Skip to main content

Prevalence of preterm birth and associated factors among mothers who gave birth in public hospitals of east Gojjam zone, Ethiopia

Abstract

Backgrounds

Preterm birth is defined as babies born alive before 37 weeks of pregnancy or fewer than 259 days since the first day of a woman’s last menstrual period. Globally, 14.84 million babies were preterm births. Preterm infants are at risk for specific diseases related to the immaturity of various organ systems. This study aimed to assess the prevalence of preterm birth and associated factors among mothers who gave birth in public hospitals of east Gojjam zone, Ethiopia.

Methods

An institutional-based cross-sectional study was conducted from April 1 up to June 30, 2021, in public hospitals in the east Gojjam zone. Systematic random sampling was used. Data were collected through structured questionnaires, patient interviews and patient card reviews. We used binary logistic regression analysis with 95% CI and P-value < 0.05 to identify the significant factors with preterm birth.

Results

Out of 615 mothers, 13.2% gave a preterm birth. Antenatal care (AOR = 2.87; 95% CI = (1.67, 5.09)), educational status of mother (AOR = 2.79; 95% CI = (1.27, 6.67)), husband educational status(AOR = 2.11; 95% CI = (1.10, 4.18)), Average monthly family income(AOR = 1.95; 95% CI = (1.05, 3.75)),family size(AOR = 0.15; 95% CI = (0.03, 0.67)), multifetal gestation (AOR = 3.30; 95% CI = (1.29, 8.69), having Premature Rupture Of Membrane (AOR = 6.46; 95% CI= (2.52, 18.24)), history of chronic illness (AOR = 3.94; 95% CI = (1.67, 9.45)), being HIV positive(AOR = 6.99; 95% CI= (1.13, 44.65)), Ante-Partum Hemorrhage (AOR = 3.62; 95% CI= (1.12, 12.59)), pregnancy Induced Hypertension (AOR = 3.61; 95% CI= (1.19, 11.84)), mode of delivery (AOR = 7.16; 95% CI = (2.09, 29.29)), and onset of labor (AOR = 0.10; 95% CI = (0.03, 0.29)) were found to be significantly associated with preterm birth.

Conclusions

antenatal care, educational status of the mother, husband’s educational status, family income, family size, multifetal gestation, Premature Rupture of the membrane, history of chronic illness, being HIV positive, Ante-Partum Hemorrhage, pregnancy Induced Hypertension, mode of delivery, and the onset of labor were found to be significantly associated with preterm birth. To minimize the proportion of preterm birth focusing on this important variables, timely identification of obstetric complications, strengthening early screening of HIV and high-risk pregnancies like multiple gestations, PIH and APH were important.

Peer Review reports

Background

Preterm birth (PTB) is defined as babies born alive before 37 weeks of pregnancy or fewer than 259 days since the first day of a woman’s last menstrual period [1]. Globally, 14.84 million babies were preterm births. The majority of these births occurred in Asia and sub-Saharan Africa [2]. Preterm birth is a global problem, with 60% of preterm births occurring in Africa and South Asia. On average, 12% of babies born in the poorest countries are premature, compared with 9% in higher-income countries [3]. Direct complications of preterm birth account for one million deaths yearly, and preterm birth is a risk factor in over 50% of all neonatal deaths [4].

Preterm infants have a higher risk of developing specific diseases due to the immaturity of various organ systems and the causes of preterm birth. As a result of their prematurity, preterm babies are subjected to serious illnesses or deaths during their neonatal period. In the absence of appropriate treatment, survivors are more likely to suffer a lifelong disability and a compromised quality of life. Prematurity complications are the leading cause of neonatal death and the second leading cause of death among children under 5 years old. [5]. Preterm birth complications were the leading cause of death in children younger than 5 years of age globally, accounting for approximately 16% of all deaths and responsible for 35% of deaths among newborn babies [6]. Prematurity now takes the first place for neonatal intensive care unit (NICU) admission, longer hospital stay, the second leading cause of death in children under 5 years, and the single most important direct cause of death in the critical first month of life of infants [7, 8].

In low- and middle-income (LMIC) countries, preterm births account for more than 60% of all births, and the rate has steadily increased. Despite this high preterm birth rate, it is challenging to determine the trend of preterm birth in the majority of low-income economies due to a lack of accurate data [9]. Sub-Saharan African countries have a high preterm birth rate: 23.7% in Nigeria [10], 18.3% in Kenya [11], and 16.3% in Malawi [12]. The overall prevalence of preterm birth in Ethiopia was 10.48% [13]. There is a high rate of infant mortality (48 deaths per 1,000 live births) and neonatal mortality (29 deaths per 1,000 live births) and complication of preterm birth is a major risk factor of this mortality [14]. In the Amhara region, the prevalence of preterm birth was 11.41% [15].

The complication of infants born at preterm gestational age results in a trivial cost to the health sector, parents, and society. The prediction and prevention of preterm birth is a major health care priority [16]. Global efforts to further reduce child mortality demand urgent action to address preterm birth. However, it is a complex multifactorial process associated with diverse pathogenic mechanisms and the prevalence of preterm delivery is one of the strongest predictors of neonatal mortality in our country. During the neonatal period, preterm babies are at a higher risk of serious illness or death. Those who survive preterm delivery without proper care are at great risk of chronic impairment and poor quality of life [17].

