Investigating Factors Related to the Occurrence of Premature Infants in the South of Iran: A Population-based Study

Reza Faryabi1, Mehran Nikvarz2, Mahdieh Ardaneh3, Rasoul Raesi4, Salman Daneshi1, *, Vahid Mashayekhi Mazar5
1 Department of Public Health, School of Health, Jiroft University of Medical Sciences, Jiroft, Iran
2 Department of Pediatrics, School of Medicine, Jiroft University of Medical Sciences, Jiroft, Iran
3 Department of Epidemiology, School of Health, Tehran University of Medical Sciences, Tehran, Iran
4 Department of Health Services Management, Mashhad University of Medical Sciences, Mashhad, Iran
5 Deputy of Health, Jiroft University of Medical University, Jiroft, Iran

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© 2023 Faryabi et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Public Health, School of Health, Jiroft University of Medical Sciences, Jiroft, Iran;
Tel: 03443318337; E-mail:



Prematurity has been recognized worldwide as the leading cause of death in infants under 4 weeks of age for at least a decade. This study aimed to investigate the factors related to the occurrence of premature infants in the south of Iran in 2020.


In this cross-sectional study, the reporting of premature birth infants was done from the national system of the Ministry of Health (Iman). In the next step, according to the checklist made by the researcher, the information was extracted from the SIB system and completed by a survey of the mothers. Premature infants whose prematurity disorder occurred from March 21, 2019, to March 19, 2020, formed the study cohort. Analyses of the data used SPSS v 20 software, and the statistical significance level was set at <0.05.


In this study, 554 premature infants were examined, and about 55% of them were boys. The residence of the parent’s premature infants was the village (58.5%). The more common factors seen with PTBs were an unwanted pregnancy, hemoglobin less than 11, Body Mass Index (BMI) more than 30, pre-eclampsia, gestational diabetes, opium use in family members, history of cesarean section in previous pregnancies, low family monthly income, low education, and nonprofessional occupations of the mother and father.


Spontaneous premature infants were common in socially vulnerable groups, such as rural residents or people with low education and a poor economic situation. It is emphasized that the reduction of social and health inequalities will likely reduce premature birth rates.

Keywords: Risk factors, Premature, Infants, Population, Health, Prematurity disorder.


The global burden of preterm birth (PTB) includes the morbidity and mortality of infants born before 37 weeks of gestation [1]. Prematurity has been the leading cause of death in infants worldwide for at least a decade, but it has now also become the leading cause of death in children under five years of age. Worldwide, 15 million premature infants are born each year, which is estimated to account for about 11% of all births. Preterm birth appears to be increasing in most countries [1, 2]. Premature infants (28 weeks pregnant) account for only about 0.7 percent of births in the United States and an even smaller proportion in other developed and wealthy countries. However, these births comprise a disproportionate number of children with cerebral palsy, intellectual disability, autism spectrum disorder, attention deficit hyperactivity disorder, and epilepsy. Among people born prematurely, there is an increased risk of more neurodevelopmental disorders. Early life factors that contribute to this risk include perinatal brain injury, broncho-pulmonary dysplasia, and neonatal systemic inflammation. It seems that prenatal factors, especially the socio-economic status of the family, also play a role in creating risk. For most adverse outcomes, the risk is greater in male births [2]. A prior study noted that black women (who may have greater socioeconomic disparities than white women in the US) have a higher PTB rate (14% compared to 9%) [3].

