Introduction:
Birth weight is one of the most important determinant of the neonatal and infant survival. Low birth weight (LBW) infants are approximately 20 times more likely to die than heavier babies.[1] Birth weight is a useful parameter in predicting the future growth and development of child. It can be used in identifying “at risk” families and help in decision making during the implementation of intervention programs especially in countries and regions with limited resources.[2] Low birth weight leads to an impaired growth of infant with its attendant risk factors of higher mortality rate, increased morbidity, impaired mental development and risk of chronic adult diseases.[3] Low birth weight babies are likely to start school late, dropout of school, complete fewer years of schooling, grow into stunted adult, and suffer from lower productivity and chronic diseases in later life.[4] All these result in substantial costs to the health sector and imposes a significant burden on the society as a whole.[5]
It is now well recognized that birth weight is not only a critical determinant of survival, growth and development, but also a valuable indicator of maternal health, nutrition and quality of life. Low birth weight (LBW) remains an unresolved important national concern in India. Twenty-nine percent of infant mortality rate is associated with LBW in India.[6] Three fourth of all neonatal deaths occur in LBW babies who are at 11-13 times higher risk of mortality during the neonatal period compared to normal birth weight babies.[7]
The goal of reducing low birth weight incidence by at least one third between 2000 and 2010 was one of the major goals in ‘A World Fit for Children’. The Declaration and Plan of Action was adopted at the United Nations General Assembly Special Session on Children in 2002. The reduction of low birth weight is also an important component of Millennium Development Goals (MDGs) for reducing child mortality. Low birth weight is therefore an important indicator for monitoring progress towards the internationally agreed goals.[4]
Birth weight of a newborn depends on the stay in utero, his intrauterine environment and effect of maternal factors. Low birth weight may indicate that baby did not remain in utero long enough or it did not develop enough.[8] The identification of factors contributing to low birth is therefore of considerable importance. Genetic factors, socio-demographic factors, obstetric factors, nutritional factors maternal morbidity during pregnancy, toxic exposures and antenatal care are all reported to influence the occurrence of LBW. The prevention of low birth weight is a public health priority, particularly in developing countries with high magnitude. Majority of studies focused on the maternal factors; there are very few studies which analysed the socio-demographic and socio-economic variables too. Independent effect of each of these factors is still debatable. Hence, the present study was carried out to study the magnitude and the correlates of low birth weight.
Methodology:
Two hundred and six newborn babies were recruited on a birth cohort from Anji (Mothi) and Kharangana (Gode) Primary Health Centres (PHC) of Wardha district to study growth in first year of life. Here, we present the baseline analysis of 172 children who were born full term to study the correlates of low birth weight babies born full term. The children were recruited within first week of their birth after obtaining written informed consent.
Data collection
Before conducting the study, prior intimation was given to Medical officer of PHC for sustained co-operation. Monthly meeting of PHC staff, ASHA and Anganwadi worker was utilised to get information about births in their area. Contact number of investigator was given to all PHC staff, ASHA and Anganwadi workers, so that investigator could get information of birth within eight days and parents could be contacted within eight days of delivery. Data on socio-demographic characteristics of the family was collected using a pre-designed interview schedule. Also information on ante-natal visits, IFA consumption was collected. Height and weight of the mother was measured. Complications during pregnancy and birth weight were noted from the hospital records. Information on smoking in the family was also collected.
Socio-demographic Profile
Enquiry was made about caste, family composition, colour of ration card and health insurance. Colour of ration card was considered as a proxy indicator of socio-economic status. Under Public Distribution System, (PDS) Government of Maharashtra had distributed colour coded ration cards to families depending on its socio-economic status. Yellow card signifies families below poverty line (BPL) and ration card of orange and white colour signifies family above poverty line (APL).
Birth History
All mothers were enquired about birth-order, antenatal check-up, gestational age and complication during pregnancy or child birth. Information was also collected on how many times mother received the ANC check-up. Gestational age was assessed by asking the last menstrual period of mother and date of child birth. Babies born before the end of 37 weeks gestation (less than 259 days) were considered preterm, those babies who were born from 37 completed weeks to less than 42 completed week (259-293 days) of gestation were considered full term and those babies born at 42 completed weeks or anytime thereafter (294 days & over) were considered post term.[9] Mother was also enquired regarding the birth weight of the child. Low birth weight was defined as birth weight less than 2500 grams.9 We noted the birth weight from hospital records.
