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
Identifiers and Pagination:Year: 2023
E-location ID: e187494452306051
Publisher ID: e187494452306051
Article History:Received Date: 26/01/2023
Revision Received Date: 06/05/2023
Acceptance Date: 12/05/2023
Electronic publication date: 11/07/2023
Collection year: 2023
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: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.
The global burden of preterm birth (PTB) includes the morbidity and mortality of infants born before 37 weeks of gestation . 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 . 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%) .
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 . 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 . 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 .
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 . 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 . 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).
|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)|
|Diploma and less||397 (71.7)||Other||271 (48.9)|
|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)|
|Village||324 (58.5)||Drinking water source||Piping network||512 (92.4)|
|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)|
|Variable||Number (Percent)||Variable||Number (Percent)|
|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)|
|No||531 (95.8)||Use of addictive substances
by the mother
|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)|
|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|
|Gender of the baby||Girl||109||49.8||129||41.1||9||42.9||0.137|
|Mother's age in the recent pregnancy||≤18||8||3.7||4||1.3||0||0||0.004|
|Diploma and less||155||70.8||228||72.6||14||66.7|
|Diploma and less||150||68.5||237||75.5||15||71.4|
|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|
|Type of residential house||Personal||160||73.1||243||77.4||19||90.5||0.150|
|Drinking water source||Piping network||201||91.8||292||93||19||90.5||0.823|
|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|
|Gender of the baby||Girl||240||44.7||7||42.2||0.77|
|Mother's age in the recent pregnancy||≤18||12||2.2||0||0||0.74|
|Diploma and less||388||72.3||9||52.9|
|Diploma and less||392||73||10||58.8|
|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|
|Drinking water source||Piping network||496||92.4||16||94.1||0.78|
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.  and Eshghizadeh et al.  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.  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.  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 . Gonabad also showed that the low economic level significantly increased the chance of PTB , 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. .
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 , 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 . The results of Jafari et al.'s study  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.  and the hypertension findings of Fuchs et al. . Similar to our study, Enayat Rad et al.  found that a mother's illness was associated with a higher PTB risk. Conversely, in an urban setting, Davari et al.  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 . 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 .
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 . 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 . Although Dadipour et al. found an association between PTB and drug addiction , 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 .
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.
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.
ETHICS APPROVAL AND CONSENT TO PARTICIPATE
This article is taken from a research project supported by Jiroft University of Medical Sciences with Code of Ethics IR.JMU.REC.1399.086
HUMAN AND ANIMAL RIGHTS
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.
CONSENT FOR PUBLICATION
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).
STANDARDS OF REPORTING
STROBE guideline has been followed.
AVAILABILITY OF DATA AND MATERIALS
The data supported the findings of this study will be available upon request for the corresponding author [S.D].
CONFLICT OF INTEREST
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.