Introduction. Preterm birth is a significant problem in maternal and fetal health worldwide, being one of the most common causes of infant mortality and morbidity. Its prevalence is alarmingly high, the causes being unknown in a substantial proportion of cases. However, there are a considerable number of biological, psychological and social factors that have been studied over the years to identify an etiological pattern of prematurity.
Purpose. The present research aimed to identify factors related to living and environmental conditions that may contribute to the development of pregnancy and that may lead to a premature birth.
Materials and method. To collect the data, an online questionnaire was used for mothers who had given birth no more than 24 months before completing the questionnaire, distributed through social media platforms on specific groups. The resulting information was collected in a database, using Microsoft Office Excel 2019, the data being subsequently statistically processed using Microsoft Office Excel 2019 and SPSS 20.
Results. The high level of education was identified as a protective factor against a premature birth. Also, among mothers employed during pregnancy, the percentage of premature births was much lower than among those without a job. Regarding marital status, no statistically significant correlation was identified between the mother’s marital status and the degree of prematurity.
Conclusions. The results of this research suggest that certain factors related to living and environmental conditions may contribute to the development of the pregnancy and lead to a premature birth. These results could be a starting point for further research to analyze the psychosocial factors that influence pregnancy.
Introducere. Naşterea prematură reprezintă o problemă semnificativă în domeniul sănătăţii materno-fetale din întreaga lume, fiind una dintre cele mai frecvente cauze de mortalitate şi morbiditate infantilă. Prevalenţa sa este inacceptabil de mare, iar cauzele sunt necunoscute într-o proporţie substanţială de cazuri, însă există un număr considerabil de factori biologici, psihologici şi sociali care au fost studiaţi de-a lungul anilor în vederea identificării unui model etiologic al prematurităţii.
Scop. Cercetarea de faţă şi-a propus să identifice factorii legaţi de condiţiile de viaţă şi mediu care pot contribui la desfăşurarea sarcinii şi care pot conduce la o naştere prematură.
Materiale şi metodă. Pentru colectarea datelor s-a folosit un chestionar online adresat mamelor care au avut o naştere în urmă cu cel mult 24 de luni anterior completării chestionarului, distribuit prin intermediul platformelor de socializare, pe grupurile de profil. Informaţiile rezultate au fost cuprinse într-o bază de date, folosind Microsoft Office Excel 2019, datele fiind ulterior prelucrate statistic cu ajutorul programelor Microsoft Office Excel 2019 şi SPSS 20.
Rezultate. S-a identificat nivelul educaţional ridicat ca factor protector faţă de o naştere prematură. De asemenea, în rândul mamelor angajate pe parcursul sarcinii, procentajul naşterilor premature a fost mult mai mic decât în rândul celor fără loc de muncă. În ceea ce priveşte starea civilă, nu a fost identificată o corelaţie semnificativă statistic între statusul marital al mamei şi gradul de prematuritate.
Concluzii. Rezultatele cercetării de faţă sugrează că anumiţi factori legaţi de condiţiile de viaţă şi mediu pot contribui la desfăşurarea sarcinii şi pot conduce la o naştere prematură. Aceste rezultate ar putea fi un punct de plecare pentru cercetări ulterioare care să analizele factorii psihosociali care influenţează sarcina.
Preterm birth (PTB; less than 37 weeks of gestation) remains a significant burden both individually and in our society. The World Health Organization (WHO) estimated in 2016 that the proportion of premature births varied between 5% and 18% globally, with the rate increasing in some countries(3). While many sociodemographic, nutritional, medical, obstetrical and environmental factors have been shown to increase the risk of spontaneous preterm birth, its etiology remains imperfectly understood(4,5). Despite decades of study, few risk factors for PTB have been identified that span all subtypes and populations. Also, a significant number of PTBs do not have a clearly reported risk factor. Although a number of maternal characteristics have been shown to be associated with specific subtypes of PTB in one or more populations, differences between definitions of PTB in several studies and consideration of subtypes led to variable conclusions. Elucidating the commonalities between the determinant factors studied in this research can help us better understand the etiology of PTB and can lead to strategies to reduce the cumulative burden of PTB(6,7).
Of all the known risk factors of premature birth, psychosocial factors form a complicated collection, which includes a multidimensional spectrum of interdependent mediation mechanism. Understanding these interconnected mechanisms is vital for designing targeted interventions to reduce preterm births(8). There are studies that describe a socio-medical model to explain the interaction of psychosocial factors with certain biomedical fields, such as preexisting conditions, pregnancy complications and maternal behaviors(9).