Psychosocial factors, such as depression and other social determinants of health (SDH) which are associated with shorter gestational age (e.g., not receiving health services), can increase the risk of PTB. A large number of premature children have short-term and long-term cognitive, social-emotional, mental health, behavioral and supervisory problems up to school age and beyond [4]. In general, several multifaceted factors that can be classified as biological-medical risks, psychosocial risks, and SDH can be considered the causes of prematurity [2, 5]. Biomedical risks of PTB include high blood pressure, being underweight and having other medical disorders, multiple pregnancies, inflammation and infections, uterine abnormalities, and a variety of genetic disorders. In addition to psycho-social risk factors, such as depression and anxiety, stress, traumatic events, lack of social support and some individual/household factors, such as low levels of education, housing insecurity, and transportation limitations, are associated with PTB [5, 6]. Premature infants can also be a major source of stress in a parent's life. After birth, parents (of a PTB) may find themselves in an unfamiliar and highly technological environment that can be overwhelming as they adjust to their new role as parents of a preterm infant. Uncertainty related to infant health risks and complex parenting needs due to the prematurity of the baby leaves the parents under significant psychological pressure [4]. Therefore, the prevention of prematurity of infants is considered a public health priority to reduce the mortality rate of infants and children related to this disease, but unfortunately, relatively little progress has been made in preventing PTBs. One of the biggest challenges in studying this disease is that PTB is a complex condition that results from multiple etiological pathways [7].

PTB is a complex trait determined by multiple environmental and genetic factors. There is a critical need to move beyond traditional approaches to dealing with PTB [3]. Despite the reported association between PTB and a wide range of sociodemographic, medical, obstetric, fetal, and environmental factors, approximately two-thirds of PTBs occur without an obvious risk factor [8]. Therefore, more research is needed to understand the multiple factors associated with prematurity and to identify effective interventions to prevent this problem [6, 8].

To add to the body of literature identifying factors associated with PTB internationally and focusing on the lower socioeconomic Kerman province versus other parts of Iran, the researchers investigated PTB cases in the rural south of Iran.


In this cross-sectional study, first, the national code of mothers of premature infants was extracted by reporting from the national system of the Ministry of Health (Iman) (the Iman system is one of the most comprehensive systems of the Ministry of Health, which registers their information at the same time as the baby is born in the hospital), in the next step, we identified 554 premature infants by entering the national code of mothers (only Iranian mothers) in the SIB system, additional information was collected from the SIB system and a follow-up survey of mothers using a checklist made by the research team which was derived from various scientific sources and review articles and approval by PTB content experts. Some information was obtained from the electronic file of maternal care in the SIB system. Premature infants whose prematurity disorder occurred from April 1, 2019, to the end of March 2020 were recorded in the SIB system and followed up as above. To complete the predetermined data checklist, demographic information (such as baby's sex, mother's age, mother's education, father's education, etc., information related to the mother’s pregnancy history (such as the history of cesarean section in previous pregnancies, history of premature infants, history of stillbirth, history of abortion, a history of using addictive substances) and information related to recent pregnancies (such as the number of the birth, pregnancy order, polyhydramnios, pre-eclampsia, Infectious disease, concurrent infectious disease, use of birth control methods, placenta previa and gestational diabetes) were used.

During the phone call with the mother, the survey included information, such as the history of using family planning techniques, domestic violence, employment, and educational information regarding the baby's father, the family's monthly income and the source of drinking water that was not available in the Iman system data. The data collection tool also included the demographic characteristics of the baby, the baby's mother and father, the place of residence, and the risk factors related to the mother and the baby that could possibly be related to the occurrence of prematurity.

Data analysis was done using SPSS version 20 software. Descriptive statistics (number, percentage, mean and standard deviation) and subsequent analysis of demographic variables were used in a one-way analysis of variance, chi-square, t-test, and Spearman's correlation. The statistical significance level was set at a p-value <0.05.


In this study, 554 premature infants were examined, and about 55% of them were male. About 80% of the mothers of premature infants were between 18 and 35 years old. In terms of education, most mothers and fathers had primary education (13.7% and 20%, respectively), most of the mothers were housewives (87.9%), and most of the fathers were farmers/ranchers (37%). The residence of the parents of most premature infants was the village (58.5%) (Table 1).

