CC BY 4.0 · Journal of Health and Allied Sciences NU 2023; 13(04): 568-574
DOI: 10.1055/s-0043-1761609
Original Article

Socioeconomic Risk Factors for Preterm Birth in Manipur, Northeast India: A Community-Based Study

1   Department of Anthropology, Manipur University, Canchipur, Imphal, India
,
Huidrom Suraj Singh
1   Department of Anthropology, Manipur University, Canchipur, Imphal, India
› Author Affiliations
Funding None.
 

Abstract

Background Preterm birth (PTB) is one of the world's leading health concerns, affecting both the mother and the children. This study was undertaken to determine the prevalence and socioeconomic factors associated with PTB among the Meitei women of Manipur.

Materials and Methods A community-based cross-sectional study was conducted in postpartum Meitei women of Manipur. Participants were 126 postpartum women that gave birth to a singleton live-born infant and were classified as women giving birth before 37 weeks of gestation (PTB) and women giving birth at ≥37 weeks (term). Data were compared using univariate analysis, and the association of socioeconomic factors with PTB was determined through multivariate logistic regression using Statistical Package for Social Sciences 25 version software program, and statistical significance was taken at a p-value < 0.05.

Results The overall prevalence of PTB is 23.01%, of which 13.79% are of extremely preterm. This study revealed a significant association of PTB with mother's occupation (adjusted odds ratio [AOR] = 4.46, 95% confidence interval [CI]: 1.40–14.26, p = 0.012), tobacco consumption during pregnancy (AOR = 2.90, 95% CI: 1.01–8.33, p = 0.048), having family history of PTB (AOR = 3.14, 95% CI: 1.09–9.04, p = 0.034), and early age at menarche (AOR = 4.26, 95% CI: 1.49–12.12, p = 0.007).

Conclusion The study highlights the high prevalence of PTB and its association with various socioeconomic factors. Such community-specific studies should be performed to understand the differential risk factors of PTB to control premature death in under 5 years children and to promote women's reproductive health.


#

Introduction

Preterm birth (PTB), <37 weeks of gestation, is one of the world's leading causes of death in children under 5 years of age. It increases the risk of high mortality and lifelong impairment.[1] It is estimated to be a risk factor in at least 50% of all neonatal deaths.[2] More than 60% of the total PTBs occur in Africa and South Asia. In low-income countries, 12% of babies are born too early, which is comparatively higher than in high-income countries (9%). Studies reported an increasing trend in PTB rates over the past two decades, specifically from low- and middle-income countries.[3] In India, the prevalence of PTB varies significantly from 6.1% in Maharashtra to 28.25% in Tamil Nadu.[4] [5] India contributes 23.4% of the global PTB.[6] The PTB affects both the mother and the children with an increased risk of developing short- and long-term chronic noncommunicable diseases.[7] Identifying the causes of PTB is one of the major areas of research to prevent PTB and its associated adverse health outcomes. The occurrence of PTB has been linked with multiple factors such as genetic, pregnancy-induced chronic diseases (hypertension, diabetes), infections, early induced labor, multiple gestations, anemia, antenatal care visits, age at menarche, etc.[8] Moreover, socioeconomic factors of the mother are associated with the occurrence of PTB, including less maternal income, low level of education, occupation, early childbearing, smoking, and place of residence.[9] [10] [11] [12] However, ethnic differences in the causation of PTB remain. Thus, identifying women at high risk of PTB at an early pregnancy stage may allow timely intervention against PTB and future life-threatening health events.[13]

The epidemiology of PTB has not been explored in most of the Indian population. Moreover, existing studies are concentrated only in some specific states, with limited studies predicting risk factors contributing to PTB.[8] However, studies on PTB have been lacking among the Northeast Indian populations. A limited study has been performed reporting the association of PTB with maternal low hemoglobin levels during pregnancy and also claimed risk of low birth weight among PTB babies.[14] Thus, a preliminary study was conducted to determine the prevalence and correlate PTB with socioeconomic factors among the Meitei women of Manipur, Northeast India, to understand the population-specific PTB prognosis.


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Materials and Methods

Design, Population, and Setting of the Study

A community-based cross-sectional study was conducted among the postpartum Meitei women of Manipur, Northeast India. Women from the Meitei ethnic group, with an East Asian Ancestry, residing in urban and rural areas were recruited in the present study from October 2019 to January 2020. In this study, two districts, Imphal East and Imphal West, were selected considering the poor reproductive health conditions per the reports of the National Family Health Survey, Manipur. The highest burden of anemia and increased rate of early childbearing age were reported in Imphal West. In contrast, Imphal East reports the worst performing district for inadequate antenatal care during the first trimester.[15]

Postpartum women that gave birth to a singleton live-born infant and women who are residing permanently in the area and willing to participate in the study were included in the present study. Women with twin pregnancies, cognitive and physical limitations, and those unwilling to participate were excluded from the study.


