|Year : 2018 | Volume
| Issue : 3 | Page : 129-135
Maternal predictors of low birth weights: Audit of data of delivery room register at a Tertiary Health Center of Sub-Himalayan Region
Manoj Kumar Gandhi1, Mitasha Singh2, Piyush Sharma1, Ankush Kaushal1, Chander Deep Sharma3, Sunil Kumar Raina1
1 Department of Community Medicine, Dr. Rajendra Prasad Government Medical College, Kangra at Tanda, Himachal Pradesh, India
2 Department of Community Medicine, ESIC Medical College and Hospital, Faridabad, Haryana, India
3 Department of Obstetrics and Gynecology, Dr. Rajendra Prasad Government Medical College, Kangra at Tanda, Himachal Pradesh, India
|Date of Web Publication||28-Jun-2019|
Dr. Mitasha Singh
Department of Community Medicine, ESIC Medical College and Hospital, Faridabad, Haryana
Source of Support: None, Conflict of Interest: None
Background: Birth weight is an indicator of social and economic development of any country. It predicts the status of women of country and the resources invested in them so that they deliver healthy child. The proportion of low birth weight (LBW) has reduced in India in the past few decades; however, we have still a long way to go. Objective: We aimed to conduct an audit of retrospective data from the records of labor room of a Tertiary Care Center of Dr. Rajendra Prasad Government Medical College, Kangra at Tanda, Himachal Pradesh. Methodology: A retrospective data mining of records of the labor room wards (delivery rooms) was conducted from January to June 2016 at Tertiary Care Center of Dr. Rajendra Prasad Government Medical College, Kangra at Tanda, Himachal Pradesh. LBW was our outcome of interest. Results: Out of the total 3738 deliveries conducted, 1251 (33.46%) of babies born were LBW, and there were 100 intrauterine deaths and 12 stillbirths with a total of 112 (2.9%) deaths. Age, height of mother, backward caste, history of pregnancy-induced hypertension, preterm delivery, one living child of mother, multiple fetuses, and cesarean section in current pregnancy were significant predictors after controlling for confounders. Conclusion: The higher proportion of LBW in tertiary center as compared to state-level data shows that secondary prevention in the form of early identification of high-risk cases, and timely referral to higher centers is functioning well in this part of the country. However, the community-based primary prevention efforts are still given a low preference.
Keywords: Birth weight, labor room, tertiary center
|How to cite this article:|
Gandhi MK, Singh M, Sharma P, Kaushal A, Sharma CD, Raina SK. Maternal predictors of low birth weights: Audit of data of delivery room register at a Tertiary Health Center of Sub-Himalayan Region. J Sci Soc 2018;45:129-35
|How to cite this URL:|
Gandhi MK, Singh M, Sharma P, Kaushal A, Sharma CD, Raina SK. Maternal predictors of low birth weights: Audit of data of delivery room register at a Tertiary Health Center of Sub-Himalayan Region. J Sci Soc [serial online] 2018 [cited 2019 Jul 20];45:129-35. Available from: http://www.jscisociety.com/text.asp?2018/45/3/129/261660
| Introduction|| |
The prevalence of underweight among children aged less than 5 years is reduced by 10.2% in last 10 years that is from 45.9% as reported by National Family Health Survey (NFHS)-3 (2005–2006) to 35.7% in NFHS-4 (2015–2016). On the other side, the prevalence of overweight and obesity among adult males and females increased by 9.3% and 8.1%, respectively, in the same one decade., Undernutrition in early years of life and overweight and obesity in later ages of life are proven risk factors for noncommunicable diseases (NCDs) such as hypertension, diabetes, and chronic heart diseases. The intergenerational cycle of the risk of developing NCDs starts from a premature or low-birth weight (LBW) neonate. This may continue as undernutrition among women and girls which in turn has negative maternal health outcomes and LBW of the offspring, and thus passes on the burden of undernutrition to the next generation. On the other hand, the increasing burden of NCDs in India adds to the evidence of the link between prematurity and LBW and NCDs. The other challenge our country has to face because of a relatively higher proportion of LBW is the high cost of special care and intensive care units which further impair the socioeconomic development.