Previous studies conducted in different regions indicated that several risk factors were identified for preterm birth. This includes having a previous preterm birth, having a short cervix, short intervals between pregnancies, and certain pregnancy complications (including multiple pregnancy, pregnancy-induced hypertension, premature rupture of the membrane, and vaginal bleeding), chronic illness, educational status, Multiple pregnancies, maternal age, residing in rural areas, antenatal care visits, being HIV positive, family number, and income [15, 18,19,20,21,22,23,24,25,26].

PTB is a major public health problem. However, in most low-income countries, including Ethiopia, little emphasis is given to PTB intervention as a means of reducing infant mortality. Health care providers or other stakeholders who worked in this public health problem need data related to common factors associated with preterm birth. However, in our country, the studies conducted about this problem were minimal. Although few studies have been conducted in some areas of Ethiopia, the magnitude and possible risk factors of PTB vary by area. A number of methodological issues are addressed in our study (sample size calculation, sampling technique, and multicenter study area) that were not considered in previous studies. Studying a large region is essential to designing effective interventions and programs in public health. Most of the previous study focused mainly on the prevalence of PTB, rather than associated factors of PTB. In addition, different cultures and socioeconomic statuses within a society have varied factors of preterm birth. Therefore studying in different and multicenter settings was important. Determining preterm birth prevalence and associated factors greatly guides health professionals and health policymakers to identify indicators for monitoring preterm birth strategy and applying necessary preventive and appropriate measures to decrease preterm birth. Moreover, the study area’s magnitude of preterm births and associated factors were unknown. Therefore, this study was conducted to assess the prevalence of preterm birth and to identify factors associated with preterm birth in public hospitals in the east Gojjam zone.

Methods

Study design

The hospital based cross-sectional study design was conducted using interviewer administered questionnaire from April 1 up to June 30, 2021. Additional information was obtained from medical records of the mothers and babies.

Study area

The study was conducted in public hospitals in the east Gojjam zone, Amhara National Regional State, Ethiopia. Debre Markos city is a zonal administrative city. It covers 14,010 square kilometers and is divided into 18 administrative districts, further subdivided into 49 urban and 392 rural kebeles, the smallest administrative units [27]. The East Gojam zone is located in northwest Ethiopia, which is 265 Km far from Bahirdar, the capital city of Amhara region, 299 km from Addis Ababa, the country’s capital. The Oromia region borders the zone on the south, West Gojjam on the west, South Gondar on the north, and South Wollo on the east. With a population of 3.8 million, the East Gojjam Zone has 21 Woreda, 480 Kebeles, 10 government hospitals (1 referral hospital, 9 primary hospitals), 102 health centers, and 423 health posts [28,29,30]. This study was conducted in five randomly selected public hospitals. The randomly selected public hospitals were Shegaw Motta general hospital, Bichena primary hospital, Dejen primary hospital, Lumame primary hospital, and Debre Markos referral hospital. All these health institutions are currently providing maternal and child health care services.

Participants

All mothers who gave birth in randomly selected public hospitals in the east Gojjam zone during the study period were our study population. Mothers who gave birth and had known either LNMP or had early ultrasound (before 24 weeks) diagnosis were included for this study. Mothers who were deaf and comatose, had unknown last normal menstrual period (LNMP) and had no early ultrasound (before 24 weeks) were excluded from this study.

Sampling technique and sample size determination

A systematic sampling technique was used. The sample was arranged based on the three-month patient flow before the data collection period by referring to the hospital’s delivery registration book/ record. To calculate K, the summation of the three months delivery report for the hospital was 1698.Then K = N/n, 1698/615 = 2.8 ≈ 3. Where k = interval, N = total population, n = sample size. Every third mother’s was interviewed, gestational age of the newborn was calculated based on the mothers LNMP or first-trimester ultrasound result, in estimating gestational week, when there are extra days it was counted to the near lowest gestational age. The next participant was taken when the selected study participant was not eligible for the study.

The sample size was determined by using a single population proportion formula by considering the prevalence (p) of preterm birth = 15.5%, which was obtained from the previous study in Ethiopia [17], 95% confidence interval (\(z\frac{\alpha }{2}\)=1.96), and level of precision (d) = 0.03.

$$n=\frac{\left({{Z}_{\frac{\alpha }{2}}}^{2}\right)\left(p\right)(1-p)}{{d}^{2}}$$
(1)
$$n=\frac{{ 1.96}^{2}\left(0.155\right)\left(0.845\right)}{\left({0.03}^{2}\right)}=559$$
(2)

Finally, after taking a 10% non-response rate the total sample size (n) was 615. An average delivery report for a month before the actual data collecting period was computed for each hospital by analyzing the client’s registration book to distribute the sample size proportionally to each hospital. The sample size was then proportionally allocated to each hospital. Finally, the immediate postnatal mother with her baby had been selected every three intervals using a systematic sampling technique.