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Variables

All the information was collected through a structured interview schedule consisting of questions on socioeconomic characteristics such as age at conception, gestational age, educational level, occupation, income, place of residence, and social category (unreserved or schedule caste) were assessed after obtaining informed written consent. Information on mother menarche age (<13 years or ≥13 years), antenatal care visit (less than four visits and four or more visits), family history of PTB (either maternal or paternal), and tobacco consumption/chewing during pregnancy was also obtained through the interview. Data on the mother's age at conception were obtained with childbearing ages of <20 years and ≥20 years. Regarding maternal occupational status, women with no current occupation but managing household affairs and doing housework were categorized as housewives, while women engaged as artisans, laborers, manual work, and physical exertion-related work as full-time workers.


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Data Analysis

The data collected were initially transferred to MS Excel software and coded, and further statistical analyses were performed using Statistical Package for Social Sciences 25 version. The prevalence of PTB was calculated in the studied community. Univariate analysis was performed to determine the association of socioeconomic variables with PTB. The significant variables as observed in the univariate analysis were further adjusted to perform the multivariate analysis using logistic regression. Statistical significance was taken based on a p-value <0.05.


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Results

The overall prevalence of PTB was 23.01%. Of the different types of PTB, moderate-to-late preterm comprised 55.17%, very preterm 31.03%, and extremely preterm 13.79%. Socioeconomic characteristics revealed that mothers with a monthly income of less than 10,000 rupees were significantly more common in the preterm group than in the term group (41.37 vs. 20.61%, p = 0.024). Low-income mothers were 2.7 (odds ratio [OR] = 2.72, 95% confidence interval [CI]: 1.12–6.60, p = 0.024) times more likely to experience PTB than high-income mothers.

Similarly, full-time working women were found quite frequently in the preterm group than in the term group (41.37 vs. 18.55%), with 3.10 times more likely to deliver PTB than the housewife mothers (OR = 3.10, 95% CI: 1.26–7.61, p = 0.011). Moreover, mothers who consumed tobacco were more likely to experience PTB than those who did not (OR = 3.03, 95% CI: 1.28–7.15, p = 0.01). Regarding the educational level, there were significantly more women with illiteracy and elementary education in the preterm group than in the term group (65.52 vs. 43.29%, p = 0.036). Mothers with a family history of PTB were 2.84 times more likely to give preterm delivery than their counterparts (OR = 2.84, 95% CI: 1.20–6.72, p = 0.015). The proportion of mothers with early menarche was significantly higher among the preterm groups than in the term group (55.17 vs. 29.89%, p = 0.013). The analysis also showed that the chances of giving PTB were significantly higher among the mothers who visited fewer antenatal check-ups during pregnancy than those with adequate antenatal care (ANC) visits (51.72 vs. 28.86%, p = 0.023). Most of the mothers delivering PTB children resided in rural areas (86.20%) and had early childbearing age (34.48%) than the mothers who gave term birth. However, no statistically significant difference was observed between the two groups (p > 0.05). The social category of the mother did not show any significant differences between the unreserved category and schedule caste group with respect to PTB (p = 0.372; [Table 1]).

Table 1

Distribution of socioeconomic characteristics of Meitei women in preterm and term groups

Socioeconomic characteristics

Birth

p-Value

OR

CI

Preterm

(29, 23.01%)

Full-term

(97, 76.9%)

No.

%

No.

%

Mother's income (per month)

Less than 10,000

12

41.37

20

20.61

p = 0.024

OR = 2.72

CI = 1.12–6.60

More than 10,000

17

58.62

77

79.38

Mother's occupation

Housewives

17

58.62

79

81.44

p = 0.011

OR= 3.10

CI = 1.26–7.61

Full time workers

12

41.37

18

18.55

Tobacco consumption (during pregnancy)

No

11

37.93

63

64.94

p = 0.010

OR = 3.03

CI = 1.28–7.15

Yes

18

62.06

34

35.05

Level of education

Illiterate and elementary

19

65.52

42

43.29

p = 0.036

OR = 0.40

CI = 0.17–0.95

Higher secondary and above

10

34.48

55

56.70

Age at menarche

Less than 13 y

16

55.17

29

29.89

p = 0.013

OR = 0.35

CI = 0.15–0.81

More than 13 y

13

44.83

68

70.10

Childbearing age

< 20 y

10

34.48

23

23.71

p = 0.247

OR = 1.69

CI = 0.69–4.15

≥ 20 y

19

65.51

74

76.28

Social category

Unreserved

20

68.96

58

59.79

p = 0.372

OR = 0.67

CI = 0.28–1.62

Scheduled caste

9

31.03

39

40.20

Place of residence

Rural

25

86.20

67

71.27

p = 0.102

OR = 2.54

CI = 0.81–7.96

Urban

4

13.79

30

30.92

Antenatal care (ANC)

Less than four visits

15

51.72

28

28.86

0.023

OR = 0.38

CI = 0.16–0.89

More than four visits

14

48.27

69

71.13

Family history of preterm births

No

15

51.72

73

75.25

0.015

OR = 2.84

CI = 1.20–6.72

Yes

14

48.27

24

24.74

Abbreviations: CI, confidence interval; OR, odds ratio.