In 2011, the Indian Statistical Institute reported nearly 20% of newborn having LBW in India. District Level Health Survey (DLHS)-4 (2012–2013) in Himachal Pradesh reported the prevalence of LBW as 13.8%. Owing to the multifactorial causation of LBW including social factors, environmental factors, nutrition-related factors, pregnancy-related factors, and comorbidities, we need to focus on few impending causes which can be intervened.,
We have witnessed improvement in institutional delivery, which is 77.8% in Himachal Pradesh against the target of 100.0%, though majority of deliveries occurred at tertiary/secondary than at the primary facility. Almost, all of the high-risk antenatal cases are advised skilled care. The high-risk pregnant cases prefer tertiary centers to deliver. In our country, the institutional deliveries, which are nonhigh-risk antenatal cases, have also been observed to prefer the secondary or tertiary level of care for the delivery of their newborn. Hence, we aimed to conduct an audit of retrospective data from the records of labor room of a tertiary care center of Dr. Rajendra Prasad Government Medical College, Kangra at Tanda, Himachal Pradesh.
| Methodology|| |
A retrospective data mining of 6 months (January–June 2016) was conducted at the labor room wards of a Tertiary Care Center of Dr. Rajendra Prasad Government Medical College, Kangra at Tanda, Himachal Pradesh. Records of all pregnant women admitted to the Obstetrics Department and further shifted to labor room ward nearing delivery were retrieved from labor room register.
District Kangra is situated in the Southern escarpment of Himalayas. The altitude varies from 500 meters above mean sea level (AMSL) to around 5000 m AMSL. It is encapsulated in the North by district Chamba and Lahaul Spiti, in the South by Hamirpur and Una, and the East by Mandi and in the West by Gurdaspur district of Punjab. Kangra district is situated between 31°2–32°5 N and 75°–77°45 E. According to DLHS-4 (2011–2012), in Himachal Pradesh, 77.8% of deliveries were institutional of which 66% were conducted at government institutions. It is expected that patients from subcenters are referred to primary health centers (PHCs) and from PHCs to community health centers (CHCs) and from CHCs to district hospital and district to tertiary level of care. The Laissez-Faire approach toward both free choice of provider and free access to secondary and tertiary levels of care directly increases the load of patients at secondary and tertiary levels.
Data collection procedure
Ethical approval and due permission were obtained from the Institutional Ethics Committee. Prior formal administrative approval was sought from the concerned authorities and medical records department of the institute. The screening of records was done by a research fellow of the institute under the supervision of a resident from the Department of Community Medicine with the help of personnel of medical records department. The recorded data included demographic details of the pregnant woman, anthropometric profile, comorbidities, and outcome regarding newborn delivered. A brief history pertaining to maternal details of previous delivery was also recorded. The final listing of cases was done by a resident of community medicine, also a coinvestigator.
Following operational definitions were used in the following analysis:
The LBW has been defined as weight at birth of <2500 g (5.5 pounds). Very LBW is <1500 g (up to and including 1499 g). Extremely LBW is <1000 g (up to and including 999 g). Preterm birth is defined as birth at gestational age <37 weeks.
The levels of hemoglobin (Hb) used for the classification of anemia in pregnant women as mild, moderate, and severe anemia were those recommended by the Indian Council of Medical Research. Mild, moderate, and severe anemia was defined as follows: mild anemia: Hb 10.0 mg/dl–10.9 mg/dl; moderate anemia: Hb 7.0 mg/dl–10.0 mg/dl; severe anemia: Hb <7 mg/dl; and very severe anemia: Hb <4 mg/dl.
Stillbirth was defined as fetus delivered with no signs of life at or after 28 weeks of gestation.
Intrauterine fetal death is referred to fetus with no signs of life in utero.
A cutoff point of 145 cm was used to define short stature as per the NFHS-3.
The caste of woman was classified into “scheduled caste,” “scheduled tribe,” “other backward class,” and “none (general caste).”
This classification of caste focuses more on the socially disadvantaged castes, and all privileged caste groups are coded in the “general caste” group.
Data and statistical analysis
Data were entered in Microsoft Excel sheet (2003–2007). Subsequently, data cleaning and editing were carried out. The different parameters were computed as proportion and mean. All the parameters were computed with weight group of delivered neonates. Chi-square and t-test were used to test differences between categorical and continuous variables in the bivariate analysis. The parameters in the bivariate analysis which demonstrated P < 0.10 were included in multinomial regression model. Statistical analysis was done using SPSS version 21 (IBM Corp., Armonk, NY, USA). Statistical significance for the association was considered at P < 0.05.
| Results|| |
Out of the total 3738 deliveries conducted, 1251 (33.46%) of babies born were LBW, and there were 100 intrauterine deaths and 12 stillbirths with a total of 112 (2.9%) of deaths. [Table 1] depicts that about 50% of babies born to mother with age of ≤20 years were having LBW and 50% were having normal birth weight. The proportion of LBW decreased significantly as the age of mother increased. The females who belonged to other backward castes delivered a higher proportion of LBW babies (37.8%) as compared to scheduled castes/tribe and general caste [Table 1].
