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, 12:2 | Cite as

Proportion of Preterm birth and associated factors among mothers who gave birth in Debretabor town health institutions, northwest, Ethiopia

  • Dawit Gebeyehu Mekonen
  • Ayenew Engida YismawEmail author
  • Tewodros Siyoum Nigussie
  • Worku Mequanint Ambaw
Open Access
Research note
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Abstract

Objective

Each year, about 15 million babies in the world are born too prematurely. Complication of preterm birth is the single largest direct cause of neonatal deaths and the second most common cause of under-5 deaths after infection. Therefore, assessing the proportion of preterm birth and associated factors among Mothers who gave Birth in Debretabor town health institutions have a paramount importance in designing an effective strategy to intervene.

Result

In this study preterm birth was 12.8%. Obstetric complication [AOR = 6.6, 95% CI (3.4–12.6)], maternal Mid Upper Arm Circumference less than 24 cm [AOR = 2.6, 95% CI (1.1–6.1)], antenatal care follow up < 4 visits [AOR = 3.0, 95% CI (1.6–5.9)], being HIV positive [AOR = 5.1, 95% CI (1.7–15.4)], Premature Rupture Of membrane [AOR = 3.0, 95% CI (1.5–6.2)], and being Anemic [AOR = 2.9, 95% CI (1.3–6.6)] were found to be statistically significant. Proportion of preterm birth was high in Debretabor town. Timely identification of obstetric complications and health education to improve antenatal care utilization will minimize the proportion of preterm birth.

Keywords

Preterm birth Proportion Debretabor Town 

Abbreviations

ANC

Ante Natal Care

AOR

adjusted odds ratio

APH

ante-partum hemorrhage

CHTN

chronic hypertension

CI

confidence interval

COR

crud odds ratio

CS

cesarean section

DM

diabetes mellitus

DTH

Debretabor Hospital

EDHS

Ethiopian Demographic and Health Survey

GA

gestational age

HC

Health Center

HCT

hematocrit

HGB

hemoglobin

HIV

human immune deficiency virus

LMICs

lower and middle income countries

MDG

millennium development goal

MUAC

mid upper arm circumference

NICU

Neonatal Intensive Care Unit

PIH

pregnancy induced hypertension

PROM

premature rupture of membrane

SD

standard deviation

SVD

spontaneous vaginal delivery

SPSS

Statistical Package for Social Sciences

UTI

urinary tract infection

WHO

World Health Organization

Introduction

Preterm birth is defined by WHO as all viable births before 37 completed weeks of gestation or fewer than 259 days since the first day of woman’s last menstrual period [1]. Each year, more than one in 10 births, are born too prematurely in the world. Preterm birth is the single largest direct cause of neonatal deaths responsible for 35% and the second most common cause of under-5 deaths a year [2]. About 28% of early neonatal deaths are due to preterm birth [3].

Globally preterm neonates take the first place for neonatal intensive care unit (NICU) admission and longer hospital stay [4]. Complication of the preterm result in significant cost to the health sector, parents and the society. Therefore, the prediction and prevention of preterm birth is a major health care priority [5, 6].

Over 60% of preterm births occur in low- and middle-income countries (LMICs) and consistently risen in most countries [7]. Study on preterm birth highly recommends the need for focused and continuous studies across those nations in order to fill this information gap [8].

Ethiopia had reduced under-5 mortality by 67%, however, the reduction in neonatal mortality is not as impressive and premature birth is the leading cause accounts 37% [9].

Studies estimated preterm birth rates ranged from 5% in developed countries to 26% in developing countries [5]. In 2010 WHO estimated the global rates was 11.1%. From this majority (85%) occurred in sub-Saharan Africa and South Asia [5]. Another retrospective study estimated as 10.2% [10]. One cohort study estimated to be 15.8% [11], in Jeddah, Saudi Arabia, 13.7% [12], in Reliance Region (West of Algeria), 9.26% [13], in Brazilian young parturient women 21.7% and Multicenter Study in Brazil preterm births was 12.3%, ranging from 14.7% in the northeast region to 11.1% in the southeast [14, 15].