We further performed multivariate analysis using logistic regression to investigate which socioeconomic factors were the best predictors of PTB after adjusting the factors like the mother's income, education, occupation, tobacco consumption, antenatal care, age at menarche, and family history of PTB ([Table 2]). The most significant risk observed in the study was the mother's occupation; full-time workers showed greater odds of having PTB (adjusted odds ratio [AOR] = 4.46, 95% CI: 1.40–14.26, p = 0.012) compared with housewife mothers. Early age at menarche revealed a significant association with PTB, with 4.26 times more likely to deliver preterm (AOR = 4.26, 95% CI: 1.49–12.12, p = 0.007). The present study also found that those with a family history of PTB had a threefold increased risk of delivering PTB (AOR = 3.14, 95% CI: 1.09–9.04, p = 0.034). Women who consumed tobacco during pregnancy were 2.90 times more likely to experience delivering PTB than mothers who did not with a statically borderline significant difference (AOR = 2.90, 95% CI: 1.01–8.33, p = 0.048). Notwithstanding this, women with less than four antenatal visits and those with lower educational levels showed a more than twofold increased risk of giving PTB. However, both predictors were statically insignificant after adjusting the risk factors in multivariate logistic regression analysis (p > 0.05).

Table 2

Association between socioeconomic factors and PTB in multivariable logistic regression analysis

Socioeconomic characteristics

OR[a] 95% CI

p-Value

Mother's income

More than 10,000

Ref

p = 0.022

Less than 10,000

0.28(0.10–0.84)

Mother's occupation

Housewives

Ref

p = 0.012

Full time workers

4.46(1.40–14.26)

Tobacco consumption

No

Ref

p = 0.048

Yes

2.90(1.01–8.33)

Level of education

Higher secondary and above

Ref

p = 0.116

Illiterate and elementary

2.30(0.81–6.51)

Antenatal care (ANC)

More than four visits

Ref

p = 0.169

Less than four visits

2.05(0.74–5.73)

Family history of preterm birth

No

Ref

p = 0.034

Yes

3.14(1.09–9.04)

Age at menarche

More than 13 y

Ref

Less than 13 y

4.26(1.49–12.12)

p = 0.007

Abbreviations: CI, confidence interval; OR, odds ratio; PTB, preterm birth.


a Adjusted for mother's income, occupation, tobacco consumption, level of education, age at menarche, antenatal care, and family history of preterm births.



#

Discussion

PTB is considered one of the major health issues of birth outcomes throughout the world, which is also the leading cause of death in children under 5 years of age. More than 15 million babies are born preterm yearly, accounting for approximately more than 1 in every 10 babies. The PTB rate has increased specifically in low- and middle-income countries in the last two decades. Previous studies reported that there is a lack of reliable information on the prevalence of PTB in most of the developing country.[3] [16] In India, studies on PTB are mainly concentrated in some specific states. The present findings showed a high prevalence of PTB, 23.01%, higher than the global prevalence across 184 countries ranging from 5 to 18%.[16] The PTB rate is also found to be higher than in other studies reported from Zimbabwe, Malawi, and Kenya.[17] [18] [19] In India, studies on PTB report varied prevalence rates across different geographical regions.[12] The current finding also reports higher rates of PTB than previous studies as reported from Gujarat (9.0%), Maharashtra (6.1%), and Tamil Nadu (5.6%).[5] [20] [21] Although the high prevalence of PTB in the present study is in accordance with some groups of the Indian population, it suggests a high prevalence of PTB in the Indian population.[4] [11] [12] Of the overall prevalence of PTB, 13.79% are extremely preterm. Such a high alarming prevalence of PTB highlights the need for urgent attention, monitoring, regulation, and intervention of PTB at the population level throughout the country. It will become one of India's major public health burdens if not intervened in time.

Regarding the risk factors of PTB, previous studies have identified several potential predictors of PTB.[8] The current study shows a significant association between a mother's income and PTB occurrence. It supports previous studies in which mother with low monthly income was more likely to give birth to preterm infants.[22] However, the inconsistent finding was also reported from Qatar, suggesting no positive association between a mother's low income and the occurrence of PTB.[9] The education level of mothers has an impact on their pregnancy outcomes. It is claimed that the mother's education level was a strong predictor of PTB. In the study, mothers with lower educational levels have more than twofold increased risk of delivering PTB babies (AOR = 2.30 95% CI: 0.81–6.51). However, it does not reveal statistical significance (p = 0.116). It could be because of the lack of proper educational awareness on maternal health and related pregnancy outcomes among the term group with higher education levels. It is explained by the constitution of 23.63% of mothers reporting inadequate antenatal care visits among the term mothers with higher secondary and above education levels. No association between the mother's education level and PTB is supported by the previous studies.[9] [23] However, different studies also reported inconsistent results suggesting the mother's lower education level is one of the strong predictors of PTB.[24] [25] It suggests that educational awareness of maternal health and pregnancy outcomes could be a strong predictive factor of PTB than mothers' education level, particularly in rural populations of low-middle-developing countries.