Maternal characteristics
Information was collected on age, education and occupation of the mother. Information was also collected for cigarette smoking by family members. Nutritional status of mother was assessed by taking anthropometric measurement i.e. weight and height of mother.
Mother’s height: Standing height was measured to the nearest 0.1 cm using a flexible tape. The participant was asked to stand erect (as tall as one can) with occiput, buttocks and back of the heels touching the straight wall and Frankfort horizontal plane.[10]
Mother’s weight: Body weight was measured (to the nearest 0.5 kg) with the subject standing motionless on the bathroom weighing scale.[10] Zero error if present was removed everytime.
Body mass index (BMI) was calculated using formula BMI= Weight (in kilograms) / Height2 (in metres). Nutritional status of mother had been classified as thin (BMI<18.5 kg/m2); normal (BMI between18.5 to 22.9 kg/m2) and overweight (BMI=23.0 kg/m2).[11]
Statistical analysis: Proportion of low birth weight was expressed in percentage along with 95% confidence interval. Univariate and multivariate logistic regression was carried out to study the correlates of low birth weight. Low birth weight was taken as dependent variable while age of the mother, mother’s education, her occupation, caste category, family type, socio-economic status, health insurance, body mass index of mother, birth order, birth interval, antenatal care, consumption of IFA and complications during pregnancy were taken as independent variables. Associations were expressed in terms of odds ratio and its 95% confidence interval. Multivariate logistic regression was carried out using backward LR method.
Results:
Majority of the mothers (94.7%) were below thirty years of age and were involved in household work. 58.7% mothers were at least 10th standard educated. Two-third of them were residing in joint family and about one-fifth belonged to BPL families. More than half had health insurance coverage. On first visit after the child birth which was within eight days of the delivery, 12% were thin while 20% were overweight.
Proportion of low birth weight (LBW) was found to be 33.1% (95% CI: 26.4%-40.4%) among those who were born full term.
On univariate analysis, mother’s age more than 30 years did not increase the odds of LBW as compared to mothers less than 30 years of age (OR =1.009; 95% CI: 0.243-4.191). No significant increase in odds of LBW was observed for mother’s education and occupation. When OBC and SC/ST caste categories were compared with open category again the odds of LBW did not increase significantly. Odds of LBW were higher among children born in nuclear family as compared to joint family and in children born in below poverty level family as compared to above poverty line family. But the association was not statistically significant. Similarly odds of LBW did not change significantly with status of health insurance, history of smoking in the family, birth order, birth interval, number of antenatal visits. Higher odds of LBW was found in children born to thin mothers as compared to overweight mothers and the association was found to be statistically significant. Similarly mothers who consumed less than 50 IFA tablets significantly higher odds of delivering LBW baby as compared to mothers who consumed 100 or more IFA tablets (OR=3.092) and the association was statistically significant. Odds ratio of 2 was observed for LBW babies among mothers who had complications during pregnancy when compared to those mothers who did not have the complications. But the association was found to be statistically significant. Higher odds of LBW were found among male children as compared to female children but the association sex with LBW was not statistically significant. (Table 1)
| Table 1: Correlates of low birth weight: univariate analysis |
