Preterm birth refers to the percentage of babies born before 37 completed weeks of gestation. In addition, more granularity would be helpful for programmes, such as dividing moderately preterm (33–36 completed weeks of gestation), very preterm (<32 weeks), and extremely preterm (<28 weeks)(Lawn et al, 2015). Globally, it is the leading cause of perinatal and neonatal mortality and morbidity (Cnattingius et al, 2016). Preterm infants are particularly vulnerable to complications due to impaired respiration, difficulty in feeding, poor body temperature regulation and a high risk of infection (Greene et al, 2006; Offiah et al, 2012; Jakiel et al, 2015).
Preterm birth is a global problem affecting 5%–18% of births across 184 countries, according to the World Health Organization ([WHO], 2012). The highest prevalence is in Sub-Saharan Africa and Asia, which accounts for half of the world's births, more than 60% of the world's preterm babies and over 80% of neonatal deaths annually due to complications related to preterm births. Although most countries, especially in low- and middle-income countries, lack reliable data on preterm births, nearly all of those with reliable trends data show an increase in preterm birth rates over the past 20 years. Indeed, all but 3 out of 65 countries in the world with a reliable trend show an increase in preterm birth rates in the last 20 years. Significant progress has been made in the care of premature infants but not in reducing the prevalence of preterm birth, which is generally on the rise (Assembly, 2000; Goldenberg et al, 2008; Lawn et al, 2009; 2012; Blencowe et al, 2012; WHO, 2012).
In Ethiopia, 320 000 preterm babies are born each year and directly contribute to 12.5% of under five deaths (Adane et al, 2014). Preterm birth is the major single burden for both developing and developed countries. Some of the anticipated complications following the birth of preterm neonates are motor development, behaviour, academic performance impairment and even death within minutes (Gebreslasie, 2016).
Identifying and understanding the risk factors for preterm birth has the potential to address these problems. Ethiopia, like other low-income countries, lacks reliable data on the burden of preterm birth. Therefore, determining the factors associated with preterm birth has a great role in guiding health professionals and policymakers in designing interventional strategies, and applying necessary preventive and appropriate measures to decrease preterm birth. Therefore, this study aimed to determine the prevalence and associated factors of preterm birth in Dire Dawa City, Eastern Ethiopia.
Methods and materials
Study area and period
The study was conducted in Dire Dawa City from 1 October 2019 to 30 November 2019. The City is located 515 km away from Addis Ababa, the capital city of Ethiopia. According to 2019 population projections, 313 000 people (63%) live in the city and 180 000 people (37%) live in rural areas, with 49 % and 51% being males and females, respectively. The Dire Dawa has nine kebeles (the smallest administrative units in Ethiopia). There are two hospitals, eight health centres, 12 private clinics and 31 health posts.
Study design and population
An institutional-based, cross-sectional study was conducted among mothers who gave birth in Dire Dawa City hospitals during the study period. Mothers who did not remember their last normal menstrual period (LNMP) had no early ultrasound results as well as those who were severely ill and needed emergency care were excluded from the study.
Sample size and procedure
The sample size was determined using a single population proportion formula by considering the proportion of preterm births (26 %) in Jimma hospital (Tema, 2006), confidence level (95%), the margin of error (4.4%) and by adding 10% non-response rate, the final sample size was 420. According to the hospitals' delivery report, about 1 300 mothers gave birth in two months. The subjects of the study were selected using a systematic sampling technique and the sampling interval (k) was three. The first mother was selected using the lottery method and then every three were interviewed.
Data collection tools
Data were collected by a face-to-face interviewer and chart review using a structured and pre-tested questionnaire prepared from different kinds of literature (Carmo et al, 2016; Tehranian et al, 2016; Deressa et al, 2018; Tsegaye and Kassa, 2018; Kelkay et al, 2019; Mekonen et al, 2019). First, the questionnaire was prepared in English, translated into local languages, and then translated back into English to keep its integrity. Gestational age was determined by the LNMP and early ultrasound result, which means ultrasound done in the first trimester since an ultrasound in the first trimester is more reliable as +1 week, but mothers who did not remember the last LNMP and had no early ultrasound were excluded. Maternal height was measured against a stand metre at the height scale and recorded to the nearest centimetre and weight by abeam balance to the nearest two digit decimal place. Maternal data were collected within six hours of delivery.