Regarding the age of the mother, the risk of premature birth is high in both adolescent pregnancies and in elderly mothers(10-12). A meta-analysis of cohort studies found that nulliparous women under the age of 18 years old have the highest risk of premature birth in all age/parity categories(13). Premature birth in a previous pregnancy remains a strong risk factor for premature birth in a later pregnancy, as does maternal nulliparity, as demonstrated in various researches, such as those conducted by Kazemier or Ferrero(14,15).
The educational level of mothers is another factor considered in the etiological model of PTB. According to data published by Ruiz et al., low maternal education was associated with premature birth, although this aspect could not be separated from the link with maternal age in the study(16). A study conducted in the USA that considered maternal and paternal education, maternal and paternal occupation, as well as family income, found that levels of maternal and paternal education were the best global predictors of preterm birth. Mothers with a medium or low level of education (less than 12 years) were identified as having a higher chance of having a premature birth than mothers with more education(17). In contrast, according to the results published by Thompson et al., a relatively equal increase in premature births was observed among groups divided by the mother’s educational levels(18). Also, the results of a study conducted in four European countries (Denmark, Finland, Norway and Sweden) showed that, compared to mothers with less than 12 years of education, mothers with more than 10 years of education had similar high risks of preterm birth(19).
The marital status of the mother has been identified in a large number of populations as a risk factor for premature birth, single mothers (defined as unmarried or living without a partner) being the category subjected to a higher degree of risk(20-26). There are several interdependent reasons why being unmarried could increase the risk of having a premature birth, some of which may be: greater economic insecurity faced by single mothers(20,27-29), lack of social or emotional support offered by a partner, or the acute stress generated by the separation of the partner during pregnancy(30-33).
The mothers’ job situation and the level of stress associated with the workplace have also been reported to associate with preterm birth by some studies(17). This can be explained by the fact that demanding work schedules can alter sleep patterns and the neuroendocrine balance of pregnancy, further generating stress(34,35). According to a study conducted in 16 European countries, mothers who had an extended work schedule (more than 42 hours/week), a prolonged standing time (more 6 hours/day) and those with lower job satisfaction were more likely to have premature births(36).
It is well known that all these factors increase the likelihood of adverse health outcomes; however, their possible mechanisms of interaction, resulting in preterm birth, have yet to be explored. The aim of this paper was to examine the relationships between identified maternal psychosocial risk factors, which can act in a complex way to cause premature birth.
The research presented in this paper was based on an observational, retrospective, descriptive analysis study. The information used in this research was collected between April and August 2020. Given the epidemiological context and the restrictions imposed, data collection could not be done in a physical format in hospital wards, which is why an online questionnaire was used, distributed through social platforms on profile groups. In order to validate and confirm the data, the respondents were asked to agree to be contacted by phone.
The questionnaire was addressed to mothers who had given birth no more than 24 months before completing the questionnaire. The purpose of the questionnaire was to obtain information on the progress of pregnancy and premature births. The questionnaire also included information on sociodemographic data related to the mother (age, educational level, information about the family situation, about the job etc.) and the father. Also, data were collected on the mother’s medical situation (chronic diseases, personal pathological history, hereditary history) and on pregnancy and birth (getting pregnant, number of pregnancies stopped developing, complications, treatments etc.).
The data obtained by completing the online questionnaire were entered into a database using Microsoft Office Excel 2019. Subsequently, the data were statistically processed using the Microsoft Office Excel 2019 and SPSS 20 programs.
Of the 331 responses received to the online questionnaire, those that were incomplete and those in which the birth occurred more than 24 months before completion were excluded. Thus, 292 women were included in the database, who gave birth at least once in the last two years. The age of the mothers at the time of completing the questionnaire was between 18 and 50 years old. The minimum age at birth was 18 years old, and the maximum age was 48 years old, with an average birth age of 30.11±4.76. The age of fathers at birth ranged from 21 to 47 years old, with an average of 33.08±4.76 years old.
Figure 1 shows the number of previous births. Thus, 55.15% (n=161) of the respondents were at the first birth, 31.51% (n=92) at the second birth, and 13.36% (n=39) had more than two previous births.