It is claimed that the mother's occupation can predict an increased risk for PTB. Women who performed intensive work during pregnancy had a fivefold increased risk of having a PTB (AOR = 5.37, 95% CI: 1.39–20.68).[23] This claim is supported by the present study in which women in full-time work were more likely to give PTB than housewife mothers (AOR = 4.46, 95% CI:1.40–14.26). Studies conducted in Iran and Italy have also reported consistent findings with the present study where heavy working women during pregnancy increase the risk of PTB.[26] [27]

Regarding unhealthy lifestyles, tobacco consumption before or during pregnancy showed a more than twofold increased risk of PTB in the present study (AOR = 2.90, 95% CI: 1.01–8.33, p = 0.048). It is consistent with the study reporting high odds as seven times more likely to deliver PTB among the mothers consuming tobacco than the nonuser mothers (OR = 7.08, 95% CI: 4.14–12.14).[28] Such a significant positive association between tobacco consumption and preterm delivery is further strengthened by the reports of systematic and meta-analysis studies among pregnant women in India.[29] The study reveals maternal tobacco use during pregnancy increases the risk for PTB with a 1.39 pooled odds ratio.

Of the different predictors of PTB, age at menarche has been investigated to predict the risk of preterm delivery. Various PTB studies across the world report significant associations between preterm delivery and mother's menarche onset age. This study reports a significant association between early menarcheal age (mean age 11.5 ± 1.37) and PTB. Women who had menarcheal age less than 13 years of age are found to have a 4.26-fold increased risk of preterm delivery among the studied Meitei women population (AOR = 4.26, 95% CI: 1.49–12.12, p = 0.007). A study on 11,016 Chinese women from the Healthy Baby Cohort between 2012 and 2014 also claims a significant association between age at menarche with PTB (OR = 1.67, 95% CI: 1.18–2.36).[13] No significant association between PTB and age at menarche is also reported in some studies.[30] So far, there is no concrete evidence, to our best knowledge, on the occurrence of preterm delivery due to early age at menarche among the Indian population. This is the first study that reports the risk of preterm delivery in women with an early menarcheal age among the northeast Indian population. It needs to be validated in other Indian populations to establish menarcheal age as one of the potential predictors of PTB.

Receiving antenatal health care services by a mother, particularly during pregnancy, immensely improves overall pregnancy outcomes. Therefore, inadequate antenatal care visits become another vital risk factor for PTB. In the present study, mothers who had inadequate ANC visits were more likely to deliver preterm when compared with those mothers with adequate ANC visits (p = 0.023). However, after adjusting all the significant confounding factors, no significant positive association was observed between inadequate ANC visits and preterm delivery. Similarly, there is no substantial correlation between PTB and the number of ANC visits from the Gambia and the Belgian population.[31] [32] However, another study claims a significant positive association between inadequate ANC visits of the mothers during pregnancy with PTB (AOR = 1.90, 95% CI: 1.13–3.18).[31] [33] It is further supported by other research findings reporting less than four ANC visits during pregnancy were significantly associated with an increased risk of delivering PTB compared with adequate ANC visits.[24] [34]

Different studies have investigated the association between a mother's family history of PTB and the risk of preterm delivery. In the study, mothers with a family history of preterm delivery have a threefold higher risk of PTB than their counterparts (AOR = 3.14, 95% CI: 1.09–9.04). It signifies that women with a family history of PTB either in maternal or paternal (or both) are at higher risk of preterm delivery. Such a positive association is supported by other studies reporting a positive association between a mother's family history of PTB and a higher risk of delivering preterm babies (adjusted relative risk [aRR] = 1.44, 95% CI: 1.22–1.97).[34] [35] Such significant positive association results highlight that PTB runs in families. Moreover, it signifies the inheritance and genetic influence on the duration of gestation, causing the risk of PTB.

Different studies suggest a significant positive association between maternal childbearing age and preterm delivery. The risk of PTB increases significantly among teenage mothers or young maternal age groups.[5] [24] Mothers less than 20 years were significantly associated with a high risk of PTB.[9] However, the present study does not find any significant association between childbearing age and preterm delivery. The study supports such inconsistent findings performed in the Bangladesh population in which women aged <20 years were protective against PTB.[36] Moreover, a longitudinal cohort study also reveals a reduced risk of preterm delivery among Canadian mothers aged 30 to 34 years.[37] Such discrepancy in the results could be addressed by conducting population-specific studies on the differences in PTB risks, such as sociodemographic factors among different ethnic groups.