Operational definition
Preterm birth is defined by WHO as all viable births before 37 completed weeks of gestation or less than 259 days since the first day of a woman's LNMP (Ramokolo et al, 2014).
Data quality control
To ensure the quality of the data, three days of intensive training was provided to all data collectors and supervisors. A pre-test was conducted on 5 % (22) of the sample size in the Art General Hospital which was not included in the actual study. The modification of the questionnaire was carried out accordingly. The data collection process was done with continuous follow-up and supervision by the supervisors and investigators. Finally, to ensure the quality of the data, two independent data clerks did double data entry.
Data processing and analysis
The data were entered, cleaned and edited using Epi-Data version 3.1, then exported to SPSS version 20.0 software packages for analyses. Descriptive statistics were used to describe the frequency distribution and proportions for categorical variables. Both bivariate and multivariate logistic analyses were done to see the association between independent and outcome variables. Model fitness and multi-collinearity check were done. Variables with a p-value <0.25 in bivariate analysis were candidates for multivariable analysis. Adjusted odds ratio (AOR) with a 95% confidence interval at a p-value <0.05 was considered significant.
Results
Sociodemographic characteristics of mothers
A total of 412 mothers were included in this study, with a response rate of 98.1%. The mean (±SD) age of the mothers was 27.46 (+5.0) years. A total of 246 (59.7%) mothers were in the age group of 25–34 years. The majority, 368 (89.3%), of mothers were married. The religion of more than half of the women were Muslim (57.3%) and more than half of the women were Oromo in ethnicity (53.9%). A total of 301 (73.1%) women were urban residents (Table 1).
Table 1. Sociodemographic characteristics of mothers who gave birth at Dire Dawa hospitals, Eastern Ethiopia in 2019 (n=412)
| Variables | Frequency | % |
|---|---|---|
| Age | ||
| 18-24 | 122 | 29.6 |
| 25-34 | 246 | 59.7 |
| 35 and above | 44 | 10.7 |
| Marital status | ||
| Married | 368 | 89.3 |
| Unmarred | 44 | 10.7 |
| Religion | ||
| Muslim | 236 | 57.3 |
| Orthodox | 118 | 28.6 |
| Protestant | 53 | 12.9 |
| Catholic | 5 | 1.2 |
| Ethnicity | ||
| Oromo | 222 | 53.9 |
| Amhara | 88 | 21.4 |
| Somali | 90 | 21.8 |
| Others* | 12 | 2.9 |
| Maternal educational status | ||
| Unable to read and write | 77 | 18.7 |
| Able to read and write | 82 | 19.9 |
| Attend primary school | 119 | 28.9 |
| Attended secondary school | 48 | 11.7 |
| College and above | 86 | 20.9 |
| Maternal occupational status | ||
| Housewife | 164 | 39.8 |
| Merchant | 128 | 31.1 |
| Government employee | 71 | 17.2 |
| Non-governmental employee | 49 | 11.9 |
| Residence | ||
| Urban | 301 | 73.1 |
| Rural | 111 | 26.9 |
| Family size | ||
| <5 | 344 | 83.5 |
| >5 | 68 | 16.5 |
Maternal obstetric characteristics
A total of 283 (68.7%) of the mothers were multipara; 189 (45.9%) had used modern contraceptives before the current pregnancy, and three-fourths (75.7%) of them had planned pregnancy. A total of 290 mothers (70.4%) had ANC follow-ups, of which 54.1% had started ANC follow-ups before four months. A majority of 384 (93.2%) of the mother's HIV status was negative. Concerning the mother's haemoglobin level, 164 (39.8 %) had <11 g/dl (Table 2).