A cross-sectional study in the University of Ilorin teaching hospital, Ilorin, Nigeria, reported 11.8% [16], in Cameron and Malawi was 26.5% and 16.3% respectively [17, 18], and in the eastern part of Africa the largest prevalence of preterm was in Kenya 18.3% [19].

Studies conducted in different areas reported risk factors for preterm birth: low socio-economic status, malnutrition, pregnancy-related complications and history of preterm birth [20], age less than 20, PROM, UTI, multiple pregnancy, preeclampsia [15, 24], cervical insufficiency, fetal malformation, polyhydramnios, antepartum hemorrhage, previous abortion, high BMI, suffered from domestic violence [21], history of child death [22], residence, lack of antenatal care and maternal disease [23], null parity and being unmarried [25, 26]. Presence of chronic illness, the absence of antenatal follow up, and hematocrit (HCT) level < 33 were found to be significantly associated with preterm birth [27, 28].

Therefore determining factors has a great role in guiding health professionals and health policy makers to design the intervention strategy and applying necessary preventive and appropriate measures to decrease preterm birth.

Main text

Methods

Institutional based cross-sectional study design was conducted among mothers who gave birth in Debretabor town health institutions from June 1 to September 30, 2016

The source population was mothers who gave birth in Debretabor town health institutions and the study population was those mothers who gave birth during the study period.

By considering single population proportion formula the final sample sizes were 575. Health institutions providing labor and delivery service in the town were systematically selected. Then the sample for each health institution was proportionally allocated based on their patient flow prior to the data collection period. Systematic sampling technique was employed to select the study participants from each health institution. Neonates born at less than 37 completed weeks of gestation but after viability (28 weeks of gestation) were taken as preterm, gestational age was calculated based on LNMP or first-trimester ultrasound result. Maternal nutritional status was assessed by measuring the left middle upper arm circumference (MUAC) using non-stretchable world food program MUAC tapes. Most screening programs have used a cut off of 21–23 cm. Given that there is no international consensus on the cut off to use, a MUAC of < 24 cm was chosen for this study.

Data were collected by face to face interview and chart review using a structured and pre-tested questionnaire. Data were checked, coded and entered into EPI Info 7 and exported to SPSS 20 for analysis. Binary logistic regression was used to identify the associations between the dependent and independent variables. Those variables with a p-value of < 0.2 on binary logistic regression analysis were transferred to multivariable logistic regression. The degree of association was assessed using odds ratio with a 95% confidence interval and variables with a p-value < 0.05 were taken as statically significant.

Results

Socio-demographic characteristics of respondents

A total of 548 participants were completed the interview making 95.3% of response rate. The mean age of the study participants was 27.7 with SD of 5.8. Sixteen years old was the minimum and 41 years old was the maximum ages of clients participated in this study. Majority of the participants were 517 (94.2%) Orthodox Christians in religion. Majority 538 (98.2%) of the respondents were married and one-third 184 (33.4%) of the mothers were secondary and above on their educational status (Table 1).
Table 1

Socio-demographic and economic characteristics of the women delivered in Debretabor town health institution northwest, Ethiopia, from June to September 2016 (N = 548)

Variables

Frequencies

Percent (%)