The most common factor in PTBs was the history of cesarean section in previous pregnancies (49.8 percent), followed by the history of miscarriage (21.3 percent), the history of premature infants (9.4 percent), and the history of stillbirth (3.8 percent) and a history of using addictive substances (1.6 percent) (Table 2).

Table 1. Status of demographic variables of study participants.
Variable Number (Percent) Variable Number (Percent)
Gender of the baby Girl 247 (44.6) Mother's job Employee 32(5.8)
Boy 307 (55.4) Housewife 487 (87.9)
Mother's age in the recent pregnancy Less than 18 years 12(2.2) Farmer/rancher 9(6.1)
18 to 35 437 (78.9) Other 26(4.7)
More than 35 105(19) Father's job Employee 65 (11.7)
Mother's education Illiterate 34(6.1) Farmer/rancher 205(37)
Diploma and less 397 (71.7) Other 271 (48.9)
University 123(22.2) Unemployed 13(2.3)
Father's education Illiterate 47(8.5) Monthly family income Less than 5 million 102 (18.4)
Diploma and less 402 (72.6) 5 to 10 million 219 (39.5)
University 105(19) More than 10 million 233(42.1)
Address City 230 (41.5) Type of residential house Personal 422 (76.2)
Rental 132 (23.8)
Village 324 (58.5) Drinking water source Piping network 512 (92.4)
Other 42(7.6)
Table 2. Frequency of historical factors in the PTB cohort.
Variable Number (Percentage)
Cesarean sections in previous pregnancies 276 (49.8)
Prior premature infants 52 (9.4)
Prior stillbirth 21 (3.8)
Prior abortion/miscarriage 118 (21.3)
Use of addictive substances by the mother 9 (1.6)
Table 3. Frequency of common pregnancy-related health factors and premature births in the context of recent pregnancy.
Variable Number (Percent) Variable Number (Percent)
Number of
the births
Single 465 (83.9) Distance to previous pregnancy No history of pregnancy 141 (25.5)
2≤ 89 (16.1) Less than a year 22 (4)
Pregnancy order First 148 (26.7) One to two years 72 (13)
2 – 5 386 (69.7) More than two years 319 (57.6)
6 ≤ 20 (3.6) Choice in pregnancy with authority 456 (82.3)
Polyhydramnios Yes 23(4.2) Unwanted 98 (17.7)
No 531 (95.8) Use of addictive substances
by the mother
Yes 6 (1.1)
Pre-eclampsia Yes 77 (13.9) No 548 (98.9)
No 477 (86.1) Type of addictive substance used None 548 (98.9)
Infectious disease Yes 59 (10.6) Drugs 5 (0.9)
No 495 (89.4) Tranquilizers 1 (0.2)
Type of infectious disease None 492 (88.8) Addictive drug use in family members None 435 (78.3)
Urogenital 47 (8.5) Opium 60 (10.8)
Sexually transmitted infection 7 (1.3) Cigarettes 35 (6.3)
Viral disease 4 (0.7) Hookah smoking 20 (3.6)
Other infections 4 (0.7) Other 5 (0.9)
Use of birth control methods Yes 29 (5.2) Rupture of water sac during pregnancy Yes 59 (10.6)
No 525 (94.8) No 495 (89.4)
Placenta previa Yes 15 (2.7) Placental Abruption Yes 7 (1.3)
No 539 (97.3) No 547 (98.7)
Gestational Diabetes Yes 66 (11.9) Psychopathy Yes 8 (1.8)
No 488 (88.1) No 546 (98.6)
Domestic violence Yes 9 (6.1) Stomach kick Yes 5 (0.9)
No 545 (98.4) No 549 (99.1)
Presence of uterine disorders Yes 5 (0.9) Cervical insufficiency Yes 10 (1.8)
No 549 (99.1) No 544 (98.2)
Contact with chemical toxins Yes 5 (0.9) Having underlying diseases No 426 (76.9)
No 549 (99.1) Cardiovascular 5 (0.9)
Body mass index ≤18 44 (7.9) Diabetes 29 (5.2)
18 – 25 274 (49.5) Psychological 3 (0.5)
25 to 30 152 (27.4) Kidney 10 (1.8)
More than 30 84 (15.2) Hepatic 8 (1.4)
Having pyelonephritis Yes 13 (2.3) Bloody 14 (2.5)
No 541 (97.7) Immunodeficiency 1 (0.2)
Periodontal diseases Yes 34 (6.1) Other 58 (2.5)
No 520 (93.9)
Maternal hemoglobin in the first 3 months of pregnancy ≤11 89 (16.1) Maternal hemoglobin in the second 3 months of pregnancy <10.5 89 (16.1)
11≤ 465 (83.9) 10.5< 465 (83.9)