Table 2. Obstetric characteristics of mother's who gave birth in Dire Dawa hospitals, Eastern Ethiopia in 2019 (n=412)
| Variables | Frequency | % |
|---|---|---|
| Parity (n=412) | ||
| Primipara | 129 | 31.3 |
| Multipara | 283 | 68.7 |
| Ever had abortion (n=283) | ||
| Yes | 75 | 26.5 |
| No | 208 | 73.5 |
| Ever had a stillbirth (n=283) | ||
| Yes | 65 | 23 |
| No | 218 | 77 |
| History of contraceptive utilisation | ||
| Yes | 189 | 45.9 |
| No | 223 | 54.1 |
| ANC follow up (412) | ||
| Yes | 290 | 70.4 |
| No | 122 | 29.6 |
| Time of the first ANC visit (290) | ||
| <4 months | 133 | 45.9 |
| ≥4 months | 157 | 54.1 |
| Number of ANC visit (290) | ||
| <4 | 131 | 45.2 |
| ≥4 | 159 | 54.8 |
| Pregnancy type | ||
| Planned | 312 | 75.7 |
| Unplanned | 100 | 24.3 |
| Maternal HIV status (412) | ||
| Positive | 28 | 6.8 |
| Negative | 384 | 93.2 |
| Counselling on diet at the time of ANC | ||
| Yes | 261 | 63.3 |
| No | 151 | 36.7 |
| Taken an additional diet | ||
| Yes | 282 | 68.4 |
| No | 130 | 31.6 |
| Haemoglobin level | ||
| <11 gm/dl | 164 | 39.8 |
| ≥11 gm/dl | 248 | 60.2 |
| Sex of the neonate | ||
| Male | 188 | 45.6 |
| Female | 224 | 54.4 |
| Fetal presentation | ||
| Cephalic | 315 | 76.5 |
| Breach | 97 | 23.5 |
| History of pregnancy complications | ||
| Yes | 161 | 39.1 |
| No | 251 | 60.9 |
| Types of pregnancy complications | ||
| Pre-eclampsia | 49 | 11.9 |
| Diabetes miletus | 26 | 6.3 |
| Antepartum haemorrhage | 46 | 11.2 |
| Premature rupture of membrane | 65 | 15.8 |
ANC: antenatal care
Anthropometric and behavioural characteristics
The majority (93%) of the mothers' weight was 50 kg and above before pregnancy and 391 (94.9%) of the mothers' height of 150 cm and above. More than half (72.1%) of the mothers' mid upper arm circumference (MUAC) was above 24 cm. A total of 35 (8.5%) and 85(20.6%) of the mothers had a history of smoking and alcohol drinking during the current pregnancy, respectively (Table 3).
Table 3. Anthropometric and behavioural characteristics of mothers who gave birth in Dire Dawa hospitals, Eastern Ethiopia in 2019 (n=412)
| Variables | Frequency | % |
|---|---|---|
| Weight of the mother before pregnancy | ||
| <50 kg | 16 | 7.0 |
| >=50 kg | 213 | 93.0 |
| Height of the mother | ||
| <150 cm | 21 | 5.1 |
| >=150 cm | 391 | 94.9 |
| MUAC | ||
| <24 cm | 177 | 43.0 |
| >24 cm | 235 | 57.0 |
| Smoked during the current pregnancy | ||
| Yes | 35 | 8.5 |
| No | 377 | 91.5 |
| History of a passive smoker | ||
| Yes | 85 | 20.6 |
| No | 327 | 79.4 |
| History of alcohol drinking | ||
| Yes | 19 | 4.6 |
| No | 393 | 95.4 |
*MUAC: mid upper arm circumference
Prevalence of preterm birth
The overall prevalence of preterm birth in this study was 9% (95% CI:6.1%, 12.1%) and the mean gestational age at birth was 38.77+1.56 weeks (Figure 1).
Figure 1. Prevalence of preterm births among babies delivered at Dire Dawa hospitals, Eastern Ethiopia in 2019
Factors associated with preterm birth
In multivariable logistic regression analyses, mothers who smoked during the current pregnancy, history of pregnancy complications, five and above family size, MUAC less than 24 cm and height less than 150 cm were found to be a statistically significant association with preterm births. Mothers who smoked during the current pregnancy were four times [AOR=4.3, 95% CI:1.29, 14.45] more likely to give preterm birth compared to those mothers who did not smoke during pregnancy. The occurrence of complications during the current pregnancy was nearly 23 times [AOR=22.70, 95% CI:6.10, 84.10] more likely to give preterm birth compared to their counterparts. Similarly, mothers with MUAC less than 24 cm and height less than 150 cm [AOR=2.40, 95% CI:1.01, 5.72], [AOR=8.70, 95% CI: 1.90, 40.03] were more likely to give preterm birth than their counterparts, respectively. Moreover, those mothers who had a family size of five or more were three times [AOR=2.70, 95% CI:1.12, 6.40] more likely to deliver preterm babies (Table 4).