Data collection

Data were collected and extracted by reviewing medical records of mother’s .The data collectors were four fourth year undergraduate midwives students from Debre Markos University. One staff midwife from each hospital was assigned to supervise the data collection process. The data collection process was supervised by both the principal investigator and the supervisor. Information collected from the mother included Residency, Age, Religion, Education status, Occupation, Marital status, Average monthly Average monthly family income, Antenatal Care (ANC), inter pregnancy interval parity, Educational Status of husband, Ante-Partum Hemorrhage (APH), Premature Rupture Of Membrane (PROM), Pregnancy Induced Hypertension (PIH), multiple pregnancies, polyhydramnios, anemia, cardiac disease, hypertension, HIV status, Modern contraceptive use before current pregnancy, Urinary Tract Infection (UTI) and malaria were the predictor variable for this study. Anemia was defined as an HGB level below 11gm/dl (HCT < 33%). Obstetric complications was defined as challenges or problems that happen during labor or delivery. PIH was defined clinically as a blood pressure of > 140/90 mmHg after 20 weeks of gestation with or without proteinuria and/or edema as diagnosed and documented by the attending clinician. APH was defined as any vaginal bleeding in the mother after 24 weeks of gestation as documented in the records by the attending clinician. UTI was defined as a documented clinical/laboratory diagnosis of UTI any time during the pregnancy and/or a positive history of treatment of burning sensation with micturition as reported by the mother. Birth to pregnancy interval was defined as the time between the start of the index pregnancy and the preceding live birth. Last normal menstrual period was defined as the date of the starting of last normal menstruation the women had to index pregnancy. Preterm birth was defined as a newborn with a gestational age of 28 weeks to less than 37 weeks. To assure the quality of data, the questionnaire was pre-testing on 31 mothers in public hospitals in the east Gojjam zone, and the questionnaire’s fitness was confirmed from pre-testing. One-day practical training on how to collect data was given to the data collectors and the supervisor before data collection. During the data collection period, the collected data were reviewed, checked for completeness, and signed by the supervisor at the end of each day.

Data processing and analysis

All questionnaires were checked, coded, and entered into the SPSS version 25 software packages, which were then analyzed using R 4.1.3. The data were presented using frequency tables and graphs. The relevant determinants of preterm birth were identified using binary logistic regression. The researchers used both bivariable and multivariable analyses. In the bivariable analysis, independent variables with a p-value less than 0.25 were chosen for the multivariable analysis. An adjusted odds ratio with a 95% confidence level was used to examine the degree of relationship between independent and dependent variables, and variables with a p-value of 0.05 were considered statistically significant.

Results

Socio-demographic characteristics the of the respondents

All participants completed the interview (100% response rate). The majority of the respondents 358 (58.2%) were rural residents. The majority of the study participants 360 (58.5%) were between 25 and 34 years old. The entire respondent belongs to Amhara by ethnicity, and 516 (96.1%) were Orthodox Christians in religion. Regarding the marital status of the respondents, the majority of them 596(96.9%) were married. 253 (41.1%) were have no formal education, 217(35.3%) of the respondents were taken education in primary school, and 253(41.1%) of them can only read and write, and the remaining 145 (23.6%) were secondary and above education level. The occupational status of most of the respondents 228(37.1%) were farmers, 144 (23.4%) of the respondents were housewives, 150 (24.4%) of the respondents were employers, and the remaining respondents had other occupations (labor work, no work, students, work-seekers). 264 (42.9%) of the husband were have no formal education. 264(42.9%) of the respondent’s family average monthly income is less than or equal to 3000 Birr and 212(34.5%) of the respondent’s family average monthly income is average greater than 5000 Birr. The majority of the respondents, 415 (67.5%) had between 3 and 5 family members (Table 1).

Table 1 Socio-Demographic Characteristics of the study participants in public hospitals of east Gojjam zone, Ethiopia, 2021 from April to June

Obstetric and medical-related characteristics

Most of the respondents (96.3%) had ANC follow up and 56.1% of the respondent had at least 4 visits. The majority of the respondents used modern contraceptives (94.8%) before their pregnancy, and the majority of mothers had their pregnancy wanted and planned (99.3%). More than two-thirds of the respondents had birth-to-pregnancy intervals greater than or equal to 36 months (83.5%). Labor spontaneously started in 87.8% of the respondents. Three-quarters of the respondents (74.3%) gave birth by SVD. Out of the total respondents, 595(96.7%) were tested for HIV status, and 36 (5.7%) respondents were HIV positive. (133) 21.6% of women had APH, (142) 23.1%, of women had PIH, (70) 11.4%, of mothers had multiple pregnancies, and women had Polyhydramnios (10) 1.6%. 105(17.1%) of the respondents had a history of chronic illness. 70(11.4%) of mothers had urinary tract infection (Table 2).

Table 2 Obstetric and medical related characteristics of the study participants in public hospitals of east Gojjam zone, Ethiopia, 2021 from April to June

The proportion of preterm birth

The proportion of preterm birth in this study was found to be 13.2% (CI: 0.11, 0.16) (Fig. 1).

Fig. 1
figure 1

proportion of preterm birth in the east Gojjam zone

Factors associated with preterm birth

All independent variables were analyzed using binary logistic regression with the dependent variable preterm birth and those, which were significant at a p-value of < 0.25 were transferred to multivariable logistic regression analysis. The variable with a p-value < 0.05 was significant.