Residence

 Urban

311

56.8

 Rural

237

43.2

Age

 15–20

46

8.4

 21–25

183

33.4

 26–30

194

35.4

 31–35

66

12

 36+

59

10.2

Marital status

 Married

538

98.2

 Single

10

1.8

Religion

 Orthodox

516

94.2

 Muslim

32

5.8

Educational status of the client

 Unable to read and write

162

29.6

 Read and write only

62

11.3

 Primary education

141

25.7

 Secondary and above

183

33.4

Occupational status of the client

 Housewife

103

18.8

 Gov’t employer

101

18.4

 Private employee

106

19.3

 Daily laborer

10

1.8

 Farmer

228

41.6

Educational status of the husband

 Unable to read and write

110

20.1

 Read and write only

103

18.8

 Primary education

111

20.3

 Secondary and above

224

40.8

Income

 ≤ 1233 birr

398

72.6

 > 1233 birr

150

27.4

Family size

 ≤ 5

447

81.6

 > 5

101

18.4

*p value < 0.05, **p value < 0.001

Obstetric and medical related characteristics

Majority of the respondents 511 (93.3%) had ANC follow up and three-fifth of them had at least 4 visits 326 (59.5%). Nearly all of the respondents had used modern contraceptive 488 (89%) prior to their pregnancy and majority of them had their pregnancy wanted and planned 496 (90.4%). More than two-fifth of the respondents had birth to pregnancy interval greater than or equal to 36 months 231 (42.2%). One in ten mothers had Mid Upper Arm Circumference (MUAC) less than 24 cm (10%). Labour spontaneously started in 91.6% of the respondents. One-sixth of participants had one or more obstetric complications 92 (16.8%) (Table 2).
Table 2

Obstetric and medical related characteristics of the women delivered in Debretabor town health institution northwest, Ethiopia, from June to September 2016 (N = 548)

Variables

Frequency

Percent (%)

MUAC

 < 24 CM

55

10

 ≥ 24 CM

493

90

Parity

 Primi para

233

42.5

 Para 2–5

250

45.6

 Para > 5

65

11.9

ANC follow up

 Yes

511

93.2

 No

37

6.8

Number of ANC visit

 < 4 times

222

40.5

 ≥ 4 times

326

59.5

Pregnancy status

 Planned

496

90.4

 Unplanned

52

9.6

Modern contraceptive use prior to current pregnancy

 Yes

488

89

 No

60

11

Birth to pregnancy interval in months

 None

233

42.5

 < 36

84

15.3

 ≥ 36

231

42.2

Poor obstetric history

 Preterm

14

2.6

 Still birth

18

3.3

 Abortion

101

18.4

Mode of delivery

 SVD

412

75.3

 Instrumental delivery

59

10.7

 C/S

77

14

Status of labor

 Spontaneous

501

91.6

 Induced

33

5.9

 Elective C/S

14

2.5

History of HIV testing

 Yes

523

95.6

 No

25

4.4

Status of HIV

 Positive

27

4.9

 Un known

25

4.6

 Negative

496

90.5

Blood RH factor

 Positive

534

97.4

 Negative

14

2.6

PROM

 Yes

68

12.4

 No

480

87.6

HGB (mg/dl)

 < 11

52

9.5

 ≥ 11

496

90.5

GA at delivery

 Preterm

70

12.8

 Term

415

75.7

 Post term

63

11.5

Obstetric complication

 Yes

92

16.8

 No

456

83.2

Type of obstetric complication

 APH

42

7.7

 PIH

27

4.9

 Multiple pregnancy

15

2.7

 Polyhydraminous

8

1.5

History of medical illness

 Yes

60

10.9

 No

488

89.1

Type of medical illness

 CHTN

13

2.4

 DM

8

1.5

 Heart failure

9

1.6

 Asthma

5

0.9

 UTI

19

3.5

 Malaria

6

1.0

Proportion of preterm birth

The proportion of preterm birth in this study was 12.8% [95% CI (9.9%, 15.7%)] and 11.5% was post term.

Factors associated with preterm birth

After multivariable logistic regression, number of ANC visits less than 4 times, MUAC less than 24 cm, having PROM, being anemic, the complication in current pregnancy and being HIV positive were found to be statistically significant at a p-value of < 0.05.

