOR 2.7), Systematic review and meta-analysis in low- and middle-income countries (RR: 1.63) and Korea (AOR 1.53). This might be due to that anemia and iron deficiency by causing hypoxia and maternal infections induce maternal and fetal stress which leads to the formation of corticotrophin-releasing hormone. Excess corticotrophin-releasing hormone is the major risk factor for preterm labor, PIH, PROM and inhibits fetal growth by stimulating cortisol production. Another possible explanation is that iron deficiency may cause oxidative damage to the erythrocytes and the feto-placental unit affecting placental function(\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe present study revealed that the chance of being preterm among neonates having birth defect was about six times more significant compared to those neonates without any visible birth defect. This finding agrees with the studies done in Tigray, Ethiopia (AOR 3.20) and Missouri U. S. A (AOR 3.20). There are plausible but need to be proven explanations that birth defects may result PTB. Among these, certain birth defects associated with abnormal connective tissue may increase the risk of preterm delivery, PTB and birth defects may have shared a common risk factors, and even not observed in our case, prenatal diagnosis of birth defect may lead to deliberately planned delivery through induction or cesarean section before 37 weeks of gestation(\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e). As stated by the study done in Ethiopia, this might be due to the interaction of environmental and genetic risk factors that contribute to PTB(\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e). As observed in this study, it may be due to that most mothers with birth defect newborns had no iron/folic acid supplementation during current pregnancy.\\u003c/p\\u003e \\u003cp\\u003eLack of iron/folic acid supplementation during current pregnancy also another significant factor for PTB. It increases the chance of PTB by four folds. A study from Kenya also showed 80% protective effect of prescribed iron/folic acid during pregnancy with PTB (AOR OR\\u0026thinsp;=\\u0026thinsp;0.20). Another study from Sudan reported that folic acid level was associated with a lower risk of PTB (OR\\u0026thinsp;=\\u0026thinsp;0.64) and there was a significant positive correlation between gestational age and folic acid level. This finding also supported by findings from China. The reason, in part, may be due to iron/folic acid supplementation prevents anemia and its effect. However, this finding contradicts the report from Bangladesh that iron/folic acid supplementation was a risk factor for PTB. We cannot definitively elucidate the disagreements between the studies in the association between maternal iron/folic acid supplementation and PTB, but it has been reported that the effects of hemoglobin concentration on birth outcomes could differ by ethnicity (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e). It needs further interventional study.\\u003c/p\\u003e \"},{\"header\":\"Conclusion\",\"content\":\" \\u003cp\\u003eThe study indicated that ANC follow up fewer than four visits, PIH, no iron/folic acid supplementation during current pregnancy, being anemic and birth defect were significantly associated determinants of preterm birth.\\u003c/p\\u003e \\u003cp\\u003eHealth education and awareness creation about the benefit of adequate ANC follow, iron/folic acid consumption, and early prevention of pregnancy-related complications are very important during pregnancy. It is also advisable to prevent and treat maternal anemia during pregnancy.\\u003c/p\\u003e \\u003cp\\u003eStrengthening patient tracing mechanism through phone call or home visit when mothers interrupt their follow up is advisable. Monitoring and evaluation of the effectiveness of maternal and child health/MCH services is also better.