In the present study, no significant association was found between the mother's residence and delivering preterm. This finding agrees with similar studies suggesting no effect of the mother's place of residence on PTB.[38] In contrast, a cross-sectional study in Uttar Pradesh reported a significant positive association where women residing in rural areas are more likely to give PTB compared with urban residents.[11] Inconsistent findings are also reported that urban resident women had an increased risk of delivering preterm.[39] Such discrepancy might be due to better and easily accessible maternal health services in urban areas, which increases PTB risk in rural residents. On the contrary, the high prevalence and increased risk of having PTB among urban residents might be due to the differential socioeconomic status and lifestyles.[34]

Strengths and Limitations

The present study reveals both strengths and limitations. This study is the outcome of a community-based study performed through a household survey that minimizes the selection bias observed in the hospital-based study. This study is the outcome of preliminary work and the first attempt among the Meitei women of Manipur to determine population-specific socioeconomic risk factors of PTB. We failed to incorporate a large sample size and some of the risk factors of PTB, such as previous PTB, pregnancy-induced hypertension, gestational diabetes, infection during pregnancy, and threatened abortion. It could be the main limitation of the present study. Despite this limitation, the present study addresses the high prevalence of PTB and the significant impact of socioeconomic factors on maternal birth outcomes among Manipur Meitei women, particularly in Northeast India.


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Conclusion

The Meitei women of Manipur, Northeast India, have a comparatively higher prevalence of PTB. Moreover, the present study highlights the significant correlations between the mother's socioeconomic factors and PTB. It is a need to understand the differential socioeconomic patterning with maternal health outcomes and the mechanism underlying such inequalities. Therefore, studies on PTB should focus on modifiable socioeconomic predictors to propose early implementation of preventive interventions among pregnant women who are at high risk for preterm delivery. Such community-specific studies may help prevent preterm delivery and later risk of cardiometabolic health problems in mothers. The present study is of the initial research; more in-depth large population-specific studies covering different risk factors will be needed to replicate and validate the results to develop population-specific interventional strategies.


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Conflict of Interest

None declared.

Acknowledgment

The authors are thankful to all the participants who were involved in the present study for their voluntary participation.