Mothers who had ANC visits < 4 times in the index pregnancy were 3.3 times more likely to have a preterm birth than mothers who had ANC visits ≥ 4 times [AOR = 3.0, 95% CI (1.6–5.9)]. Mothers who had MUAC < 24 CM were 2.6 times more likely to develop preterm birth than their counterparts [AOR = 2.6, 95% CI (1.1–6.1)]. Obstetric complications during the index pregnancy were 6.6 times more likely to develop preterm birth than mothers without any of the mentioned problems [AOR = 6.6, 95% CI (3.4–12.6)]. Being HIV positive had 5.1 times increased the risk of giving preterm birth than their counterparts [AOR = 5.1, 95% CI (1.7–15.4)]. Having anemia in the index pregnancy increased the risk of preterm birth by 2.9 times [AOR = 2.9, 95% CI (1.3–6.6)] (Table 3).
Table 3

Factors associated with pre term birth among of the women delivered in Debretabor town health institution northwest, Ethiopia, from June to September 2016 (N = 548)

Variables

Preterm

COR (95% CI)

AOR (95% CI)

p-value

Yes

No

Residence

 Rural

42

195

2.17 (1.3–3.6)*

  

 Urban

28

283

1

 

ANC follow up

 No

15

22

5.65 (2.8–11.5)*

  

 Yes

55

456

1

  

Number of ANC visits

 < 4

50

172

4.4 (2.5–7.7)*

3.0 (1.6–5.9)*

0.001

 ≥ 4

20

306

1

1

 

History of preterm delivery

 Yes

6

8

5.5 (1.8–16.4)*

  

 No

64

470

1

  

History of abortion

 Yes

21

80

2.1 (1.2–3.7)*

  

 No

49

398

1

  

Anemia

 Yes

17

35

4.06 (2.1–7.7)*

2.9 (1.3–6.6)*

0.01

 No

53

443

1

  

prom

 Yes

22

46

4.3 (2.3–7.7)*

3.0 (1.5–6.2)*

0.002

 No

48

432

1

1

 

MUAC (cm)

 < 24

12

43

2.09 (1.04–4.2)*

2.6 (1.1–6.1)*

0.022

 ≥ 24

58

435

1

1

 

Complication in current pregnancy

 Yes

37

55

8.6 (4.99–14.9)*

6.6 (3.4–12.6)**

< 0.001

 No

33

423

1

  

Status of HIV

 Positive

12

15

6.4 (2.8–14.5)*

5.1 (1.7–15.4)*

0.003

 Un known

3

22

1.0 (0.3–3.7)

  

 Negative

55

441

1

1

 

History of medical illness

 Yes

17

43

3.2 (1.7–6.1)*

  

 No

53

435

1

 

*p value < 0.05, **p value < 0.001

Discussion

In this study the proportion of preterm birth was found to be 12.8% with 95% CI (9.9%–15.7%). This finding was in line with findings in Africa (11.9%) and North America (10.9%) [5], the prevalence of preterm birth in Ethiopia 10.1% [29], a study conducted in Debremarkos town health institutions 11.6% [27], and Gondar university hospital 14.3% [28].

The current finding was higher than the findings conducted in Sweden 5.03% [31], in Gondar town health institutions 4.4% [30]. This might be due to the difference in study time, setting, and design used. However, this finding was lower than findings reported in Malawi 16.3% [18], Brazil 21.7% [14], Nigeria 23.7% [24]. This variation might be because of the difference in a study area, design, time, population and sociocultural variations.

Having pregnancy complications during index pregnancy were 6.6 times more likely to have a preterm birth than mothers without these problems [AOR = 6.6, 95% CI (3.4–12.6)]. This finding was parallel with findings in Ethiopia [30], Nigeria [24] Bangladesh [22], Brazil [14] and Kenya [19]. This might be due to that obstetric complications result in medical induced or spontaneous preterm delivery.

The current study showed that having PROM were 3 times more likely to have a preterm birth than their counterparts [AOR = 3.0, 95% CI (1.5–6.2)]. This finding was similar with findings in Tehran, Iran, [32], in Kenya [19] and in Debremarkos town, Ethiopia [27]. This might be due to that labor will spontaneously initiate within hours after term PROM and within a week after preterm PROM in the majority of the cases.

In this finding being anemic were 2.9 times more likely to have a preterm birth than their counterparts [AOR = 2.9, 95% CI (1.3–6.6)]. This finding was agreed with findings in Saudi Arabia [12], in Ethiopia [27] and in Malawi [18]. This might be due to anemia predisposes to decreased blood flow to the placenta causing placental insufficiency and result in preterm delivery.