\\u003c/p\\u003e \"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eANC: antenatal care; AOR: adjusted odd ratio; CI: confidence interval; COR: crudes odd ratio; EDHS: Ethiopian demographic and heath survey ; HIV/ADIS: Human Immunodeficiency\\u0026nbsp;virus/ Acquired immunodeficiency syndrome; IQR: interquartile range ; LBW: low birth weight; LMP: last menstrual period; LNMP: Last normal menstrual period; MSc: Master of science; MUAC: mid upper arm circumference; PIH: pregnancy induced hypertension; PROM: premature rupture of membrane; \\u0026nbsp;PTB: preterm birth; SPSS: Statistical Package for Social Sciences; UTI: urinary tract infection; VIF: variance inflation factor\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe Ethical clearance was obtained from the Ethical Review Committee of School of Nursing on behalf of University of Gondar Ethical Review Board. A permission letter was also obtained from the study Hospitals\\u0026rsquo; quality control office. After explanation had given about the purpose of the study, written consent was obtained from parent or legal guardian of the children. For parents or legal guardian who could not read and write, a thumbprint was used in place of the participant\\u0026rsquo;s signature. Anonymous questioner was used to protect the identity and confidentiality of the information obtained. Besides, once the information was gathered from the patient, it was put under a locked cupboard and in a password protected computer.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to publish: \\u003c/strong\\u003eNot applicable.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and material\\u003c/strong\\u003e\\u003cstrong\\u003e:\\u003c/strong\\u003e all relevant data are available in the main document.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interest \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declared that we have no any competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study was funded by Amhara Regional State Health Bureau but the funder has no role on publication.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; contribution \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMS\\u003c/strong\\u003e wrote the proposal, participated in data collection, analyzed the data and drafted the manuscript. \\u003cstrong\\u003eNBT \\u003c/strong\\u003eand \\u003cstrong\\u003eHSM\\u003c/strong\\u003e approved the proposal with revisions, participated in data collection, data analysis and revised subsequent drafts of the manuscript. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe would like to thank study participants, data collectors, and supervisors for their willingness, cooperation, and commitment during data collection.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eDbstet A. WHO: recommended definitions, terminology and format for statistical tables related to the perinatal period and use of a new certificate for cause of perinatal deaths. Acta Obstet Gynecol Scand. 1977;56(3):247-53. Acta Obstet Gynecol Scand. 1977;56(3):247\\u0026ndash;53.\\u003c/li\\u003e\\n\\u003cli\\u003eChawanpaiboon S, Vogel JP, Moller A-B, Lumbiganon P, Petzold M, Hogan D, et al. Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis. Lancet Glob Health [Internet]. 2018 Oct 30 [cited 2018 Nov 11];0(0).\\u003c/li\\u003e\\n\\u003cli\\u003eLawn JE, Davidge R, Paul VK, Xylander S von, de Graft Johnson J, Costello A, et al. Born Too Soon: Care for the preterm baby. Reprod Health. 2013 Nov 15;10(Suppl 1):S5.\\u003c/li\\u003e\\n\\u003cli\\u003eMarch of Dimes, PMNCH, Save the Children, WHO. Born Too Soon: The Global Action Report on Preterm Birth. Eds CP Howson, MV Kinney, JE Lawn. World Health Organization. Geneva, 2012. 2012;\\u003c/li\\u003e\\n\\u003cli\\u003eGebreslasie K. Preterm Birth and Associated Factors among Mothers Who Gave Birth in Gondar Town Health Institutions. Adv Nurs. 2016;\\u003c/li\\u003e\\n\\u003cli\\u003eBekele I*, Demeke T and Dugna K. Prevalence of Preterm Birth and its Associated Factors among Mothers Delivered in Jimma University Specialized Teaching and Referral Hospital, Jimma Zone, Oromia Regional State, South West Ethiopia. J Womens Health Care. 2017\\u003c/li\\u003e\\n\\u003cli\\u003eLi Liu, Shefali Oza, Dan Hogan, Yue Chu, Jamie Perin, Jun Zhu, Joy E Lawn, Simon Cousens, Colin Mathers, Robert E Black. Global, regional, and national causes of under-5 mortality in 2000\\u0026ndash;15: an updated systematic analysis with implications for the Sustainable Development Goals. 2016 Nov 10;\\u003c/li\\u003e\\n\\u003cli\\u003eYou D, Hug L, Ejdemyr S, Beise J. Levels and trends in child mortality. Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (IGME). Report 2015.\\u003c/li\\u003e\\n\\u003cli\\u003eLawn JE, Kinney M. Lawn JE, Kinney M. Preterm birth: now the leading cause of child death worldwide. American Association for the Advancement of Science; 2014. Sci Transl Med. 2014 Nov 19;6(263):263ed21-263ed21.