  • References

  • 1 Liu L, Oza S, Hogan D. et al. Global, regional, and national causes of under-5 mortality in 2000-15: an updated systematic analysis with implications for the sustainable development goals. Lancet 2016; 388 (10063): 3027-3035
  • 2 Lawn JE, Kerber K, Enweronu-Laryea C, Cousens S. 3.6 million neonatal deaths—what is progressing and what is not?. Semin Perinatol 2010; 34 (06) 371-386
  • 3 WHO Preterm Birth. “Born too soon,” e Global Action Report for Preterm Birth, MoD, PMNCH, Save the Children. New York, NY: WHO. retrieved on September 10. 4.56 p.m. Accessed September 10, 2012 at: http://www.who.int/news-room/fact-sheets/detail/preterm-birth 2012
  • 4 Kuppusamy N, Vidhyadevi A. Prevalence of preterm admissions and the risk factors of preterm labor in a rural medical college hospital. Int J Sci Stud 2016; 4 (09) 125-128
  • 5 Ahankari A, Bapat S, Myles P, Fogarty A, Tata L. Factors associated with preterm delivery and low birth weight: a study from rural Maharashtra, India. F1000 Res 2017; 6: 72
  • 6 India-profile for preterm birth and low birth weight prevention care [Internet]. [cited 2022 May 23]. Accessed January 12, 2023 at: https://www.healthynewbornnetwork.org/hnn-content/uploads/India-1.pdf
  • 7 Chawanpaiboon S, Vogel JP, Moller AB. et al. Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis. Lancet Glob Health 2019; 7 (01) e37-e46
  • 8 Devi TC, Singh HS. Prevalence and associated risk factors of preterm birth: a review. J Public Health Dent 2021; 19 (02) 209-226
  • 9 Salama E EIAS, Salama HS, Alobaidly SH. Socioeconomic risk factors for preterm birth in the state of Qatar: a population-based study. Acta Biomed 2021; 92 (03) e2021186
  • 10 Tellapagadra C, Eshwara VK, Bhat P. et al. Risk factors for PTB and low birth weight: a hospital-based prospective study. J Prev Med Public Health 2016; 49: 165-175
  • 11 Singh S, Malhotra AK. Across sectional study on prevalence of preterm birth in medical college Jhansi (U.P). Indian J Appl Res 2018; 8 (10) 50-52
  • 12 Dayanithi M. Low birth weight and premature births and their associated maternal factors. Int J Community Med Public Health 2018; 5 (06) 2277-2285
  • 13 Li H, Song L, Shen L. et al. Age at menarche and prevalence of preterm birth: results from the healthy baby cohort study. Sci Rep 2017; 7 (01) 12594
  • 14 Gogoi M, Prusty RK. Maternal anaemia, pregnancy complications and birth outcome: evidences from North-East India. J Near East Stud 2013; 3 (01) 74-85
  • 15 National Family Health Survey (NFHS) India. . 2015–16. International Institute for Population Science, Ministry of Health and Family Welfare, New Delhi, 2017
  • 16 Blencowe H, Cousens S, Oestergaard MZ. et al. National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications. Lancet 2012; 379 (9832): 2162-2172
  • 17 Feresu SA, Harlow SD, Welch K, Gillespie BW. Incidence of and socio-demographic risk factors for stillbirth, preterm birth and low birthweight among Zimbabwean women. Paediatr Perinat Epidemiol 2004; 18 (02) 154-163
  • 18 van den Broek NR, Jean-Baptiste R, Neilson JP. Factors associated with preterm, early preterm and late preterm birth in Malawi. PLoS One 2014; 9 (Suppl. 03) e90128
  • 19 Wagura P, Wasunna A, Laving A, Wamalwa D, Ng'ang'a P. Prevalence and factors associated with preterm birth at Kenyatta National Hospital. BMC Pregnancy Childbirth 2018; 18 (01) 107
  • 20 Soundarajan P, Muthuramu P, Veerapandi M, Marriapam R. Retrospective study factors related to preterm birth in Government Raja Mirasudar Hospital and obstetric and perinatal outcome. Int J Reprod Contracept Obstet Gynecol 2016; 5 (09) 3006-3010
  • 21 Trivedi P, Saxena D, Puwar T, Savaliya S, Ganguly P. A cohort study on risk factors for preterm births in rural Gujarat. Indian J Public Health 2018; 62 (02) 111-116
  • 22 Carcavalli L, Rocha IA, Valentim AF. et al. Difference of socioeconomic factors among mothers of preterm and full-term infant. Pesqui Bras Odontopediatria Clin Integr 2020; 20: e5005
  • 23 Acharya R, Khanal P, Bhattarai HK, Amatya A. Risk factors of preterm birth in Nepal: a hospital-based matched case-control study. Front Reprod Health 2021; 3: 697419
  • 24 Gurung A, Wrammert J, Sunny AK. et al. Incidence, risk factors and consequences of preterm birth—findings from a multi-centric observational study for 14 months in Nepal. Arch Public Health 2020; 78: 64
  • 25 Hidalgo-Lopezosa P, Jiménez-Ruz A, Carmona-Torres JM, Hidalgo-Maestre M, Rodríguez-Borrego MA, López-Soto PJ. Sociodemographic factors associated with preterm birth and low birth weight: a cross-sectional study. Women Birth 2019; 32 (06) e538-e543
  • 26 Di Renzo GC, Giardina I, Rosati A, Clerici G, Torricelli M, Petraglia F. Italian Preterm Network Study Group. Maternal risk factors for preterm birth: a country-based population analysis. Eur J Obstet Gynecol Reprod Biol 2011; 159 (02) 342-346
  • 27 Alijahan R, Hazrati S, Mirzarahimi M, Pourfarzi F, Hadi PA. Prevalence and risk factors associated with preterm birth in Ardabil, Iran. Iran J Reprod Med 2014; 12 (01) 47-56
  • 28 Krishna K. Tobacco chewing in pregnancy. Br J Obstet Gynaecol 1978; 85 (10) 726-728
  • 29 Suliankatchi RA, Sinha DN. The human cost of tobacco chewing among pregnant women in India: a systematic review and meta-analysis. J Obstet Gynaecol India 2016; 66 (Suppl. 01) 161-166
  • 30 Hennessy E, Alberman E. Intergenerational influences affecting birth outcome. II. Preterm delivery and gestational age in the children of the 1958 British birth cohort. Paediatr Perinat Epidemiol 1998; 12 (Suppl. 01) 61-75
  • 31 Jammeh A, Sundby J, Vangen S. Maternal and obstetric risk factors for low birth weight and preterm birth in rural Gambia: a hospital-based study of 1579 deliveries. Open J Obstet Gynecol 2011; 1 (03) 94-103
  • 32 Beeckman K, Louckx F, Downe S, Putman K. The relationship between antenatal care and preterm birth: the importance of content of care. Eur J Public Health 2013; 23 (03) 366-371
  • 33 Patel PK, Pitre DS, Bhooker SP. Predictive value of various risk factors for preterm labor. Natl J Community Med 2015; 6 (01) 121-125
  • 34 Muchie KF, Lakew AM, Teshome DF. et al. Epidemiology of preterm birth in Ethiopia: systematic review and meta-analysis. BMC Pregnancy Childbirth 2020; 20 (01) 574
  • 35 Koire A, Chu DM, Aagaard K. Family history is a predictor of current preterm birth. Am J Obstet Gynecol MFM 2021; 3 (01) 100277
  • 36 Shah R, Mullany LC, Darmstadt GL. et al; ProjAHNMo Study Group in Bangladesh. Incidence and risk factors of preterm birth in a rural Bangladeshi cohort. BMC Pediatr 2014; 14 (01) 112
  • 37 Fuchs F, Monet B, Ducruet T, Chaillet N, Audibert F. Effect of maternal age on the risk of preterm birth: a large cohort study. PLoS One 2018; 13 (01) e0191002
  • 38 Soltani M, Tabatabaee HR, Saeidinejat S. et al. Assessing the risk factors before pregnancy of preterm births in Iran: a population-based case-control study. BMC Pregnancy Childbirth 2019; 19 (01) 57
  • 39 Wudie FT, Tesfamicheal FA, Fisseha HZ. et al. Determinants of preterm delivery in the central zone of Tigray, northern Ethoipia: a case-control study. S Afr J Child Health. 2019; 13 (03) 108-114