In this study, mothers with less than four times ANC visit were 3 times at risk to give preterm birth than those with four and more visits [AOR = 3.0, 95% CI (1.6–5.9)]. This finding is in line with a study in Cameron [17]. This might be frequent ANC visit maximizes the opportunity of health promotion, early detection, and treatment of obstetric complications.

The current finding showed that maternal MUAC less than 24 cm were 2.6 times increased risk of developing preterm birth than mothers with MUAC greater than or equal to 24 cm with [AOR 2.6, 95% CI (1.1–6.1)]. This finding was agreed with a study in Bangladesh [22]. This might be maternal nutritional status had a direct effect on placental size, fetus and strength of the membrane resulted in preterm delivery.

This study showed that HIV positive mothers were 5.1 times at increased risk of having a preterm birth than their counterparts [AOR = 5.1, 95% CI (1.7–15.4)]. This finding is in line with a study in Ethiopia [30]. This might be due to the drug effect and immunity of the mother at risk for preterm birth.

Conclusion

The proportion of preterm birth was high. Problem with current pregnancy, being anemic, being HIV positive, MUAC less than 24 cm and ANC visit less than four times were found to be statistically significant for preterm birth in the current pregnancy. So that, Ministry of health need to engage on educating the community to improve ANC service utilization through different methods and Upgrade capacity of health institutions to identify treat obstetric complications and nutritional counseling for mothers during their ANC follow up.

Limitation

This study was conducted through a cross-sectional study and may not show the cause and effect relationship.

Notes

Authors’ contributions

DGM and AEY conceived and design the idea, participated in the data collection process, analyze data and wrote the paper. TSN and WMA participated in data analysis and wrote the paper. All authors read and approved the final manuscript.

Acknowledgements

We would like to thank study participants for their volunteer participation and their time devotion. Our heartfelt thanks also are given for Debretabor health Institutions’ staffs, administrative staffs, data collectors for their contribution to accomplishing this thesis.

Finally, we would like to acknowledge, University of Gondar collage of medicine and health sciences, School of midwifery for ethical approval to prepare this thesis.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

Data will be shared up on request and will be obtained by email to the author using “davegeby@gmail.com”.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Ethical clearance was obtained from the Ethical review board (IRB) of the University of Gondar, College of Medicine & Health Sciences School of Midwifery. Permission letters were also sought from Debretabor town Administrations Health Bureau. Written informed consent from the mother was obtained after a clear explanation of the purpose of the study. For minor participants age [16, 17] additional consent was obtained from their parents for participation. Confidentiality and anonymity were maintained.