| Variable |
Total numbers (N=172) |
Number of low birth weight babies (%) |
Odds ratio (95% CI) |
Overall |
172 |
57 (33.1)95% CI: 26.4% - 40.4% |
- |
Mother’s age |
< 30 years |
163 (94.7) |
54 (33.1) |
1 |
> 30 years |
9 (5.3) |
3 (33.3) |
1.009 (0.243-4.191) |
Mother’s education |
< 10 standard |
71 (41.3) |
22 (31.0) |
0.847 (0.443 – 1.620) |
> 10 standard |
101 (58.7) |
35 (34.7) |
1 |
Mother’s occupation |
Household work |
163 (94.7) |
54 (33.1) |
1.009 (0.243-4.191) |
Service/business |
9 (5.3) |
3 (33.3) |
1 |
Caste category |
General |
7 (4.1) |
3 (42.9) |
1 |
OBC |
90 (52.3) |
26 (28.9) |
0.542 (0.113 – 2.590) |
SC/ST/NT/VJ |
75 (43.6) |
28 (37.3) |
0.797 (0.154 – 4.547) |
Type of family |
Nuclear |
54 (31.4) |
23 (42.6) |
1.833 (0.937 – 3.584) |
Joint |
118 (68.6) |
34 (28.8) |
1 |
Socio-economic status |
Above poverty level |
135 (78.5) |
42 (31.1) |
1 |
Below poverty level |
37 (21.5) |
15 (40.5) |
1.510 (0.713-3.198) |
Health insurance |
Yes |
102 (59.3) |
37 (36.3) |
1 |
No |
70 (40.7) |
20 (28.6) |
0.703 (0.364-1.356) |
Body Mass Index of mother |
Thin |
21 (12.2) |
11 (52.4) |
4.400 (1.337 – 14.483) |
Normal |
116 (67.4) |
39 (33.6) |
2.026 (0.813 – 5.051) |
Overweight |
35 (20.4) |
7 (20.0) |
1 |
Smoking in the family |
Yes |
9 (5.3) |
5 (55.6) |
2.668 (0.688 – 10.348) |
No |
163 (94.7) |
52 (31.9) |
1 |
Birth order |
First |
96 (55.8) |
34 (35.4) |
1 |
Second and more |
76 (44.2) |
23 (30.3) |
0.791 (0.416-1.506) |
Birth interval* (rest of the children were first born) |
< 36 months |
52 (68.4) |
19 (36.5) |
1 |
> 36 months |
24 (31.6) |
4 (16.7) |
0.347 (0.103-1.168) |
Antenatal care |
< 4 visits |
11 (6.4) |
6 (54.5) |
2.588 (0.755 – 8.876) |
> 4 visits |
161 (93.6) |
51 (31.7) |
1 |
IFA consumption |
< 50 tablets |
38 (22.1) |
20 (52.6) |
3.092 (1.396 – 6.849) |
50-99 tablets |
47 (27.3) |
14 (29.8) |
1.181 (0.538 – 2.591) |
> 100 tablets |
87 (50.6) |
23 (26.4) |
1 |
Complications during pregnancy |
Yes |
47 (27.3) |
21 (44.7) |
1.997 (0.998 – 3.994) |
No |
125 (72.7) |
36 (28.8) |
1 |
Sex of the child |
Male |
89 (51.7) |
24 (27.0) |
1 |
Female |
83 (48.3) |
33 (39.8) |
1.788 (0.940-3.397) |
Final model derived by multivariate logistic regression suggested that the significant correlates of LBW are female sex of the child (OR= 2.856), thin mother (OR=5.320), mother consuming less than 50 IFA tablets (OR= 4.648), mother who had any complications of the pregnancy (OR= 2.917). Negelkerke’s R-square for the model was 0.225. (Table 2)
| Table 2: Correlates of low birth weight: final model using multiple logistic regression by backward LR method |
| Variable |
Odds ratio |
95% CI |
p-value |
Female sex of the child |
2.856 |
1.328 - 6.142 |
0.007 |
Thin mother |
5.320 |
1.435 - 19.722 |
0.012 |
Consumption of IFA tablets <50 |
4.648 |
1.876 - 11.517 |
0.001 |
Complications of pregnancy |
2.917 |
1.312 – 6.484 |
0.009 |
Discussion:
Low birth weight is a major public health problem in many developing countries including India. The last half century has witnessed many changes in the reproductive habits of population, the technologies and management of childbirth. However, during the last three decades there had hardly been change in incidence of LBW in India. Magnitude of 33% among full term reemphasises that India experiences one of the highest LBW rates in the world.
In the present study, female sex of baby, thinness of mothers (Body Mass Index (BMI) less than 18.5 Kg/m2), consumption of IFA tablets less than 50 and complications during pregnancy were found to have significant higher odds for low birth weight. Out of these, thinness and less consumption of IFA tablets are preventable factors. Also, the complications during pregnancy can be prevented to some extent. Hence, the focus of intervention shall be on improving maternal nutritional status and promoting consumption of IFA tablets.