Address for correspondence

Huidrom Suraj Singh, PhD
Department of Anthropology, Manipur University
Canchipur, Imphal 795003
India   

Publication History

Article published online:
17 March 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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  • References

  • 1 Liu L, Oza S, Hogan D. et al. Global, regional, and national causes of under-5 mortality in 2000-15: an updated systematic analysis with implications for the sustainable development goals. Lancet 2016; 388 (10063): 3027-3035
  • 2 Lawn JE, Kerber K, Enweronu-Laryea C, Cousens S. 3.6 million neonatal deaths—what is progressing and what is not?. Semin Perinatol 2010; 34 (06) 371-386
  • 3 WHO Preterm Birth. “Born too soon,” e Global Action Report for Preterm Birth, MoD, PMNCH, Save the Children. New York, NY: WHO. retrieved on September 10. 4.56 p.m. Accessed September 10, 2012 at: http://www.who.int/news-room/fact-sheets/detail/preterm-birth 2012
  • 4 Kuppusamy N, Vidhyadevi A. Prevalence of preterm admissions and the risk factors of preterm labor in a rural medical college hospital. Int J Sci Stud 2016; 4 (09) 125-128
  • 5 Ahankari A, Bapat S, Myles P, Fogarty A, Tata L. Factors associated with preterm delivery and low birth weight: a study from rural Maharashtra, India. F1000 Res 2017; 6: 72
  • 6 India-profile for preterm birth and low birth weight prevention care [Internet]. [cited 2022 May 23]. Accessed January 12, 2023 at: https://www.healthynewbornnetwork.org/hnn-content/uploads/India-1.pdf
  • 7 Chawanpaiboon S, Vogel JP, Moller AB. et al. Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis. Lancet Glob Health 2019; 7 (01) e37-e46
  • 8 Devi TC, Singh HS. Prevalence and associated risk factors of preterm birth: a review. J Public Health Dent 2021; 19 (02) 209-226
  • 9 Salama E EIAS, Salama HS, Alobaidly SH. Socioeconomic risk factors for preterm birth in the state of Qatar: a population-based study. Acta Biomed 2021; 92 (03) e2021186
  • 10 Tellapagadra C, Eshwara VK, Bhat P. et al. Risk factors for PTB and low birth weight: a hospital-based prospective study. J Prev Med Public Health 2016; 49: 165-175
  • 11 Singh S, Malhotra AK. Across sectional study on prevalence of preterm birth in medical college Jhansi (U.P). Indian J Appl Res 2018; 8 (10) 50-52
  • 12 Dayanithi M. Low birth weight and premature births and their associated maternal factors. Int J Community Med Public Health 2018; 5 (06) 2277-2285
  • 13 Li H, Song L, Shen L. et al. Age at menarche and prevalence of preterm birth: results from the healthy baby cohort study. Sci Rep 2017; 7 (01) 12594
  • 14 Gogoi M, Prusty RK. Maternal anaemia, pregnancy complications and birth outcome: evidences from North-East India. J Near East Stud 2013; 3 (01) 74-85
  • 15 National Family Health Survey (NFHS) India. . 2015–16. International Institute for Population Science, Ministry of Health and Family Welfare, New Delhi, 2017
  • 16 Blencowe H, Cousens S, Oestergaard MZ. et al. National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications. Lancet 2012; 379 (9832): 2162-2172
  • 17 Feresu SA, Harlow SD, Welch K, Gillespie BW. Incidence of and socio-demographic risk factors for stillbirth, preterm birth and low birthweight among Zimbabwean women. Paediatr Perinat Epidemiol 2004; 18 (02) 154-163
  • 18 van den Broek NR, Jean-Baptiste R, Neilson JP. Factors associated with preterm, early preterm and late preterm birth in Malawi. PLoS One 2014; 9 (Suppl. 03) e90128
  • 19 Wagura P, Wasunna A, Laving A, Wamalwa D, Ng'ang'a P. Prevalence and factors associated with preterm birth at Kenyatta National Hospital. BMC Pregnancy Childbirth 2018; 18 (01) 107