Funding

The authors have also declared that no financial support in the research, authorship and publication of this article was received.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.
    Lumley J. Defining the problem: the epidemiology of preterm birth. BJOG Int J Obstet Gynaecol. 2003;110(SUPPL. 20):3–7.CrossRefGoogle Scholar
  2. 2.
    Blencowe H, Cousens S, Chou D, Oestergaard M, Say L, Moller A-B, et al. Born too soon: the global epidemiology of 15 million preterm births. Reprod Health. 2013;10:S2.CrossRefGoogle Scholar
  3. 3.
    Dey AC, Mannan A, Saha L, Hossain I. Review article magnitude of problems of prematurity- national and global perspective: a review. Bangladesh J Child Health. 2012;36(3):146–52.CrossRefGoogle Scholar
  4. 4.
    Brown HK, Speechley KN, Macnab J, Natale R, Campbell MK. Neonatal morbidity associated with late preterm and early term birth: the roles of gestational age and biological determinants of preterm birth. Int J Epidemiol. 2014;43(3):802–14.CrossRefGoogle Scholar
  5. 5.
    Beck S, Wojdyla D, Say L, Betran AP, Merialdi M, Requejo JH, et al. The worldwide incidence of preterm birth: a systematic review of maternal mortality and morbidity. Bull World Health Organ. 2010;88(1):31–8.CrossRefGoogle Scholar
  6. 6.
    Johnston KM, Gooch K, Korol E, Vo P, Eyawo O, Bradt P, et al. The economic burden of prematurity in Canada. BMC Pediatr. 2014;14(1):93.  https://doi.org/10.1186/1471-2431-14-93.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Lawn JE, Gravett MG, Nunes TM, Rubens CE, Stanton C. Global report on preterm birth and stillbirth (1 of 7): definitions, description of the burden and opportunities to improve data. BMC Pregnancy Childbirth. 2010;10(Suppl 1):S1.CrossRefGoogle Scholar
  8. 8.
    Marchant T, Willey B, Katz J, Clarke S, Kariuki S, ter Kuile F, et al. Neonatal mortality risk associated with preterm birth in east africa, adjusted by weight for gestational age: individual participant level meta-analysis. PLoS Med. 2012;9(8):292.CrossRefGoogle Scholar
  9. 9.
    Ministry of Health. Health sector transformation plan (2015/16–2019/20). New Delhi: Ministry of Health; 2015.Google Scholar
  10. 10.
    Arora CP, Kacerovsky M, Zinner B, Ertl T, Ceausu I, Rusnak I, et al. Disparities and relative risk ratio of preterm birth in six Central and Eastern European centers. Croat Med J. 2015;56(2):119–27.CrossRefGoogle Scholar
  11. 11.
    Rouget F, Lebreton J, Kadhel P, Monfort C, Bodeau-Livinec F, Janky E, et al. Medical and sociodemographic risk factors for preterm birth in a French caribbean population of african descent. Matern Child Health J. 2013;17(6):1103–11.CrossRefGoogle Scholar
  12. 12.
    Alabbasi KH, Kruger E, Tennent M. Maternal variables as potential modifiable risk indicators of preterm labor in Jeddah, Saudi Arabia. J Pregnancy Child Health. 2015;2(3):3–6.CrossRefGoogle Scholar
  13. 13.
    Demmouche A, Mai AH, Kaddouri MS, Ghani A, Rahmani S, Beddek F, et al. Etiology of preterm birth in Relizane region (West of Algeria). J Nutr Food Sci. 2014;4(5):292.Google Scholar
  14. 14.
    Miranda AE, Pinto VM, Szwarcwald CL, Golub ET. Prevalence and correlates of preterm labor among young parturient women attending public hospitals in Brazil. Rev Panam Salud Public. 2012;32(5):330–4.CrossRefGoogle Scholar
  15. 15.
    Passini R, Cecatti JG, Lajos GJ, Tedesco RP, Nomura ML, Dias TZ, et al. Brazilian multicentre study on preterm birth (EMIP): prevalence and factors associated with spontaneous preterm birth. PLoS ONE. 2014;9(10):12–21.CrossRefGoogle Scholar
  16. 16.
    Mokuolu OA, Suleiman BM, Adesiyun OO, Adeniyi A. Prevalence and determinants of pre-term deliveries in the University of Ilorin Teaching Hospital, Ilorin, Nigeria. Pediatr Rep. 2010;2(1):11–4.CrossRefGoogle Scholar
  17. 17.