|Table 1: Demographic factors associated with low birth weight delivering at tertiary care center of Himachal Pradesh|
Click here to view
The primigravida mothers delivered more than one-third of LBW (36.6%). Mean height (155.53 ± 6.56 cm) and weight (56.20 ± 7.41) of mothers having LBW babies were significantly lower as compared to normal weight babies (P < 0.001). Previous lower segment cesarean section (LSCS) was protective for birth weight of babies as 23.3% of babies born to mothers with previous LSCS were LBW as compared to 34.7% of mothers who did not have previous LSCS (P < 0.0001). The proportion of LBW was significantly higher among mothers with pregnancy-induced hypertension (PIH) (44.6%) (P = 0.001). Diabetic mothers delivered a significantly lower proportion (12.5%) LBW babies (P = 0.029). Majority of preterm babies (80.7%) were LBW. History of previous neonatal death reported 36.7% LBW (P = 0.649) [Table 2].
|Table 2: Antenatal parameters associated with low birth weight delivering at tertiary care center of Himachal Pradesh|
Click here to view
The proportion of LBW was highest (50%) among mothers who had severe anemia at the time of delivery (<7 g/dl), as compared to those with moderate-to-mild anemia. It was seen that with an increase in Hb, there was a decrease in the proportion of LBW babies [Table 3].
|Table 3: Peripartum parameters associated with low birth weight delivering at tertiary care center of Himachal Pradesh|
Click here to view
Those with meconium-stained amniotic fluid resulted in 34.5% of LBW as compared to 33.2% among those with clear liquor (P = 0.668).
The independent variables in bivariate analysis with P < 0.10 were subjected to multinomial regression analysis to identify independent maternal predictors of LBW. Age, height of mother, backward caste, history of PIH, preterm delivery, one living child of mother, multiple fetuses, and cesarean section in current pregnancy were significant predictors even after controlling for confounders. Hb, diabetes, gravidity of mother, previous LSCS, and fetal position were significant risk factors in unadjusted analysis. However, in the adjusted model, influences of these factors greatly attenuated and were statistically insignificant. The risk of LBW was twice high among short stature women as compared to women with height >145 centimeters [Table 4].
|Table 4: Multivariate analysis predicting maternal factors responsible for low birth weight|
Click here to view
| Discussion|| |
The prevalence of term LBW in this study was 33.46%, comparable to a study done in Central India with a prevalence of 33%, but much higher as compared to studies from Pakistan (10.6%) and Ethiopia (10%)., According to DLHS-4, among all the deliveries sampled from the state, 73.5% of babies were weighed at birth, out of which 13.8% were LBW. The state healthcare is dominated by government health centers and tertiary centers in region are most sought after by the local population and neighboring district or state population too. The high proportion of LBW in our analysis as compared to state-level data represents the deliveries which are referred or are at high-risk pregnancies. However, this high proportion is an alarming figure and needs to be compared with data of other regions of the state for a focused intervention at the peripheral level.
In the current analysis of data of tertiary care center, the age, height of mother, caste of mother, history of PIH, preterm birth, one living child, multiple fetuses, and cesarean section in current pregnancy emerged as independent predictors. Nahum et al. in a prospective hospital-based study reported that the significant predictors of birth weight are maternal height, gestational age, parity, third-trimester maternal weight gain rate, and fetal gender. Anitha et al., in their analysis presented the biologically acceptable predictors under following heads: maternal height and Hb (anthropometric variable), parity and maternal age at pregnancy (biosocial variable), gestational age, PIH, and history of LBW in the previous pregnancy (obstetric variables). We divided the predictors identified in our analysis under the same heads recommended by Anitha et al.
Maternal anthropometry and nutrition status: Maternal height and hemoglobin
The risk estimates for having an infant with LBW has been observed to be significantly elevated for women with short stature (height <145 cm) in an analysis of DLHS-3 data. Our analysis reveals comparable findings. Height of a mother is an outcome of several factors including nutrition during her childhood and adolescence. This demands a lifecycle approach to maintain nutrition of a girl child through her childhood to adolescence and the childbearing ages so that the next generation of female child borne should be nutritionally sound enough to continue the reproductive cycle in healthy manner.
Lower concentration of maternal hemoglobin is one of the risk factors for LBW among children, as it limits maternal oxygen uptake, decreases oxygen delivery to the fetus, and consequently leads to fetal growth restriction., Studies have reported that anemic mothers with hemoglobin level <11 g/dl have higher chances of giving birth to LBW babies.,, In an analysis of NFHS-3 data to assess the determinants of LBW in country by Kader and Perera, 34% significantly higher risk of LBW was observed among moderate-to-severe anemic mothers. The trend of increasing proportion of LBW with decreasing Hb was also observed in the current study, however, after adjusting for other confounders maternal Hb did not show a statistically significant association.