More than half of the respondents were at their first birth, at an average age of 30.11 years old. These figures are in line with the global trend of increased average age at which women have their first child. According to the EUROSTAT report, the average age at which women have their first child in the European Union was 29.9 years old for 2019, the average ages by country varying between 26.4 and 31.3 years old(1).
Analyzing of the gestational age at which the birth took place, it was found that 37.3% of women gave birth starting with the gestational age of 37 weeks, in the rest of the cases the birth occurred prematurely. Table 1 illustrates the distribution of degrees of prematurity, as follows: grade 0 (birth is not considered premature starting with week 37 of pregnancy), grade I (birth at gestational age of 32-36 weeks), grade II (28-32 weeks), grade III (<28 weeks).
The present research aimed to analyze the statistical correlations between the mother’s living and environmental conditions and prematurity, as well as between the mother’s preexisting medical conditions and the level of prematurity.
The applied questionnaire included data related to the mother’s educational level. We mention that all 292 respondents reported at least secondary education, completing at least 10 grades or a vocational school, so mothers with low or no education were not included in this study. This distribution is among the limitations of the research, and one possible explanation may be that most people who use the virtual environment and online social groups to obtain information and complete such questionnaires are those with at least an average level of education. Figure 2 illustrates the distribution of the educational level in the studied sample. Most of the respondent mothers (138, representing 47.26%) have a university degree. Ninety-one respondents (representing 31.16%) have postgraduate studies, and 63 (representing 21.58%) have secondary education.
Table 2 illustrates the frequencies of degrees of prematurity according to the level of education of mothers. Thus, significant differences are observed between the formed groups. Of the 63 respondents with secondary education, 92.1% (58 subjects) gave birth before the gestational age of 37 weeks, presenting a degree of prematurity (26/41.3% grade 1; 19/30.2% grade 2; and 13/20.6% grade 3 prematurity). Of the group of mothers with university degrees, 66.7% had a degree of prematurity, only 46 (33.3%) giving birth at term. The degrees of prematurity in the group of mothers with university studies were distributed as follows: 42 mothers gave birth between 32 and 36 weeks (30.4%), 26 mothers between 28 and 32 weeks (18.8%), and 24 mothers gave birth before the gestational age of 28 weeks (17.4%). Regarding mothers with postgraduate studies, most gave birth at term – 58 mothers, representing 63.7% of this group. A rate of 33.3% of mothers with postgraduate studies gave birth before the gestational age of 37 weeks, as follows: 16 (17.6%) with grade I prematurity, 11 (12.1%) with grade II prematurity, and 6 (6.6%) grade III prematurity.
The Chi-Square test revealed a statistically significant correlation between the level of education of mothers and the degree of prematurity (p=0.000).
Table 3 shows the frequencies of degrees of prematurity according to the marital status of mothers: married; divorced; unmarried, but with a partner; unmarried, without a partner. In the chosen sample, 257 of the respondents (representing 88%) were married, and 28 (representing 9.6%) were unmarried, but with a stable partner. Only three respondents stated that they were divorced, and only four that they were unmarried and without a partner. Thus, in the sample formed, there was a very small number of single mothers. Out of married mothers, 101 (representing 39.3%) gave birth at term, while 60.7% gave birth before the gestational age of 37 weeks. From the group of unmarried mothers but with a partner, 7 (representing 25%) gave birth at term, and in the case of 21 of them there was a degree of prematurity. The Chi-Square test did not identify a statistically significant correlation between the mother’s marital status and the degree of prematurity (p=0.115). The distribution of marital status in the formed group is one of the limitations of the study, as there was a very small number of divorced mothers or partners.
Of the group formed, 80 respondents (representing 27.4%) were unemployed during pregnancy, while 212 (representing 72.6%) had a job during the period in which they were pregnant. Next, the stress level of mothers at the workplace during pregnancy and the correlations between the stress level and the child’s prematurity level were analyzed.