Table 4. Statistical relationship between the number of potential risk factors and the risk of PTB.
Variable Specific Potential Risk Factors and their Association with the Number of other Potential Risk Factors in this Cohort of PTBs p-value
No. Risk Factors 1 and 2 Risk Factors 3 and 4 Risk Factors
Number Percent Number Percent Number Percent
Gender of the baby Girl 109 49.8 129 41.1 9 42.9 0.137
Boy 110 50.2 185 58.9 12 57.1
Mother's age in the recent pregnancy ≤18 8 3.7 4 1.3 0 0 0.004
18 -35 183 83.6 241 76.8 13 61.9
35≤ 28 12.8 69 22 8 38.1
Mother's education Illiterate 17 7.8 16 5.1 1 4.8 0.710
Diploma and less 155 70.8 228 72.6 14 66.7
University 47 21.5 70 22.3 6 28.6
Father's education Illiterate 22 10 24 7.6 1 4.8 0.433
Diploma and less 150 68.5 237 75.5 15 71.4
University 47 21.5 53 16.9 5 23.8
Mother's job Employee 11 5 19 6.1 2 9.5 0.222
Housewife 201 91.8 268 85.4 18 85.7
Farmer/rancher 1 0.5 8 2.5 0 0
Other 6 2.7 19 6.1 1 4.8
Father's job Employee 24 11 39 12.4 2 9.5 0.669
Farmer/rancher 83 37.9 115 36.6 7 33.3
Other 110 50.2 150 47.8 11 52.4
Unemployed 2 0.9 10 3.2 1 4.8
Monthly income <5 million 37 16.9 58 18.5 7 33.3 0.356
5 - 10 million 91 41.6 123 39.2 5 23.8
10< million 91 41.6 133 42.4 9 42.9
Type of residential house Personal 160 73.1 243 77.4 19 90.5 0.150
Rental 59 26.9 71 22.6 2 9.5
Drinking water source Piping network 201 91.8 292 93 19 90.5 0.823
Other 18 8.2 22 7 2 9.5
Address City 89 40.6 134 42.7 7 33.3 0.663
Village 130 59.4 180 57.3 14 66.7
Table 5. Association of specific risk factors with a number of other PTB risk factors.
Variables Frequency of PTB as a Function of the Number of Potential Risk Factors Associated with Recent Pregnancies p-value
Up to 8 Risk Factors More than 8 Risk Factors
Number Percent Number Percent
Gender of the baby Girl 240 44.7 7 42.2 0.77
Boy 297 55.3 10 58.8
Mother's age in the recent pregnancy ≤18 12 2.2 0 0 0.74
18 -35 424 79 13 76.5
35≤ 101 18.8 4 26.5
Mother's education Illiterate 30 5.6 4 23.5 0.00
Diploma and less 388 72.3 9 52.9
University 119 22.2 4 23.5
Father's education Illiterate 44 8.2 3 17.6 0.03
Diploma and less 392 73 10 58.8
University 101 18.8 4 23.5
Mother's job Employee 32 6 0 0 0.49
Housewife 470 87.5 17 100
Farmer/rancher 9 17 0 0
Other 26 4.8 0 0
Father's job Employee 65 12.1 0 0 0.34
Farmer/rancher 199 37.1 6 35.3
Other 261 48.6 10 58.8
Unemployed 12 2.2 1 5.9
Monthly income Less than 5 million 95 17.7 7 41.2 0.04
5 to 10 million 213 39.7 6 35.3
More than 10 million 229 42.6 4 23.5
Type of residential house Personal 410 76.4 12 70.6 0.58
Rental 127 23.6 5 29.4
Drinking water source Piping network 496 92.4 16 94.1 0.78
Other 41 7.6 1 5.9
Address City 221 41.2 9 52.9 0.33
Village 316 58.8 8 47.1