In regards with sex of infant, present study revealed 3 times higher odds of being LBW among female children than that of male children. This finding was statistically significant. Kramer in his meta-analysis on determinants of low birth weight had observed sex of infant as an important risk factor and its causal effect was established.1 Similar finding have been reported by various researchers.[12-15] However, few studies did not report any association between low birth weight and infant sex.[16-18]
Most of the studies revealed that maternal anthropometry contributes significantly to low birth weight.[4,12,17] In line of literature, mother’s nutritional status is an important factor in the determination of low birth weight among Indian infants. Malnourished mothers gave birth to higher proportion of low birth weight. In the present study we also found 5 times higher odds of being low birth weight if the mother is thin as compared to overweight mother. Similar significant association was reported by several studies.[4,13,19,20] However, in contrast to this finding, Ojha did not find significant difference between BMI and low birth weight.[21]
Anaemia is prevalent in India especially among pregnant mothers. Government of India has IFA supplementation program to reduce the anaemia and prevent adverse pregnancy outcomes. In present study, 4 times higher odds of LBW was found among those who consumed less than 50 tablets as compared to those consumed at least 100 tablets of IFA. This finding is consistent with other researchers.[12,14,22] Ghosh et al found the incidence of low birth weight babies among non-anemic and mild to moderately anemic mothers was about 20% compared to 29% among the severely anemic mothers (Hb<6 gm%).[12] Hivre found that low birth weight babies significantly more likely to be born to mothers whose hemoglobin was less than 9 gm/dl (RR:1.53).[14] Deshmukh in urban area of Nagpur, identified maternal anemia had significant, four times risk of low birth weight than non anemic (OR:4.81).[23] Anand et al from rural Wardha and Mavalankar from Ahmedabad found presence of anemia during pregnancy was significantly associated with LBW and SGA repectively.[22,24] Similarly in a study Rizvi found that mothers who did not take iron supplements during pregnancy had increased odds of having an LBW baby (OR:2.88; 95% CI:1.83-4.54; p<0.001).[25]
In present study, any morbidity during pregnancy was 3 times higher odds of LBW as compared those who did not have any morbidity during pregnancy. Other researchers from various studies also showed significant association between morbidity during pregnancy and LBW.[13,19,26,27]
Kramer in his meta-analysis on determinants of low birth weight had observed low maternal age as an important risk factor and its causal effect was established.1 Similar findings have been observed by various studies.[14,23,28] But, in the present study we did not find any significant association of age with LBW. This could be attributed to less number of mothers at extremes of age as more than 94% of them were between 20-30 years of age.
In the present study, mothers’ education was not found to be significantly associated with low birth weight of babies. Amin N, Malik S et al and Radhakrishnan did not find any association between educational status of mothers and risk of delivering low birth weight babies.[18,20,29] However, Kramer in his meta-analysis on determinants of low birth weight found maternal education as an important risk factor.[1] Mavalankar also reported low level of maternal education to be significantly associated with increased odds of low birth weight.[22] Similar finding have been found by various others researchers in their studies.[12,13,23,24,29]
In the present study, association of occupation of mothers was not found statistically significant for low birth weight babies. Siza JE and Roudbari M also did not find significant association between occupation and low birth weight.[30,31] However, other researchers from various studies found significant association between occupation and low birth weight.[24,32,33]
In present study, caste category was not significantly associated with low birth weight. This finding was similar to other studies.[16,17] Amin N found percentage of scheduled caste mother who delivered LBW babies was 47.1% which was less when compared to the mothers of other caste who delivered LBW (54.4%).[20] Similarly Joshi HS and Nair NS showed incidence of low birth weight was similar in different caste.[19,32] However, Nair NS found incidence of low birth weight was significantly higher among SC/STs when compared to Hindus.[32]