In bivariable analysis ANC follow-up, educational status of mother, husband educational status, family income, marital status, occupation, family size, PROM, being HIV positive, obstetric complication, APH, PIH, history of chronic illness, multifetal gestation, RH factor, pregnancy status, mode of delivery, onset of labor, and UTI were found to be significantly associated with pre term birth. In multivariable binary logistic regression analysis ANC follow up (AOR = 2.87; 95% CI = (1.67, 5.09)), educational status of mother (AOR = 2.79; 95% CI = (1.27, 6.67)), husband educational status(AOR = 2.11; 95% CI = (1.10, 4.18)), Average monthly family income(AOR = 1.95; 95% CI = (1.05, 3.75)),family size(AOR = 0.15; 95% CI = (0.03, 0.67)), multifetal gestation (AOR = 3.30; 95% CI = (1.29, 8.69), having PROM(AOR = 6.46; 95% CI= (2.52, 18.24)), history of chronic illness (AOR = 3.94; 95% CI = (1.67, 9.45)), being HIV positive(AOR = 6.99; 95% CI= (1.13, 44.65)), APH(AOR = 3.62; 95% CI= (1.12, 12.59)), PIH (AOR = 3.61; 95% CI= (1.19, 11.84)), mode of delivery (AOR = 7.16; 95% CI = (2.09, 29.29)), and onset of labor (AOR = 0.10; 95% CI = (0.03, 0.29)) were found to be statistically significant at p-value of < 0.05 (Table 3).

Table 3 Factors associated with preterm birth among study participants in public hospitals of east Gojjam zone, Ethiopia, 2021 from April to June

Discussion

This study was conducted to assess the magnitude of preterm birth and its associated factors in public hospitals in the east Gojjam zone. During the study period, the overall proportion of preterm birth was found to be 13.2%. The finding was greater than the preterm birth rate for the world (9.8%) and North America (9%) [2, 31]. It was also more than the prevalence of preterm birth in Ethiopia, 10.1%, which was reported by the Global Action Report on Preterm Birth [32]. Compared with cross-sectional studies conducted in our country the finding was found to be in line with the finding conducted in Debre Tabor town health institutions, which was 12.8%, and with the finding conducted in Axum and Adwa town public hospitals in which the prevalence of preterm birth was 13.3% [33, 34].

The proportion of preterm birth in this study however was higher than another study conducted in our country at Gondar town health institutions, which reported a prevalence rate of 4.4% [18]. This discrepancy may be due to differences in exclusion criteria for multiple pregnancies. In our study, mothers with multiple pregnancies were included, whereas these mothers were excluded from the mentioned study. Therefore, a lower rate was expected in their study, as over distention of the uterus as in multiple pregnancies and polyhydramnios is one of the scientifically explained causative factors for preterm labor. The prevalence of preterm was also higher than the prevalence of most developed nations. The preterm birth rate from the study conducted in Sweden which was estimated to be 5.03% is good evidence [35]. The low preterm birth rate in developed nations like Sweden may be due to high socio-demographic status of the population and improved preconception and ANC services, which are important in early identifying and preventing risk factors. The proportion of preterm birth in this study was found to be lower than in some studies conducted in low and middle-income countries.

The study conducted in Malawi shows that the prevalence of preterm birth was 16.3% [12]. This higher prevalence of preterm birth in Malawi may be due to the country’s higher HIV infection rate, where one in four women are HIV positive. In Brazil, the prevalence of preterm birth among young women attending public hospitals was 21.7% which was higher compared to this study [36]. The discrepancy may be due to variation in the study population. Only parturient mothers aged 15–24 were included in their study. A similar study conducted in Nigeria reported the prevalence of preterm birth of 16.9% [37], which was also higher than this study. This variation is maybe because of the difference in the study area where their study was at a referral hospital with referrals of more complicated cases from other general hospitals.

The current study’s finding was lower than those conducted in Kenya National Hospital and Jemma University Specialized Hospital, which reported the prevalence of preterm birth was 20.2% and 25.9%, respectively [23, 38]. This variation might be due to the difference in the study time, reflecting that FMOH has currently improved maternal health care service. Another possible reason for this variation might be due to differences in the study area, the study done in Kenya and Jimma indicates that the high prevalence of alcohol consumption and substance intake during pregnancy may be the contributing factors to the increased magnitudes of preterm birth.

Mothers with less than 3000 birr Average monthly family income were 1.95 times more likely to develop preterm than those with greater than 5000 birr family income. This study was in line with the study in SSA [26, 39],which shows Average monthly family income positively affected preterm. This might be due to financial insecurity, psychosocial stress, and low health care utilization. This study was not in line with the studies in Ethiopia, which show Average monthly family income was not associated with preterm mothers [18].

Mothers from three up to five family members were 0.15 times less likely to develop/give preterm as compared to mothers from greater than five family members. This study was in line with the study in Ethiopia [17], which shows family members had a significant positive effect on preterm birth.

The education status of mothers were significant factors of preterm. Mothers who had no formal education were 2.79 times more likely to develop/give preterm than mothers who had secondary and above education levels. This study was in line with the studies in Sub-Saharan African and European countries [39,40,41], which show education had been associated with preterm birth. This study was not in line with the studies in Ethiopia and Kenya [38, 42], which show education statutes had no significant effect on preterm.