Table 4. Factors associated with preterm birth among mothers who gave birth at Dire Dawa hospitals, eastern Ethiopia in 2019 (n=412)
| Variables | Preterm birth | COR (95%CI) | AOR (95%CI) | p-value | |
|---|---|---|---|---|---|
| Yes | No | ||||
| Residence | |||||
| Rural | 20 (54.1) | 91 (24.3) | 3.7 (1.85, 7.31) | 1.1 (0.41, 2.85) | 0.87 |
| Urban | 17 (45.9) | 284 (75.7) | 1 | 1 | |
| ANC follow up | |||||
| No | 16 (43.2) | 106 (28.3) | 1.9 (0.97, 3.85) | 1.9 (0.57, 5.58) | 0.29 |
| Yes | 21 (56.8) | 269 (71.7) | 1 | 1 | |
| History of pregnancy complications | |||||
| Yes | 34 (20.7) | 127 (33.9) | 22.1 (6.67, 73.45) | 22.7 (6.10, 84.10)* | 0.001 |
| No | 3 (1.2) | 248 (66.1) | 1 | 1 | |
| Haemoglobin level | |||||
| <11 gm/dl | 21 (56.8) | 143 (38.1) | 2.1 (1.08, 4.22) | 1.1 (0.44, 2.58) | 0.892 |
| ≥11 gm/dl | 16 (43.2) | 232 (61.9) | 1 | 1 | |
| MUAC | |||||
| <24 cm | 24 (64.9) | 153 (40.8) | 2.7 (1.32,5.43) | 2.4 (1.01, 5.72)* | 0.049 |
| ≥24 cm | 13 (35.1) | 222 (59.2) | 1 | 1 | |
| Height | |||||
| <150 cm | 5 (13.5) | 16 (4.3) | 3.5 (1.21, 10.19) | 8.7 (1.90, 40.03) | 0.005 |
| ≥150 cm | 32 (86.5) | 359 (95.7) | 1 | 1 | |
| Smoked during the current pregnancy | |||||
| Yes | 7 (18.9) | 28 (7.5) | 2.9 (1.17, 7.1) | 4.3 (1.29,14.45)* | 0.017 |
| No | 30 (81.1) | 347 (92.5) | 1 | 1 | |
| Pregnancy planned | |||||
| No | 17 (45.9) | 83 (22.1) | 3.0 (1.50,5.97) | 1.9 (0.57,6.52) | 0.290 |
| Yes | 20 (54.1) | 292 (77.9) | 1 | 1 | |
| Family size | |||||
| ≥5 | 20 (54.1) | 104 (27.7) | 3.0 (1.55, 6.08) | 2.7 (1.12,6.40)* | 0.027 |
| <5 | 20 (54.1) | 104 (27.7) | 1 | 1 | |
| Contraceptive utilisation | |||||
| No | 27 (73.0) | 196 (52.3) | 2.5 (1.16, 524) | 1.2 (0.42, 3.35) | 0.741 |
| Yes | 10 (27.0) | 179 (47.7) | 1 | ||
Discussion
In this study, the overall prevalence of preterm birth was 9% (95% CI:6.1%, 12.1%). We identified mothers who smoked during the current pregnancy, history of pregnancy complications, and five and above family size, MUAC less than 24 cm, and height less than 150 cm as factors associated with preterm births. The prevalence of preterm birth in this finding was higher than the study conducted in Gondar Town, where the prevalence of preterm birth was (4.4%) (Gebreslasie, 2016). This might be due to the difference in the study setting, where the current study included only hospitals with a higher rate of referral cases. In addition to this, it might be due to differences in sociocultural values. It was also higher than in studies conducted in Iran (1.5–5.5%)(Roozbeh et al, 2016; Tehranian et al, 2016) and the US (3.8 %) (Wong et al, 2016). This difference might be due to socioeconomic disparity and the level of care given to pregnant women. Besides, it might be due to pregnant women in developed countries having better risk factor screening and early prevention, as well as better access to nutrition.