In present study, though 2 times higher odds of being LBW was found among babies born in nuclear families as compared to those born in joint families, the association was not statistically significant. In contrast to this, Vijayalaxmi in her study conducted in urban area of Bangalore found that majority of women who delivered low birth weight babies were living in joint families (54.0%).[34]
Kramer in his meta-analysis on determinants of low birth weight had observed socioeconomic status as an important risk factor.[1] But, in the present study, the association was not statistically significant. Ghosh et al reported that the incidence of babies with birth weight <2500 gm was almost the same in low (less than Rs 50 per capita income) and middle income groups (Rs 51-200 per capita income) but significantly lower in high income groups (Rs 201 and more per capita income).[12] Similarly other studies found that risk of low birth weight was significantly higher for lower socioeconomic status.[14,19,24]
In the present study, birth order was not associated significantly with low birth weight. Similarly George et al reported that parity had no direct effect on birth weight of the newborn.[35] Various researchers found association between primiparity and LBW.[12,16,23,32] Contrast to this Joshi HS found, an increase in LBW was after fourth parity.[19] Kramer in his meta-analysis on determinants of low birth weight had observed parity as an important risk factor and its causal effect was established.[1]
In present study, birth interval of 36 months or more had lesser odds of LBW as compared to interval of less than 36 months but the association was not statistically significant. However, researchers from various studies shown significant association between birth spacing.[12,13,23] Kramer in his meta-analysis on determinants of low birth weight had observed birth spacing as an important risk factor but its causal effect was uncertain.[1]
In present study, though the mothers who did visit antenatal clinic less than 4 recommended visits had 2.5 times higher odds of having LBW baby as compared to those who paid recommended visits but the association was not statistically significant. However, in case control study in city of Natal, North-East Brazil, observed that the crude risk of both outcome i.e. preterm and IUGR, increased among mothers with inadequate (<5 visit) antenatal care (AR;11.6%).[36] Significant findings were reported by other studies.[4,19,24,32] The difference may be attributed to high coverage of adequate antenatal care in the study area. Kramer in his meta-analysis observed that antenatal care, was potentially important risk factor but casual effect was uncertain.[1]
To conclude, female sex of the child, thin mother, consuming less than 50 IFA tablets and complications during pregnancy were important correlates of low birth weight of the baby. Effort to improve nutrition of mother, promoting consumption of IFA tablets as per recommendation and early detection and prompt management of complications during pregnancy may bring down the problem of low birth weight.
References:
- Kramer MS. Determinants of Low Birth Weight: Methodological assessment and meta-analysis. Bull World Health Organ. 1987;65(5): 663-737.
- Kumar V, Datta N. Birth weight as an indicator of health. Indian Pediatr. 1984 Feb;21(2):113-8.
- Muthayya S. Maternal nutrition of and low birth weight- what is really important? Indian J Med Res 2009 Nov;130(5): 600-8
- Dharmalingam A, Navaneetham K, Krishkumar CS. Nutritional Status of Mothers and Low Birth Weight in India. Matern Child Health J 2010 Mar;14(2):290-8
- World Health Organization. Development of a strategy towards promoting optimal fetal growth. 2005. Available from: URL: http://www.who.int/nutrition/topics/lbw_strategy _background.pdf
- United Nations Children’s Fund and World Health Organization. Low Birth weight: Country, regional and global estimates. New York (NY) UNICEF. 2004. Available from URL: http://www.childinfo.org/files/low_birthweight_from_EY.pdf
- Chaudhari S, Kulkarni S, Pandit A, Desmukh S. Mortality and morbidity in high risk infants during six year follow up. Indian Pediatr 2000 Dec;37(12):1314-20
- Ghosh S, Bhargava SK, Madhavan S, Taskar AD, Bhargava V, Nigam SK. Intra-uterine growth of north Indian babies. Pediatrics 1971 May;47(5):826-30.
- Park K. Park's Text Book of Preventive & social Medicine. Jabalpur: Banarsidas Bhanot; 2013.
- Luepker RV, Evans A, McKeigue P, Reddy KS. Cardiovascular survey methods. 3rd ed. Geneva: World Health Organization; 2004.
- Misra A, Chowbey P, Makkar BM, Vikram NK, Wasir JS, Chadha D, et al. Consensus statement for diagnosis of obesity, abdominal obesity and the metabolic syndrome for Asian Indians and recommendations for physical activity, medical and surgical management. J Assoc Physicians India. 2009;57:163-70.
- Ghosh S, Hooja V, Mittal SK, Verma RK. Bio-social determinants of birth weight. Indian Pediatr 1977 Feb;14(2):107-14.