The frequency of maternal pregnancy factors (including complications) is shown in Table 3. The most common factor was unwanted pregnancy (17.7%), hemoglobin less than 11 (16.1%), body mass index (BMI) greater than 30 (15.2 percent), pre-eclampsia (13.9%), gestational diabetes (11.9%), opium use in family members (10.8%), and water sac rupture (10.6%).

There was “no” statistical relationship between the number of risk factors associated with the occurrence of PTB in the context of previous pregnancies and demographic variables, except for the mother’s age (p< 0.05) (Table 4).

There was a statistically significant association between the mother's and father's education and the number of PTB risk factors (in both cases, p < 0.05). Also, there was a statistically significant relationship between the family's monthly income and the number of PTB risk factors (p<0.05). However, there was no statistical relationship with other demographic variables (Table 5).


The birth of a preterm infant is one of the serious complications of obstetrics, and prematurity is considered one of the risk indicators for infant death in any society. Various maternal factors have been associated with this phenomenon. Knowledge of these maternal PTB risk factors can guide to eliminate or reduce the impact of such factors. The present study was conducted to determine the maternal factors commonly accompanying PTB in the south of Iran. In this study, the most frequent factors associated with PTB were mothers and fathers with a primary level education, mothers with a housewife occupation, fathers with a farmer/rancher occupation, parental residence in a village, an unwanted pregnancy, maternal hemoglobin less than 11, higher Body Mass Index, pre-eclampsia, gestational diabetes, opium use, and history of cesarean. There was a statistically significant relationship between the number of PTB risk factors and the mother's and father's education and monthly family income.

We found the frequency of low education in parents of premature infants to be very high. Similarly, Enayat Rad et al. [9] and Eshghizadeh et al. [10] found that the lower education level of the parents is associated with PTB. Also, a study in Spain found that most cases of premature infants occurred in mothers with only a secondary school level education [11, 12].

Also, we found that most mothers of PTBs were housewives, and most fathers were in agriculture and animal husbandry in this rural region of Iran. Those in more urban areas likely had better access to medical facilities than those in rural areas. These families also likely had a higher level of awareness regarding PTB risk, a higher level of health literacy, easier access to information and health education, and finally, the possibility of receiving more advanced care.

Our work supports prior study findings. Enayat Rad et al. [9] found that the chance of a PTB in rural areas was higher than in the city. Also, Zhang et al. [13, 14] and Eshghizadeh et al. [10] found that women who live in the city have a lower chance of a PTB. Other studies found that people who live in rural areas are more prone to PTB due to difficult working conditions and difficult access to health services [15-17]. Our PTB cohort had a high frequency of unwanted pregnancies. A case-control study in Gonabad showed that unwanted pregnancy significantly increases the chance of PTB [10]. Gonabad also showed that the low economic level significantly increased the chance of PTB [10], as found in our study.

We also found that anemia was high among women who had PTBs, and this was consistent with the study of Smith et al. [18].

We also found that an elevated body mass index was common among women with a PTB; this is consistent with the results of Enayat Rad et al.'s study [9], which found a significant relationship between body mass index and premature infants. They found that very thin and fat women have a higher chance of giving birth to a preterm infant. In this regard, Schummers et al. found that pre-pregnancy weight loss counseling and achievable weight loss goals for patients could reduce PTB risk [19]. The results of Jafari et al.'s study [20] are inconsistent with this finding of the present study, as they did not find a significant relationship between body mass index and PTB. The different results may be due to a different age classification scheme and population social and cultural differences.