Table 4 shows the frequencies of degrees of prematurity according to the level of stress of mothers at work during pregnancy. Workplace stress was graded into four levels: 0 (no workplace stress), 1 (minimum workplace stress level), 2 (moderate workplace stress level), and 3 (high stress at work). The assessment of the stress level was a subjective one of the respondents. Thus, reporting the level of stress at work is not done according to an objective scale, but reflects the perception of each of the mothers on the discomfort felt at work. Twenty-four of the respondents employed during pregnancy (representing 11.3%) reported a job in which they did not feel stress, 67 (representing 31.6%) reported a minimum level of stress, 77 (representing 36.3%) reported a moderate level of stress, and 44 (representing 20.8%) felt a high level of stress at work during the period they were pregnant. Of the mothers who reported a job without stress levels, half gave birth on term. Of the total mothers who worked during pregnancy, 41% gave birth at term, 26.9% gave birth between weeks 32 and 36, 20.3% gave birth between weeks 28 and 32, and 11.8% before 28 weeks. Of the mothers without a job during pregnancy, 80% gave birth before 37 weeks of pregnancy (Figure 3). The Chi-Square test did not identify a statistically significant correlation between the mother’s stress level at work during pregnancy and the degree of prematurity (p=0.644).
Discussion and conclusions
This paper aims to investigate whether, in addition to medical factors related to parental health and hereditary history, other factors can be identified as being involved in pregnancy and childbirth. We started from the premise that the mother’s level of information, a stable living environment and a good level of mental comfort during pregnancy can positively influence the development of pregnancy, can contribute in taking it to its term and to a birth without complications.
In the chosen sample, most women were giving birth for the first time. The average age of mothers at birth was 30.11 years old, in line with the growing average European age at which mothers have their first child. Eurostat reports the average age at first birth at 29.9 years old in the European Union for 2019, compared to the average age at first birth of 28.7 years old in 2013.
Out of the group of 292 subjects, 109 mothers (representing 37.32%) gave birth at term, while in 183 mothers (representing 62.68%), the birth occurred before 37 weeks. In 84 of the total subjects, there was a grade I prematurity (representing 28.8% of the total subjects and 45.9% of the prematurity cases), in 56 a grade II prematurity (representing 19.2% of the total subjects and 30.6% of the prematurity cases), and in 43, a grade III prematurity (representing 14.7% of all subjects and 23.5% of cases of prematurity). The high percentage of cases of prematurity in the chosen group is well above the average reported by the European Union (for Romania, in 2015, 8.4% of births were premature)(2). This very high percentage of premature births in the chosen sample can be explained by the method of data collection, online, using groups dedicated to mothers. Thus, it is possible that those mothers who have concerns about pregnancy and prematurity are those who turn to profile groups and are more willing and interested in completing a questionnaire on research related to prematurity.
Analyzing the educational level of mothers, it was found that 21.58% of respondents had a secondary education, 47.26% had a university education and 31.16% had a postgraduate education. Most subjects had higher education (78.42%) and there were no subjects who reported low-level education or no education. Analyzing the link between the level of education and prematurity, a statistically significant correlation was found. In the group of mothers with secondary education, only 7.9% gave birth at term, compared to 33.3% in the group of mothers with university education, and 63.7% in the group of mothers with postgraduate education. Thus, it was found that an increased level of education was correlated with taking the pregnancy to term. This correlation can be explained by the fact that a high educational level translates into a correct and complete information of the mother about the pregnancy and the measures she can take for a normal development of the fetus. Also, the high level of information can be associated with more frequent medical examinations and more rigorous monitoring, thus reducing the risk of a premature birth.
Next, the marital status of the mother was analyzed. In the chosen sample, only seven of the respondents were single mothers, unmarried and without a partner. A total of 257 of the respondents (representing 88%) were married, and 28 (representing 9.6%) were unmarried, but with a stable partner. No statistically significant correlation was identified between marital status and prematurity.
The analysis of the mother’s occupation revealed that most of the respondents had a job during the pregnancy – 212 of the subjects, representing 72.6%. Of these, 24 (representing 11.3%) reported a stress-free job, 67 (representing 31.6%) reported a minimum level of stress, 77 (representing 36.3%) reported a moderate level of stress, and 44 (representing 20.8%) felt a high level of stress at work during the period when they were pregnant. Of the mothers who reported a stress-free job, 50% gave birth on term. Also, 40.3% of those who reported a low level of stress, 36.4% of those who reported a moderate level of stress, and 35.5% of those who reported a high level of stress at work gave birth at term. No statistically significant correlation was identified between stress at the workplace and prematurity, but the percentage of mothers who gave birth prematurely was much higher among the women unemployed during pregnancy (80% of unemployed mothers gave birth prematurely, compared to 59% of those with a job during pregnancy, regardless of the reported stress level).