Mothers who had husbands who had no formal education were 2.11 times more likely to develop/give preterm than mothers who had husbands with secondary and above education levels. This study is in line with the previous study in Iran [43], which shows a significant association between husbands’ education level and preterm birth.

Mothers with APH were 3.62 times more likely to have a preterm birth than mothers without APH. This finding is in line with the study conducted in Kenya, Ethiopia, and East Africa [38, 44, 45]. This suggests that obstetric problems caused by APH may significantly impact the occurrence of PTB. This could be due to decreased placental blood flow, impacting the mother-fetus exchange of nutrients and oxygen. As a result, fetal growth would be slowed, and the chance of PTB would be increased.

Mothers with multifetal gestation were 3.30 times more likely to have a preterm birth than mothers without multifetal gestation. This finding is in line with the study conducted in Ethiopia, Korea, Greek [46,47,48]. It might be since multiple pregnancies are more likely to be associated with a variety of problems, including preeclampsia, PROM, and polyhydramnios, all of which could contribute to iatrogenic PTB. Furthermore, this could be related to uterine overstretching and deciding to terminate the pregnancy before it reaches term.

Mothers who delivered with SVD were 7.16 times more likely to develop preterm as compared to mothers delivered with CS or Instrumental delivery. This finding is in line with the study conducted in Ethiopia [49], which shows delivered with SVD had a positive significant effect on preterm. This study is not in line with [50], which shows women delivering by previous cesarean section had a significantly higher risk of preterm birth when compared to women with vaginal delivery.

Mothers with spontaneous labor were 0.10 times less likely to have preterm birth as compared to mothers with induced labor. This finding is in line with the study conducted in Ethiopia [49], which shows spontaneous labor had a negative impact on preterm mothers. In addition, this study is in line with the study conducted in Ethiopia [51], which shows Labor status was associated with preterm birth.

History of chronic illness was significantly associated with the outcome variable, mothers who had a history of chronic illness were 3.94 times more likely to give preterm birth than mothers who had no chronic illness history. This finding is in line with a study conducted in Ethiopia [18, 44, 49], which shows chronic illness positively associated with preterm. This might be due to maternal illnesses that impede or impair the placental transfer of oxygen and nutrients to the developing fetus in the uterus can raise the chance of preterm birth.

This study revealed a significant association between pregnancy-induced hypertension and preterm birth. Mothers who had complications PIH were 3.61 times increased risk of having a preterm birth than those mothers without this problem during the index pregnancy. This finding is in line with the study conducted in East Africa, Ethiopia, Nigeria, Iran, Ghana, and Kenya [18, 38, 45, 46, 51,52,53,54,55], which shows PIH had significant effect on preterm birth. This might be due to the vascular damage of the placenta caused by PIH, which results in preterm labor and delivery.

Another significant association was found between mothers who had premature rupture of membranes (PROM) and preterm birth. Mothers with PROM were 6.46 times more likely to have a preterm birth than their counterparts. This finding was in line with the previous findings in Tehran, Iran, Ethiopia, East Africa, Nigeria [7, 26, 46, 53, 56], showing a significant association between PROM and preterm. This might be because in the absence of any clinical intervention labor will spontaneously initiate within hours after term PROM and within a week after preterm PROM in the majority of the cases.

In this study, the ANC follow up were significantly associated with the outcome variable preterm birth. Mothers with < 4 times ANC follow-up in the index pregnancy were 2.87 times more likely to have a preterm birth than mothers who had the ANC visits ≥ 4 times. This finding is in line with a study conducted in Ghana, Nigeria teaching hospitals, and western Ethiopia [20, 44, 52, 54, 57], which shows a significant association between ANC visits and preterm. WHO recommends at least four ANC visits, this will assure the quality of care and early detection of high-risk pregnancies which result in the prevention and proper management of obstetric complications. This may happen regularly. The benefits of an ANC visit include health promotion, early detection, and treatment of obstetric problems. However, this study is not in line with the study conducted in Ethiopia [18], which shows ANC follow-up had no significant effect on preterm.

Being HIV positive was significantly associated with preterm birth. HIV-positive mothers were 6.99 times more likely to give preterm compared to HIV-negative mothers. This finding is in line with a study in Malawi and Ethiopia [12, 18, 41, 58, 59], which shows a significant association between prevalence of HIV and preterm birth. This might be due to the drug effect and immunity of the mother as risk factors for preterm birth. This study was not in line with studies conducted in Botswana, and Malawi [60, 61]. This might be due to the drug’s impact and the mother’s immunity to premature birth.

In contradiction to the previous studies in our study, age, marital status, RH factor, Pregnancy status, and UTI [42, 62, 63]. This difference can be due to differences in the study area, design, period, population, and culture differences.

Limitation of the study

Despite efforts to reduce recollection biases by informing local events, there may be a recall bias. When ultrasound facilities are not accessible, the menstrual history and clinical examination are used to confirm gestational age and which may subject to considerable error. Another drawback of our study is using secondary data for some variables.