- Rafati S, Borna H, Akhavirad MB, Fallah N. Maternal determinants of giving birth to low birth weight neonates. Arch Iran Med 2005;8(4):277-81.
- Hivre SS, Gantra BR. Determinants of low birth weight: a community based prospective cohort study. Indian Pediatrics 1994 Oct;31(10):1221-5.
- Mondal B. Risk factors for low birth weight in Nepali infants. Indian J Pediatr 2000 July;67(7):477-82.
- Shrivastava BC, Mishra VN, Singh R. A study of low birth weight infants in a teaching hospital. Indian J Pediatr 1976 Jan;43(336):5-9.
- Dhall K, Bagga R. Maternal determinants of birth weight of North Indian babies. Indian J Pediatr 1995 May-June;62(3):333-44.
- RadhaKrishnan T, Thankappan KR, Vasan RS, Sharma PS. Socio-economic and demographic factors associated with birth weight. A community based study in Kerela. Indian Pediatr 2000;37:872-95.
- Joshi HS, Subba SH, Dabral SB, Dwivedi S, Kumar D, Singh S. Risk factors associated with low birth weight in newborns. Indian J Community Med 2005;30(4):142-3.
- Amin N, Abel R, Sapmathkumar V. Maternal risk factors associated with low birth weight. Indian J Pediatr 1993;60(2):269-74.
- Ojha N, Malla DS. Low birth weight at term: relationship with maternal anthropometry. J Nepal Med Assoc 2007 Apr-Jun;46(166):52-6.
- Mavalankar DV, Gray RH, Trivedi CR. Risk factors for preterm and term low birth weight in Ahmedabad, India. Int J Epidemiol 1992 Apr;21(2):263-72.
- Deshmukh JS, Motghare DD, Zodpey SP, Wadhav SK. Low birth weight and social associated maternal factors in urban area. Indian Pediatr 1998 Jan;35(1):33-6.
- Anand K, Garg BS. A study of factors affecting low birth weight. Indian J Community Med 2000;25(2):57-62.
- Rizvi SA, Hatcher J, Jehan I, Qureshi R. Maternal risk factors associated with low birth weight in Karachi. East Mediterr Health J 2007 Nov-Dec;13(6):1343-52
- Idris MZ, Gupta A, Mohan U, Shrivastava AK, Das V. Maternal health and low birth weight among institutional deliveries. Indian J Community Med 2000;25(4):156-60.
- Nagraj K. Determinants of Low Birth Weight. A case control study in Tirupati, India. The Ind Pract 2003 April;56(4):226-30.
- Bhargava SK, Ghosh S, Lall UB. A study of low birth weight infants in an urban community. Health Popul Perspect Issu 1979 Jan-Mar;2(1):54-66.
- Malik S, Ghidhiyal RG, Udani R, Waingankar P. Maternal biosocial factors affecting low birth weight. Indian J Pediatr 1997 May-June;64(3):373-7.
- Siza JE. Risk factors associated with low birth weight of neonates among pregnant women attending a referral hospital in Northern Tanzania. Tanzan J Health Res 2008 Jan;10(1):1-8.
- Roudbari M. Yaghmari M, Sochili M. Prevalence and risk factors of low birth weight infants in Zahanden, Islamic Republic of Iran. East Mediterr Health J 2007 July-Aug;13(4):834-45.
- Nair NS, Rao RSP, Chandrashekar S, Das A, Bhatt HV. Socio-demographic and maternal determinants of low birth weight: A multivariate approach. Indian J Pediatr 2000 Jan;67(1):9-14.
- Ahmed P, Jaakkola JJ. Maternal occupation and adverse pregnancy outcomes: A Finnish population-based study. Occup Med (Lond) 2007 Sep;57(6):417-23.
- Vijayalaxmi KG, Urooj A. Influence of maternal factors on mode of delivery and birth weight in urban pregnant women. J Hum Ecol 2009;25(2):133-6.
- George KA, Sreedevi R, Kumar NS, Sharma PS. A study on the influence of maternal factors on birth weight. Indian J Nutr Diet 2003;40:291-6
- Ferraz ME, Gray RH, Cunha TM. Determinants of preterm delivery and intrauterine growth retardation in North-East Brazil. Int J Epidemiol. 1990;19(1):101-8.
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