The present research aimed to identify factors related to living and environmental conditions that may contribute to the development of pregnancy and which may lead to a premature birth. The high level of education was identified as a protective factor against a premature birth. Also, among mothers employed during pregnancy, the percentage of premature births was much lower than among those without a job. These results could be a starting point for further research to analyze the psychosocial factors that influence pregnancy, with premature birth remaining a global public health priority. The development of evidence-based strategies to prevent the onset of prematurity, as well as to mitigate its effects on newborns, is needed, especially in low-resource settings. The volume and quality of data on PTB risk factors vary between states, preventing accurate conclusions globally. Thus, further research is needed on the discovery of spontaneous mechanisms of PTB, in order to be better predicted, prevented and controlled by improving information in the population, prenatal care and establishing favorable social environments for pregnant women.
The method of obtaining the data can be considered one of the limitations of this research. Taking into account the epidemiological context, it was decided to collect the data through an online questionnaire. Online application of questionnaires and scales can decrease the veracity and accuracy of the data. In order to limit the effects of this disadvantage, the selective contacting of the subjects was chosen to confirm the completed data. Obtaining data by using the profile groups on social networks may have influenced the composition of the study group, in which the percentage of premature births was very high. This distribution can be explained by the profile of mothers who turn to these groups, as they are the ones who have encountered difficulties during pregnancy or childbirth and seek opinions or advice from people with the same type of problems. In the future, depending on the epidemiological context, the research can be extended and completed by applying questionnaires in the neonatology departments.
Conflicts of interest
The authors declare that they have no conflict of interest.
Eurostat, Data explorer, Fertility indicators.
European Perinatal Health Report, 2015, Europeristat.
World Health Organization, 2016. Preterm Birth. Available online at World Health Organization. http://www.who.int/mediacentre/factsheets/fs363/en/.
Goldenberg RL, Culhane JF, Iams JD, Romero R. Epidemiology and causes of preterm birth. Lancet. 2008;371(9606):75-84.
Muglia LJ, Katz M. The enigma of spontaneous preterm birth. N Engl J Med. 2010;362(6):529-35.
Jelliffe-Pawlowski LL, Baer RJ, Blumenfeld YJ, Ryckman KK, O’Brodovich HM, Gould JB, Druzin ML, El-Sayed YY, Lyell DJ, Stevenson DK, Shaw GM, Currier RJ. Maternal characteristics and mid-pregnancy serum biomarkers as risk factors for subtypes of preterm birth. BJOG. 2015;122:1484–1493.
Vogel JP, Chawanpaiboon S, Moller AB, Watananirun K, Bonet M, Lumbiganon P. The global epidemiology of preterm birth. Best Practice & Research Clinical Obstetrics & Gynaecology. 2018; S152169341830079. doi:10.1016/j.bpobgyn.2018.04.003
Batra K, Pharr J, Olawepo JO, Cruz P. Understanding the Multidimensional Trajectory of Psychosocial Maternal Risk Factors Causing Preterm Birth: A Systematic Review. Asian Journal of Psychiatry. 2020;102436. doi:10.1016/j.ajp.2020.102436
Misra D, Strobino D, Trabert B. Effects of social and psychosocial factors on risk of preterm birth in black women. Paediatr Perinat Epidemiol. 2010;24(6):546–554.
Smith GC, Pell JP. Teenage pregnancy and risk of adverse perinatal outcomes associated with first and second births: population based retrospective cohort study. BMJ. 2001;323(7311):476.
Waldenström U, Aasheim V, Nilsen AB, Rasmussen S, Pettersson HJ, Schytt E, et al. Adverse pregnancy outcomes related to advanced maternal age compared with smoking and being overweight. Obstet Gynecol. 2014;123(1):104-12.
Carolan M. Maternal age ≥45 years and maternal and perinatal outcomes: a review of the evidence. Midwifery. 2013;29(5):479-89.
Kozuki N, Lee AC, Silveira MF, Sania A, Vogel JP, Adair L, et al. The associations of parity and maternal age with small-for-gestational-age, preterm, and neonatal and infant mortality: a meta-analysis. BMC Public Health. 2013;13 Suppl 3:S2.
Kazemier BM, Buijs PE, Mignini L, Limpens J, de Groot CJ, Mol BW, et al. Impact of obstetric history on the risk of spontaneous preterm birth in singleton and multiple pregnancies: a systematic review. BJOG. 2014;121(10):1197-208; discussion 209.