Conclusion

The proportion of preterm birth in public hospitals in the east Gojjam zone is 13.7%. number of ANC, educational status of mother, husband educational status, family income, family size, occupation, multifetal gestation, having PROM, history of chronic illness, being HIV positive, APH, PIH, mode of delivery, and onset of labor were found to be significantly associated with preterm birth. As a result, focusing on these important variables would reduce the number of premature births. Furthermore, it was suggested that educating the community about the importance of ANC service utilization of mothers and preventing preterm birth be strengthened. Moreover, strengthening early screening of HIV and high-risk pregnancies like multiple gestations, PIH and APH were important to prevent preterm.

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

ANC:

Antenatal Care

AOR:

Adjusted Odds Ratio

APH:

Ante-Partum Hemorrhage

CHTN:

Chronic hypertension

CI:

Confidence Interval

COR:

Crud Odds Ratio

CS:

Cesarean Section

DCSH:

Debre Markos comprehensive specialized hospital

DM:

Diabetes mellitus

EDHS:

Ethiopian Demographic and Health Survey

HCT:

Hematocrit

HGB:

Hemoglobin

HIV:

Human Immune Deficiency Virus

IESO:

Integrated Emergency Surgery and Obstetrics

LNMP:

last normal menstrual period

LMICS:

Lower and Middle-Income Countries

MDG:

Millennium Development Goal

NICU:

Neonatal Intensive Care Unit

PIH:

pregnancy Induced Hypertension

PROM:

Premature Rupture Of Membrane

PTB:

Preterm Birth

SD:

Standard Deviation

SPSS:

Statistical Package of Service solution

SVD:

Spontaneous Vaginal Delivery

UTI:

Urinary Tract Infection

WHO:

World Health Organization

References

  1. Organization WH. World health statistics 2013: a wealth of information on global public health. World Health Organization; 2013.

  2. Chawanpaiboon S, et al. Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis. The Lancet Global Health. 2019;7(1):e37–e46.

    Article  PubMed  Google Scholar 

  3. Dey AC, et al. Magnitude of problems of prematurity-national and global perspective: a review. Bangladesh J Child Health. 2012;36(3):146–52.

    Article  Google Scholar 

  4. Blencowe H, et al. Born too soon: the global epidemiology of 15 million preterm births. Reproductive health. 2013;10(1):1–14.

    Google Scholar 

  5. Organization WH. WHO recommendations on interventions to improve preterm birth outcomes 2015.

  6. Hug L, Sharrow D, You D. Levels and trends in child mortality: report 2017. The World Bank; 2017.

  7. Blencowe H, et al. National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications. The lancet. 2012;379(9832):2162–72.

    Article  Google Scholar 

  8. Brown HK, et al. 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.

    Article  PubMed  Google Scholar 

  9. Lawn JE, et al. 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(1):1–22.

    Article  Google Scholar 

  10. Onankpa B, Isezuo K. Pattern of preterm delivery and their outcome in a tertiary hospital. Int J Health Sci Res. 2014;4(3):59–65.

    Google Scholar 

  11. Wagura P. factors associated with preterm birth at kenyatta national Hospital BMC Pregnancy Childbirth, (18).

  12. 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):e90128.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Muchie KF, et al. Epidemiology of preterm birth in Ethiopia: systematic review and meta-analysis. BMC Pregnancy Childbirth. 2020;20(1):1–12.

    Article  Google Scholar 

  14. Zewudie AT, Gelagay AA, Enyew EF. Determinants of Under-Five Child Mortality in Ethiopia: Analysis Using Ethiopian Demographic Health Survey, 2016 International Journal of Pediatrics, 2020. 2020.

  15. Adugna DG. Prevalence and associated risk factors of preterm birth among neonates in referral hospitals of Amhara Region, Ethiopia. PLoS ONE. 2022;17(10):e0276793.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Waitzman NJ, Jalali A, Grosse SD. Preterm birth lifetime costs in the United States in 2016: an update. In seminars in Perinatology. Elsevier; 2021.

  17. Woldeyohannes D, et al. Factors associated with preterm birth among mothers who gave birth in Dodola town hospitals, Southeast Ethiopia: institutional based cross sectional study. Clin Mother Child Health. 2019;16(317):2.

    Google Scholar 

  18. Gebreslasie K. Preterm birth and associated factors among mothers who gave birth in Gondar town health institutions Advances in Nursing, 2016. 2016.

  19. Belaynew W, et al. Effects of inter pregnancy interval on preterm birth and associated factors among postpartum mothers who gave birth at Felege Hiwot referral hospital. World J Pharm Pharm Sci. 2015;4(4):12–25.

    Google Scholar 

  20. Woday A, Muluneh MD, Sherif S. Determinants of preterm birth among mothers who gave birth at public hospitals in the Amhara region, Ethiopia: a case-control study. PLoS ONE. 2019;14(11):e0225060.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Aynalem YA, et al. Determinants of neonatal mortality among preterm births in Black Lion Specialized Hospital, Addis Ababa, Ethiopia: a case–cohort study. BMJ open. 2022;12(2):e043509.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kelkay B et al. Factors associated with singleton preterm birth in Shire Suhul general hospital, northern Ethiopia, 2018 Journal of Pregnancy, 2019.