\\u003c/li\\u003e\\n\\u003cli\\u003ePike USNL of M 8600 R, Usa. WHO Recommendations on Interventions to Improve Preterm Birth Outcomes. [Internet]. World Health Organization; 2015 [cited 2018 Nov 12].\\u003c/li\\u003e\\n\\u003cli\\u003eWHO, UNICEF Mathers, C. Global strategy for women\\u0026rsquo;s, children\\u0026rsquo;s and adolescents\\u0026rsquo; health (2016-2030) [Internet]. WHO. [cited 2018 Nov 15].\\u003c/li\\u003e\\n\\u003cli\\u003eLawn JE, Kinney MV, Belizan JM, Mason EM, McDougall L, Larson J, et al. Born Too Soon: Accelerating actions for prevention and care of 15 million newborns born too soon. Reprod Health. 2013 Nov 15;10(Suppl 1):S6.\\u003c/li\\u003e\\n\\u003cli\\u003eOftedal A-M, Busterud K, Irgens LM, Haug K, Rasmussen S. Socio-economic risk factors for preterm birth in Norway 1999\\u0026ndash;2009: Scand J Public Health [Internet]. 2016 Jun 15 [cited 2018 Nov 15];\\u003c/li\\u003e\\n\\u003cli\\u003eMohammed Abaraya1 Sheka Shemsi Seid2 Shemsedin Amme Ibro2. Determinants of preterm birth at Jimma University Medical Center, southwest Ethiopia [Internet]. ResearchGate. [cited 2018 Nov 15].\\u003c/li\\u003e\\n\\u003cli\\u003eGirmay Teklay1* , Tsega Teshale2, Hagos Tasew1, Teklewoini Mariye1, Hagos Berihu1 and Teklay Zeru1. Risk factors of preterm birth among mothers who gave birth in public hospitals of central zone, Tigray, Ethiopia: unmatched case\\u0026ndash;control study. BMC. 2018 Aug;\\u003c/li\\u003e\\n\\u003cli\\u003eMenon R. Preterm birth: a global burden on maternal and child health. Pathog Glob Health. 2012 Jul;106(3):139\\u0026ndash;40.\\u003c/li\\u003e\\n\\u003cli\\u003eVervers M, Antierens A, Sackl A, Staderini N, Captier V. Which Anthropometric Indicators Identify a Pregnant Woman as Acutely Malnourished and Predict Adverse Birth Outcomes in the Humanitarian Context? PLoS Curr [Internet]. 2013 Jun 7 [cited 2018 Dec 13];5.\\u003c/li\\u003e\\n\\u003cli\\u003eAlice M. Tang, Mei Chung, Kimberly Dong, Norma Terrin, Andrew Edmonds, Nega Assefa, Terusha Chetty, Roshan Ramlal, Parul Christian, Keith West, Naveed Janjua, Christine Wanke, Megan Deitchler, Zeina Maalouf-Manasseh. FANTA Project III. USAID.Determining a Global Mid-Upper Arm Circumference Cutoff to Assess Malnutrition in Pregnant Women. 2016;\\u003c/li\\u003e\\n\\u003cli\\u003ePr\\u0026aelig;stegaard C, Kesmodel PS, Kesmodel US. Is TWEAK a valid screening questionnaire to identify alcohol risk drinkers among pregnant women in Denmark? Acta Obstet Gynecol Scand. 2018;97(4):483\\u0026ndash;90.\\u003c/li\\u003e\\n\\u003cli\\u003eUS Department of Health Human Services. Dietary guidelines for Americans 2015-2020: Skyhorse Publishing Inc.; 2017. :144.\\u003c/li\\u003e\\n\\u003cli\\u003eTigist B, Abdela A, Kahsay ZG*. Preterm Birth and Associated Factors among Mothers Who gave Birth in Debremarkos Town Health Institutions, 2013 Institutional Based Cross Sectional Study. 2015;\\u003c/li\\u003e\\n\\u003cli\\u003eDorka W. Abiy S. Goitom G.: Association of maternal anthropometrics with preterm birth and determine other risk factors for preterm birth among mothers who delivered in Gandhi Memorial Hospital and Tikur Anbessa Hospital, 2016/17.\\u003c/li\\u003e\\n\\u003cli\\u003eCSA. Ethiopia Demographic and Health Survey 2016. Addis Ababa, Ethiopia, and Rockville, Maryland, USA: CSA and ICF. 2017;\\u003c/li\\u003e\\n\\u003cli\\u003eWagura P, Wasunna A, Laving A, Wamalwa D, Ng\\u0026rsquo;ang\\u0026rsquo;a P. Prevalence and factors associated with preterm birth at kenyatta national hospital. BMC Pregnancy Childbirth [Internet].\\u003c/li\\u003e\\n\\u003cli\\u003eButali A, Ezeaka C, Ekhaguere O, Weathers N, Ladd J, Fajolu I, et al. Characteristics and risk factors of preterm births in a tertiary center in Lagos, Nigeria. Pan Afr Med J [Internet]. 2016\\u003c/li\\u003e\\n\\u003cli\\u003eAlijahan R, Hazrati S, Mirzarahimi M, Pourfarzi F, Ahmadi Hadi P. Prevalence and risk factors associated with preterm birth in Ardabil, Iran. Iran J Reprod Med. 2014 Jan;12(1):47\\u0026ndash;56.\\u003c/li\\u003e\\n\\u003cli\\u003eZhang Q, Ananth CV, Li Z, Smulian JC. Maternal anaemia and preterm birth: a prospective cohort study. Int J Epidemiol. 2009 Oct 1;38(5):1380\\u0026ndash;9.\\u003c/li\\u003e\\n\\u003cli\\u003eAllen LH. Biological mechanisms that might underlie iron\\u0026rsquo;s effects on fetal growth and preterm birth. J Nutr. 2001;131(2S-2):581S-589S.