We found that preeclampsia was high among PTB mothers with a preterm infant, similar to the pre-eclampsia findings of Shulman et al. [21] and the hypertension findings of Fuchs et al. [22]. Similar to our study, Enayat Rad et al. [9] found that a mother's illness was associated with a higher PTB risk. Conversely, in an urban setting, Davari et al. [23] demonstrated a significant relationship between maternal illness and PTB in Tehran. This difference in findings could be due to the difference in urban versus rural access to care and types of illness in those settings during pregnancy.

We found that gestational diabetes is common among rural Iranian women with PTBs. These findings suggest that greater attention to managing diabetes could reduce the risk of PTBs. Hawryluk et al. found gestational diabetes to be associated with increased congenital disabilities at birth, disruption of intrauterine growth of the fetus, a higher incidence of premature infants, and a higher percentage of intrauterine fetal death [24]. Also, Xie et al., in a meta-analysis study, found that gestational diabetes was associated with a higher incidence of cesarean births, hypertension or preeclampsia, premature rupture of membranes, premature infants, neonatal asphyxia, and polyhydramnios [25].

We found that smoking was higher in mothers with a PTB, and this finding is in line with other related studies [26, 27]. Also, Wallace et al. found that smoking has a strong relationship with preterm birth, and quitting smoking can greatly reduce this risk [28]. Ion et al. found that PTBs and smoking were linked through mechanisms including vasoconstriction caused by nicotine, fetal hypoxia caused by carbon monoxide, cadmium disruption in calcium signaling, changes in steroid hormone production, disruption in prostaglandin synthesis, and vascular changes in response to oxytocin. The relative importance of each of these pathways has not yet been determined, and further research is necessary to explore the mechanisms through which smoking exerts its effects on pregnancy length and the birth process [29]. Although Dadipour et al. found an association between PTB and drug addiction [30], the small number of subjects admitting to addiction precluded us from evaluating this factor effectively in our study.

We found the history of cesarean section in previous pregnancies to be high in mothers with a PTB, consistent with other studies. Leal et al. found a high rate of PTB to be associated with previous cesarean deliveries [31].

The limitations of the retrospective cohort study design require caution when assigning causality of common factors to PTB. Also, we did not specifically study the nutritional status of the mothers during pregnancy. Maternal (and fetal) health is likely closely related to the mother’s nutritional status. Also, we may have only incomplete information related to substance addiction, domestic violence and smoking, as some women may have been reluctant to share historical information on these topics. Further, a comparison of women of similar age from the same region and time frame without PTB would be helpful for doing a more sensitive case-control analysis.


Spontaneous premature infants were common in socially vulnerable groups, such as rural residents or people with low education and a poor economic situation. It is emphasized that reducing social and health inequalities will likely reduce premature birth rates.


RF, MN, and MA contributed to the concept, design, literature search, data acquisition, and manuscript editing. RR and VM contributed to the data analysis, statistical analysis, and manuscript editing. SD supervised and reviewed the manuscript. All authors read and approved the final manuscript.


This article is taken from a research project supported by Jiroft University of Medical Sciences with Code of Ethics IR.JMU.REC.1399.086


No animals were used in this research. All procedures performed in studies involving human participants were in accordance with the ethical standards of institutional and/or research committees and with the 1975 Declaration of Helsinki, as revised in 2013.


Informed consent was obtained from all participants.


This study was funded by Jiroft University of Medical Sciences, Funder ID 1399.086, (Awards/Grant number 1399.086).


STROBE guideline has been followed.


The data supported the findings of this study will be available upon request for the corresponding author [S.D].


The authors declare no conflict of interest, financial or otherwise.


We are hereby grateful to all the participants in the study as well as the health personnel who helped us conduct this study.


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