  23. Bekele I, Demeke T, 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 Women’s Health Care. 2017;6(1):1–10.

    Google Scholar 

  24. Hosny AE-DM, et al. Risk factors associated with preterm labor, with special emphasis on preterm premature rupture of membranes and severe preterm labor. J Chin Med Association. 2020;83(3):280–7.

    Article  CAS  Google Scholar 

  25. Vogel JP, Lee AC, Souza JP. Maternal morbidity and preterm birth in 22 low-and middle-income countries: a secondary analysis of the WHO Global Survey dataset. BMC Pregnancy Childbirth. 2014;14(1):1–14.

    Article  CAS  Google Scholar 

  26. Bekele T, Amanon A, Gebreslasie K. Preterm birth and associated factors among mothers who gave birth in Debremarkos-town health institutions 2013 institutional basedcrosssectional study. Gynecol Obstet (Sunnyvale). 2015;5(5):1000292.

    Google Scholar 

  27. Asemahagn MA, Alene GD, Yimer SA. Geographic accessibility, readiness, and barriers of health facilities to offer tuberculosis services in East Gojjam Zone, Ethiopia: a convergent parallel design. Res Rep Trop Med. 2020;11:3.

    PubMed  PubMed Central  Google Scholar 

  28. Agency CS. 2007 population and housing census of Ethiopia. Central Statistical Agency Addis Ababa, Ethiopia; 2007.

  29. Bizuayew H, et al. Post-cesarean section surgical site infection and associated factors in East Gojjam zone primary hospitals, Amhara region, North West Ethiopia, 2020. PLoS ONE. 2021;16(12):e0261951.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Aynalem BY, Melesse MF. Health extension service utilization and associated factors in East Gojjam zone, Northwest Ethiopia: a community-based cross-sectional study. PLoS ONE. 2021;16(8):e0256418.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Beck S et al. The worldwide incidence of preterm birth: a systematic review of maternal mortality and morbidity Bulletin of the world health organization, 2010. 88: p. 31–38.

  32. Blencowe H, Cousens S, Oestergaard D. Lawn data from national, regional and worldwide estimates of preterm birth rate. Glob Action Rep Preterm Birth. 2010;4:1–2.

    Google Scholar 

  33. Fritz T, et al. Outcome of extremely preterm infants after iatrogenic or spontaneous birth. Acta Obstet Gynecol Scand. 2018;97(11):1388–95.

    Article  PubMed  Google Scholar 

  34. Bakhteyar K, et al. Factors associated with preterm delivery in women admitted to hospitals in Khorramabad: a case control study. Int J Health Allied Sci. 2012;1(3):147.

    Article  Google Scholar 

  35. Cnattingius S, et al. Maternal obesity and risk of preterm delivery. JAMA. 2013;309(22):2362–70.

    Article  CAS  PubMed  Google Scholar 

  36. Miranda AE, et al. Prevalence and correlates of preterm labor among young parturient women attending public hospitals in Brazil. Revista Panam de Salud Pública. 2012;32:330–4.

    Article  Google Scholar 

  37. Iyoke CA, et al. Prevalence and perinatal mortality associated with preterm births in a tertiary medical center in South East Nigeria. Int J women’s health. 2014;6:881.

    Article  Google Scholar 

  38. Wagura P, et al. Prevalence and factors associated with preterm birth at kenyatta national hospital. BMC Pregnancy Childbirth. 2018;18(1):1–8.

    Article  Google Scholar 

  39. Alamneh TS, et al. Preterm birth and its associated factors among reproductive aged women in sub-saharan Africa: evidence from the recent demographic and health surveys of sub-sharan african countries. BMC Pregnancy Childbirth. 2021;21(1):1–11.

    Article  Google Scholar 

  40. Ruiz M, et al. Mother’s education and the risk of preterm and small for gestational age birth: a DRIVERS meta-analysis of 12 european cohorts. J Epidemiol Community Health. 2015;69(9):826–33.

    Article  PubMed  Google Scholar 

  41. Wakeyo D, Addisu Y, Mareg M. Determinants of Preterm Birth among Mothers Who Gave Birth in Dilla University Referral Hospital, Southern Ethiopia: A Case-Control Study BioMed Research International, 2020.

  42. Berhe T, Gebreyesus H, Desta H. Determinants of preterm birth among mothers delivered in Central Zone Hospitals, Tigray, Northern Ethiopia. BMC Res Notes. 2019;12(1):1–6.

    Article  Google Scholar 

  43. Khezri R, et al. Relationship between body mass index before pregnancy and weight gain during pregnancy with preterm birth. Stud Med Sci. 2016;26(10):890–9.

    Google Scholar 

  44. Zewde GT. Preterm birth and associated factors among mother who gave birth in public health hospitals in harar town eastern Ethiopia 2019. OSP J Health Care Med. 2020;1(1):1–3.

    Google Scholar 

  45. Laelago T, Yohannes T, Tsige G. Determinants of preterm birth among mothers who gave birth in East Africa: systematic review and meta-analysis. Ital J Pediatr. 2020;46(1):1–14.

    Article  Google Scholar 

  46. Mulualem G, Wondim A, Woretaw A. The effect of pregnancy induced hypertension and multiple pregnancies on preterm birth in Ethiopia: a systematic review and meta-analysis. BMC Res Notes. 2019;12(1):1–7.