Ferrero DM, Larson J, Jacobsson B, Di Renzo GC, Norman JE, Martin JN, et al. Cross-Country Individual Participant Analysis of 4.1 Million Singleton Births in 5 Countries with Very High Human Development Index Confirms Known Associations but Provides No Biologic Explanation for 2/3 of All Preterm Births. PLoS One. 2016;11(9):e0162506.
Ruiz M, Goldblatt P, Morrison J, Kukla L, Švancara J, Riitta-Järvelin 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.
Parker JD, Schoendorf KC, Kiely JL. Associations between measures of socioeconomic status and low birth weight, small for gestational age, and premature delivery in the United States. Ann Epidemiol. 1994;4(4):271–278.
Thompson JMD, Irgens LM, Rasmussen S, Daltveit AK. Secular trends in socio-economic status and the implications for preterm birth. Paediatric and Perinatal Epidemiology. 2006;20:182–187.
Petersen CB, Mortensen LH, Morgen CS, Madsen M, Schnor O, Arntzen A, Gissler M, Cnattingius S, Nybo Andersen A-M. Socio-economic inequality in preterm birth: a comparative study of the Nordic countries from 1981 to 2000. Paediatric and Perinatal Epidemiology. 2009;23:66–75.
Zeitlin JA, Saurel-Cubizolles MC, Ancel PY. The EUROPOP Group. Marital status, cohabitation, and the risk of preterm birth in Europe: where births outside marriage are common and uncommon. Pediatr Perinat Epidemiol. 2002;16(2):124–130.
Berkowitz GS, Papiernik E. Epidemiology of preterm birth. Epidemiologic Reviews. 1993;15:414–443.
Blondel B, Zuber MC. Marital status and cohabitation during pregnancy: relationship with social conditions, antenatal care and pregnancy outcome in France. Paediatric and Perinatal Epidemiology. 1988;2:125–137.
Olsén P, Läära E, Rantakallio P, Järvelin M-R, Sarpola A, Hartikainen A-L. Epidemiology of preterm delivery in two birth cohorts with an interval of 20 years. American Journal of Epidemiology. 1995;142:1184–1193.
Pickering RM, Deeks JJ. Risks of delivery during the 20th to the 36th week of gestation. International Journal of Epidemiology. 1991;20:456–466.
Vagero D, Koupilova I, Leon DA, Lithell UB. Social determinants of birthweight, ponderal index and gestational age in Sweden in the 1920s and the 1980s. Acta Paediatrica. 1999;88:445–453.
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):168–174.
South S. Historical changes and life course variation in the determinants of premarital childbearing. Journal of Marriage and the Family. 1999;61:752–763.
Remez L. Married mothers fare the best economically, even if they were unwed at the time they gave birth. Family Planning Perspectives. 1999;31:258–259.
Bracher M, Santow G. Economic independence and union formation in Sweden. Population Studies. 1998;52:275–294.
Hedegaard M, Henriksen TB, Sabroe S, Secher NJ. Psychological distress in pregnancy and preterm delivery. British Medical Journal. 1993;307:234–239.
Nordentoft M, Lou HC, Hansen D, Nim J, Pryds O, Rubin P, et al. Intrauterine growth retardation and premature delivery: the influence of maternal smoking and psychosocial factors. American Journal of Public Health. 1996;86:347–354.
Hetherington E, Doktorchik C, Premji SS, McDonald SW, Tough SC, Sauve RS. Preterm birth and social support during pregnancy: a systematic review and meta-analysis. Paediatr Perinat Epidemiol. 2015;29(6):523–535.
Shah PS, Zao J, Ali S. Maternal marital status and birth outcomes: a systematic review and meta-analyses. Matern Child Health J. 2010;15:1097–1109.
Mong JA, Baker FC, Mahoney MM, Paul KN, Schwartz MD, Semba K, Silver R. Sleep, rhythms, and the endocrine brain: influence of sex and gonadal hormones. J Neurosci. 2011;31(45):16107–16116.
Von Ehrenstein OS, Wilhelm M, Wang A, Ritz B. Preterm birth and prenatal maternal occupation: the role of hispanic ethnicity and nativity in a population-based sample in Los Angeles, California. Am J Public Health. 2014;104(S1):S65–S72.
Saurel-Cubizolles MJ, Zeitlin J, Lelong N, Papiernik E, Di Renzo GC, Bréart G, Group, Europop. Employment, working conditions, and preterm birth: results from the Europop case-control survey. J Epidemiol Community Health. 2004;58(5):395–401.