However, the rates were lower in studies conducted in other parts of Ethiopia, such as Jimma hospital (25.9%)(Tema, 2006), Debre Tabor Town (12.8%) (Mekonen et al, 2019), Tigray region (13.3%–16.9%) (Teklay et al, 2018; Aregawi et al, 2019), Dodola (13%) (Woldeyohannes et al, 2015), and Gondar (14.3%) (Adane et al, 2014). This might be due to the difference in study areas and the time of the study; currently, in the study area, it might be better health-seeking behaviour among the study participants. It was also lower than a study conducted in Kenya, where the proportion of preterm births was 18.3% (Wagura, 2018). This difference might be due to the disparity of the study time that the improvement of the health service's delivery system and the quality of care over time. Besides, this might be due to the difference in study methodology, since gestational age was calculated using only an ultrasound report in Kenya, while in this study, LNMP and first-trimester ultrasound reports were used.
This finding was in line with the study conducted in Butajira Hospital (6.4%) (Abdo, 2019). This might be due to the similarity in study settings and time since both studies were conducted in hospitals in the same year.
Mothers who smoked during the current pregnancy were at a higher risk to give preterm birth. This finding was supported by a study conducted in Jimma, Tigray and Kenya(Bekele, 2018; Wagura, 2018; Kelkay et al, 2019). This might be due to cigarette smoking which might interfere with the nutritional process and negatively affect the strength of the membrane, which leads to premature rupture of the membrane (Harger et al, 1990). Mothers with a history of pregnancy complications were more likely to have a preterm birth. This finding is similar to studies conducted in Debre Tabor, Butajira, Wollo and Tawin (Abdo, 2019; Kassahun et al, 2019; Mekonen et al, 2019). This might be due to complications during pregnancy that leads to preterm delivery by induction of labour or caesarean section and spontaneous delivery before the onset of labour.
Mothers who had lived with a family size >5 were more likely to give preterm birth. This finding is similar to that of a study conducted in Dodola (Woldeyohannes et al, 2015). A possible reason could be that pregnant mothers of larger family sizes had insufficient healthcare services. It could also because a larger family size may mean a shared monthly income which could be reserved for maternal care and support.
Mothers with MUAC<24 cm were at a higher risk of giving preterm birth compared to their counterparts. This finding is comparable with studies conducted in Debre Tabor Town, Wollo in Ethiopia and Bangladeshi (Shah et al, 2014; Abdo, 2019; Kassahun et al, 2019; Mekonen et al, 2019). This might be because maternal nutritional status had a direct effect on the placental size, fetus and strength of the membrane, resulting in preterm delivery. Maternal height less than 150 cm were more likely to deliver preterm babies compared to their counterparts. This finding is consistent with a study conducted in Sweden (Derraik et al, 2016). This evidenced by women with short stature would have a shorter cervical length and a smaller maternal pelvis resulted in shorter average gestational length (Van et al, 2018).
Since the study was cross-sectional, it did not establish a possible temporal relationship. It used self-reporting (interview response) which might have a social desirability bias. Some questions also required participants to recall, which may have resulted in recall bias and also excluded those mothers who did not remember their LNMP or/and have no early ultrasound report affect the result of this study.
Conclusion
The prevalence of preterm birth was 9%. Mothers with a history of smoking had pregnancy complications, family size five and above, and MUAC less than 24 cm were predictors of preterm birth. Therefore, providing effective family planning, prevention of pregnancy complications, avoiding smoking during pregnancy and implementing proven strategies to prevent preterm birth is very important to improve child survival and decrease mortality and morbidity.
Key points
- Preterm birth is a global public health problem
- Healthcare providers should implement proven strategies to prevent preterm birth
- Further research is recommended with advanced methodological design, such as randomised controlled trials or a cohort study
CPD reflective questions
- What are the long-term complications of preterm birth?
- Do you think that seasonal variations cause preterm birth?
- What are the key strategies to decrease preterm birth and its complications?