    Article  Google Scholar 

  47. Lee KJ, et al. The clinical usefulness of predictive models for preterm birth with potential benefits: a KOrean Preterm collaboratE Network (KOPEN) registry-linked data-based cohort study. Int J Med Sci. 2020;17(1):1.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Bouras G, et al. Preterm birth and maternal psychological health. J Health Psychol. 2015;20(11):1388–96.

    Article  PubMed  Google Scholar 

  49. Aregawi G et al. Preterm births and associated factors among mothers who gave birth in Axum and Adwa Town public hospitals, Northern Ethiopia, 2018. BMC research notes, 2019. 12(1): p. 1–6.

  50. Zhang Y, et al. Mode of delivery and preterm birth in subsequent births: a systematic review and meta-analysis. PLoS ONE. 2019;14(3):e0213784.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Abaraya M, Seid SS, Ibro SA. Determinants of preterm birth at Jimma university medical center, Southwest Ethiopia Pediatric health, medicine and therapeutics, 2018. 9: p. 101.

  52. Iyoke C, et al. 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.

    Article  CAS  PubMed  Google Scholar 

  53. Tehranian N, Ranjbar M, Shobeiri F. The prevalence and risk factors for preterm delivery in Tehran, Iran. J Midwifery Reproductive Health. 2016;4(2):600–4.

    Google Scholar 

  54. Adu-Bonsaffoh K, et al. Determinants and outcomes of preterm births at a tertiary hospital in Ghana. Placenta. 2019;79:62–7.

    Article  CAS  PubMed  Google Scholar 

  55. Gejo NG, et al. Factors associated with preterm birth at Wachemo University Nigist Eleni Mohammed memorial hospital, southern Ethiopia: case-control study. BMC Pregnancy Childbirth. 2021;21(1):1–9.

    Article  Google Scholar 

  56. Iyoke CA et al. Prevalence and perinatal mortality associated with preterm births in a tertiary medical center in South East Nigeria.International Journal of Women’s Health, 2014: p.881–888.

  57. Abadiga M, et al. Determinants of preterm birth among women delivered in public hospitals of western Ethiopia, 2020: unmatched case-control study. PLoS ONE. 2021;16(1):e0245825.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Deressa AT, et al. Factors associated with spontaneous preterm birth in Addis Ababa public hospitals, Ethiopia: cross sectional study. BMC Pregnancy Childbirth. 2018;18(1):1–5.

    Article  Google Scholar 

  59. Mekonen DG, et al. Proportion of Preterm birth and associated factors among mothers who gave birth in Debretabor town health institutions, northwest, Ethiopia. BMC Res Notes. 2019;12(1):1–6.

    Article  Google Scholar 

  60. Kourtis AP, Fowler MG. Antiretroviral use during pregnancy and risk of preterm delivery: more questions than answers. Oxford University Press; 2011. pp. 493–4.

  61. van den Akker T, et al. HIV care need not hamper maternity care: a descriptive analysis of integration of services in rural Malawi. BJOG: An International Journal of Obstetrics & Gynaecology. 2012;119(4):431–8.

    Article  Google Scholar 

  62. El-Sayed AM, Tracy M, Galea S. Life course variation in the relation between maternal marital status and preterm birth. Ann Epidemiol. 2012;22(3):168–74.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Shah R, et al. Incidence and risk factors of preterm birth in a rural bangladeshi cohort. BMC Pediatr. 2014;14(1):1–11.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to acknowledge Bahir Dar University College of Medicine and Health Science for permitting us to conduct this research and public hospitals of east Gojjam zone staff members for providing important information.

Funding

The author(s) received no specific funding for this work.

Author information

Authors and Affiliations

Authors

Contributions

TB: conceived and designed the study, supervised the data collection, performed the analysis, and interpretation of data, and drafted the manuscript. YA: were involved in assisting with the study design, statistical analysis, and interpretation and reviewed the manuscript critically. We confirm that all methods were carried out in accordance with relevant guidelines and regulations. All authors critically reviewed the manuscript and approved the final version.

Corresponding author

Correspondence to Yikeber Abebaw Moyehodie.

Ethics declarations

Consent for publication

Not applicable.

Ethics approval and consent to participate

Ethical clearance was obtained from the ethical review committee of the College of Medicine and Health Science at Bahir Dar University (with reference number IRB/204/2021). Also, same ethics committee waived the need of informed consent. Letter of correspondence from Bahir Dar University, College of Medicine and Health Science was written to public hospitals in the east Gojjam zone. Following these, the hospital record office gave us permission to collect required data. However, informed consent from study participants was not required because the nature of the study was extracting & analyzing the existing data, which posed minimal risks to the study subjects. Nonetheless, data collectors maintained confidentiality through excluding names or any other personal identifiers from data collection sheets and reports. All methods were carried out in accordance with the Declaration of Helsinki.

Competing Interests

The authors declared that no competing interests exist.

Additional information

Publisher’s Note

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ayele, T.B., Moyehodie, Y.A. Prevalence of preterm birth and associated factors among mothers who gave birth in public hospitals of east Gojjam zone, Ethiopia. BMC Pregnancy Childbirth 23, 204 (2023). https://doi.org/10.1186/s12884-023-05517-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12884-023-05517-5

Keywords