\\u003c/li\\u003e\\n\\u003cli\\u003eDolan SM, Gross SJ, Merkatz IR, Faber V, Sullivan LM, Malone FD, et al. The Contribution of Birth Defects to Preterm Birth and Low Birth Weight: Obstet Gynecol. 2007 Aug;110(2, Part 1):318\\u0026ndash;24.\\u003c/li\\u003e\\n\\u003cli\\u003eHonein MA, Kirby RS, Meyer RE, Xing J, Skerrette NI, Yuskiv N, et al. The Association Between Major Birth Defects and Preterm Birth. Matern Child Health J. 2009 Mar;13(2):164\\u0026ndash;75.\\u003c/li\\u003e\\n\\u003cli\\u003eYi S-W, Han Y-J, Ohrr H. Anemia before pregnancy and risk of preterm birth, low birth weight and small-for-gestational-age birth in Korean women. Eur J Clin Nutr. 2013 Apr;67(4):337\\u0026ndash;42.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Supplementary File\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eFile name:\\u003c/strong\\u003e Additional file\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDescription\\u003c/strong\\u003e: English Version Questionnaire\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Determinants, preterm birth, newborns, public hospitals, northeast, Ethiopia\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-36781/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-36781/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground: \\u003c/strong\\u003ePreterm birth is a global public health problem. Worldwide, every year about 15 million newborns are born preterm and about 1.1 million of them die due to complications related to prematurity. More than 81% of preterm births were in Asia and Sub-Saharan Africa. Nowadays, preterm birth is the first leading cause of under-five mortality and is one of the limiting factors for achieving a reduction in under-five mortality rate. Therefore, the aim of this study was to identify the determinants of preterm birth at public hospitals in the Northeast Amhara, Ethiopia, 2019.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eMethod: \\u003c/strong\\u003eHospital-based unmatched case-control study was conducted at public hospitals in Northeast Amhara, Ethiopia from March 15\\u003csup\\u003eth\\u003c/sup\\u003e to April 25\\u003csup\\u003eth\\u003c/sup\\u003e, 2019. Data were collected using interviewer-administered structured pre-tested tool and document review for cases and controls. Data were entered in to Epi Info version 7.2.2.6 and exported to SPSS version 23 software for data analysis. Bi-variable and multivariable binary logistic regression were used to measure the association between preterm birth and risk factors. Adjusted Odds ratio (AOR) at 95 % confidence interval (CI) was calculated and P value ≤0.05 declared statistically significant. \\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eResult: \\u003c/strong\\u003eThe study identified that ANC follow up less than four visits [AOR 2.267, Cl (1.205, 4.264)], pregnancy-induced hypertension [AOR 4.453, Cl (1.276, 15.536)], no iron/folic acid supplementation during pregnancy [AOR 4.175, Cl (1.632, 10.680)], anemia during pregnancy [AOR 4.776, Cl (1.492, 15.285)], and birth defect [AOR 6.160, Cl (l.896, 20.013)] were significantly associated determinants of preterm birth.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eConclusion:\\u003c/strong\\u003e Antenatal care follow up fewer than four visits, pregnancy induced hypertension, no iron/folic acid supplementation during pregnancy, being anemic during pregnancy, and birth defect were important determinants of preterm birth.\\u0026nbsp;\\u003c/p\\u003e\",\"manuscriptTitle\":\"Preterm birth determinants among newborns at six public hospitals in Northeast Amhara, Ethiopia: unmatched case-control study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2020-07-02 10:27:27\",\"doi\":\"10.21203/rs.3.rs-36781/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"218d0912-dbc4-4b49-878c-dc87d89a08e3\",\"owner\":[],\"postedDate\":\"July 2nd, 2020\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":139229,\"name\":\"Sexual \\u0026 Reproductive Medicine\"}],\"tags\":[],\"updatedAt\":\"2020-09-26T20:17:19+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2020-07-02 10:27:27\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-36781\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-36781\",\"identity\":\"rs-36781\",\"version\":[\"v1\"]},\"buildId\":\"i_I_jhV-QviBjPLXJga9V\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}