    Mah E, Nguefack S, Tchokoteu PF, Mah EM, Mvondo N, Nguefack S, et al. Women â€TM s Health and Action Research Centre (WHARC) risk factors for premature births: a cross-sectional analysis of hospital records in a cameroonian health facility published by: Women â€TM s Health and Action Research Centre (WHARC) Stable URL: htt. Women’s Health Action Res Cent. 2016;17(4):77–83.Google Scholar
  18. 18.
    Van Den Broek NR, Jean-Baptiste R, Neilson JP. Factors associated with preterm, early preterm and late preterm birth in Malawi. PLoS ONE. 2014;9(3):1–8.Google Scholar
  19. 19.
    Wagura PM. Prevalence and factors associated with preterm birth. MBChB (Moi). 2014;58:1–49.Google Scholar
  20. 20.
    Shubhada S, Kambale S, Phalke B. Determinants of preterm labour in a rural medical college hospital in western Maharashtra. Nepal J Obstet. 2013;8(1):31–3. http://www.nepjol.info/index.php/NJOG/article/view/8858.
  21. 21.
    da Silveira MF, Santos IS, Barros AJD, Matijasevich A, Barros FC, Victora CG. Aumento da prematuridade no Brasil: revis??o de estudos de base populacional. Rev Saude Publica. 2008;42(5):957–64.CrossRefGoogle Scholar
  22. 22.
    Shah R, Mullany LC, Darmstadt GL, Mannan I, Rahman SM, Talukder RR, et al. Incidence and risk factors of preterm birth in a rural Bangladeshi cohort. BMC Pediatr. 2014;14(1):112.CrossRefGoogle Scholar
  23. 23.
    Olusanya BO, Ofovwe GE. Predictors of preterm births and low birthweight in an inner-city hospital in sub-Saharan Africa. Matern Child Health J. 2010;14(6):978–86.CrossRefGoogle Scholar
  24. 24.
    Onankpa BOIK. Pattern of preterm delivery and their outcome in a tertiary hospital. Int J Health Sci Res. 2014;4(3):59–65.Google Scholar
  25. 25.
    Iyoke CA, Lawani LO, Ezugwu EC, Ilo KK, Ilechukwu GC, Asinobi IN. Maternal risk factors for singleton preterm births and survival at the University of Nigeria Teaching Hospital, Enugu, Nigeria. Niger J Clin Pract. 2015;18(6):744–50.CrossRefGoogle Scholar
  26. 26.
    Kunle-Olowu OE, Peterside OAO. Prevalence and outcome of preterm admissions at the neonatal unit of a tertiary health centre in southern Nigeria. Open J Pediatr. 2014;4:67–75. http://file.scirp.org/Html/9-1330299_43766.htm.
  27. 27.
    Abdela Amanon TB. Preterm birth and associated factors among mothers who gave birth in Debremarkos Town Health Institutions, 2013 Institutional Based Cross Sectional Study. Gynecol Obstetric. 2015;5(292):5. http://www.omicsonline.org/open-access/preterm-birth-and-associated-factors-among-mothers-who-gave-birth-indebremarkos-town-health-institutions-2013-institutional-based-crosssectional-study-2161-0932-1000292.
  28. 28.
    Adane AA, Ayele TA, Ararsa LG, Bitew BD, Zeleke BM. Adverse birth outcomes among deliveries at Gondar University Hospital, Northwest Ethiopia. BMC Pregnancy Childbirth. 2014;14(90):8.Google Scholar
  29. 29.
    Blencowe H, Cousens S, Oestergaard DC, et al. Lawn data from national, regional and worldwide estimates of preterm birth rate. Glob Action Rep Preterm Birth. 2010;4:1–2.Google Scholar
  30. 30.
    Gebreslasie K. Preterm birth and associated factors among mothers who gave birth in Gondar Town Health Institutions. Hindawi Publ Corp Adv Nurs. 2012;2016:5.Google Scholar
  31. 31.
    Cnattingius S, Villamor E, Johansson S, EdstedtBonamy A-K, Persson M, Wikstrom A-K, et al. Maternal obesity and risk of preterm delivery. J Am Med Assoc. 2013;309(22):2362–70.CrossRefGoogle Scholar
  32. 32.
    Tehranian N, Ranjbar M, Shobeiri F. The prevalence rate and risk factors for preterm delivery. J Midwifery Reprod Heal. 2015;5:1–5.Google Scholar

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© The Author(s) 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Dawit Gebeyehu Mekonen
    • 1
  • Ayenew Engida Yismaw
    • 1
    Email author
  • Tewodros Siyoum Nigussie
    • 1
  • Worku Mequanint Ambaw
    • 1
  1. 1.College of Medicine and Health Science, School of MidwiferyUniversity of GondarGondarEthiopia

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