  • 20 Soundarajan P, Muthuramu P, Veerapandi M, Marriapam R. Retrospective study factors related to preterm birth in Government Raja Mirasudar Hospital and obstetric and perinatal outcome. Int J Reprod Contracept Obstet Gynecol 2016; 5 (09) 3006-3010
  • 21 Trivedi P, Saxena D, Puwar T, Savaliya S, Ganguly P. A cohort study on risk factors for preterm births in rural Gujarat. Indian J Public Health 2018; 62 (02) 111-116
  • 22 Carcavalli L, Rocha IA, Valentim AF. et al. Difference of socioeconomic factors among mothers of preterm and full-term infant. Pesqui Bras Odontopediatria Clin Integr 2020; 20: e5005
  • 23 Acharya R, Khanal P, Bhattarai HK, Amatya A. Risk factors of preterm birth in Nepal: a hospital-based matched case-control study. Front Reprod Health 2021; 3: 697419
  • 24 Gurung A, Wrammert J, Sunny AK. et al. Incidence, risk factors and consequences of preterm birth—findings from a multi-centric observational study for 14 months in Nepal. Arch Public Health 2020; 78: 64
  • 25 Hidalgo-Lopezosa P, Jiménez-Ruz A, Carmona-Torres JM, Hidalgo-Maestre M, Rodríguez-Borrego MA, López-Soto PJ. Sociodemographic factors associated with preterm birth and low birth weight: a cross-sectional study. Women Birth 2019; 32 (06) e538-e543
  • 26 Di Renzo GC, Giardina I, Rosati A, Clerici G, Torricelli M, Petraglia F. Italian Preterm Network Study Group. Maternal risk factors for preterm birth: a country-based population analysis. Eur J Obstet Gynecol Reprod Biol 2011; 159 (02) 342-346
  • 27 Alijahan R, Hazrati S, Mirzarahimi M, Pourfarzi F, Hadi PA. Prevalence and risk factors associated with preterm birth in Ardabil, Iran. Iran J Reprod Med 2014; 12 (01) 47-56
  • 28 Krishna K. Tobacco chewing in pregnancy. Br J Obstet Gynaecol 1978; 85 (10) 726-728
  • 29 Suliankatchi RA, Sinha DN. The human cost of tobacco chewing among pregnant women in India: a systematic review and meta-analysis. J Obstet Gynaecol India 2016; 66 (Suppl. 01) 161-166
  • 30 Hennessy E, Alberman E. Intergenerational influences affecting birth outcome. II. Preterm delivery and gestational age in the children of the 1958 British birth cohort. Paediatr Perinat Epidemiol 1998; 12 (Suppl. 01) 61-75
  • 31 Jammeh A, Sundby J, Vangen S. Maternal and obstetric risk factors for low birth weight and preterm birth in rural Gambia: a hospital-based study of 1579 deliveries. Open J Obstet Gynecol 2011; 1 (03) 94-103
  • 32 Beeckman K, Louckx F, Downe S, Putman K. The relationship between antenatal care and preterm birth: the importance of content of care. Eur J Public Health 2013; 23 (03) 366-371
  • 33 Patel PK, Pitre DS, Bhooker SP. Predictive value of various risk factors for preterm labor. Natl J Community Med 2015; 6 (01) 121-125
  • 34 Muchie KF, Lakew AM, Teshome DF. et al. Epidemiology of preterm birth in Ethiopia: systematic review and meta-analysis. BMC Pregnancy Childbirth 2020; 20 (01) 574
  • 35 Koire A, Chu DM, Aagaard K. Family history is a predictor of current preterm birth. Am J Obstet Gynecol MFM 2021; 3 (01) 100277
  • 36 Shah R, Mullany LC, Darmstadt GL. et al; ProjAHNMo Study Group in Bangladesh. Incidence and risk factors of preterm birth in a rural Bangladeshi cohort. BMC Pediatr 2014; 14 (01) 112
  • 37 Fuchs F, Monet B, Ducruet T, Chaillet N, Audibert F. Effect of maternal age on the risk of preterm birth: a large cohort study. PLoS One 2018; 13 (01) e0191002
  • 38 Soltani M, Tabatabaee HR, Saeidinejat S. et al. Assessing the risk factors before pregnancy of preterm births in Iran: a population-based case-control study. BMC Pregnancy Childbirth 2019; 19 (01) 57
  • 39 Wudie FT, Tesfamicheal FA, Fisseha HZ. et al. Determinants of preterm delivery in the central zone of Tigray, northern Ethoipia: a case-control study. S Afr J Child Health. 2019; 13 (03) 108-114