Social status of family: Caste
According to the caste system division in India, females belonging to other backward classes (OBC) had 75.7% significantly higher risk of LBW babies as compared to general caste. This was in contrast to a case–control study from Nepal where lower caste was protective for LBW. The analysis of NFHS-3 data revealed no association of OBC with LBW instead scheduled castes had 23% higher risk. This could be due to the relatively higher proportion of females belonging to OBC attended the tertiary center as compared to SC/STs.
Biosocial status: Parity of mother and maternal age
Many studies have shown the age of the mother to be significantly associated with the birth of LBW babies which is evident in our analysis too. It is evident from the previous studies and the current study that younger mothers are more likely to have LBW babies.,,,,,,,, Anitha et al., reported that nulliparous women were more likely to have an LBW baby as compared to multiparous women. Our analysis demonstrated that women with first pregnancy were at lower risk. Mumbare et al., Mavalankar et al., Fikree and Berende in Pakistan and Acharya et al. did not identify maternal age and parity as significant risk factors for LBW babies. The variation in the effect of biosocial factor among studies from different regions of country is due to difference in age of marriages in different communities, and the effect of social status on nutritional status of female which in turn leads to higher risk of LBW as number of pregnancies increase. Furthermore, it has been observed that the first pregnancy is given more importance socially; hence, the female is generally protected from malnutrition and anemia which in turn protects the fetus.
Premature delivery and pregnancy-induced hypertension
Premature delivery is a known predictor as mentioned by other studies from Nepal and Ahmedabad., Anitha et al. also reported that the increase in gestational age contributes to increase in birth weight of the baby. This indicates that mothers with a history of premature delivery may need special care during the antenatal period and highlights the role of our forefront workers. History of PIH was associated with twice higher risk of LBW babies, and similar findings were reported in another cross-sectional study from a tertiary care center of Thiruvananthapuram who reported a decrease of 94 g in the birth weight associated with PIH history.
Fetal presentation in utero, route of delivery
Cephalic and breech presentation of fetus had lower risk of LBW as compared to other positions of fetus. Multiple fetuses had significantly higher chance of LBW as compared to single fetus. Vaginal route of delivery had significant (70%) higher chance of LBW, and this is reduced in assisted vaginal route as compared to cesarean section. This is self-explanatory; as the weight of baby increases the assisted technique has to be used to deliver the fetus.
The health-care systems in India have been divided into three tiers of primary, secondary, and tertiary in the form of a pyramid with tertiary at apex and primary forming the larger part of base. Those on demand side have faith in hospital-based care or curative approach rather than preventive approach and thus demotivate the service providers leading to low-quality preventive services. This is the reason we witness a load of LBW s in tertiary centers as the focus of preventive aspect is shifted to diagnosis and treatment of a condition rather than its prevention.
The data obtained from records of tertiary center give us the scenario of tip of iceberg and limit the ability to understand the pattern and associations at peripheral level. The findings of this analysis cannot be generalized among all the settings of the country as it was a single-centric study.
The social status of any family is difficult to change; hence, the focus of intervention should be the nutrition status of women and the number of pregnancies. Secondary prevention in the form of early identification of high-risk cases and timely referral to higher centers is the next step which is functioning well in this part of the country. This is evident from the much higher proportion of LBW in tertiary center as compared to state-level data.
| Conclusion|| |
In the current audit of data of women delivered at tertiary care center of a district situated in the sub-Himalayan region, one-third of the deliveries resulted in LBW. The biosocial status of woman and social status of the family remained a predictor of LBW which have been mentioned in previous literature also.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Government of India. National Family Health Survey III (2005-2006). IIPS. Mumbai: Ministry of Health & Family Welfare; 2007.
Government of India. National Family Health Survey IV (2015-16). IIPS. Mumbai: Ministry of Health & Family Welfare; 2017.
International Institute for Population Science (IIPS) and Macro International. District Level Household and Facility Survey 4: Fact Sheet, Himachal Pradesh. IIPS, Mumbai: 2012-13. Available from: http://www.rchiips.org/pdf/dlhs4/report
. [Last accessed on 2017 Dec 27].
Risnes KR, Vatten LJ, Baker JL, Jameson K, Sovio U, Kajantie E, et al.
Birthweight and mortality in adulthood: A systematic review and meta-analysis. Int J Epidemiol 2011;40:647-61.
Larroque B, Bertrais S, Czernichow P, Léger J. School difficulties in 20-year-olds who were born small for gestational age at term in a regional cohort study. Pediatrics 2001;108:111-5.
Singh M, Sharma S, Kumar D, Bhardwaj AK. Studying the decision making process for giving birth at tertiary health facility of Sub Himalayan region. Int J Epidemiol Public Health 2016;1:15-24.
World Health Organization. International Statistical Classification of Diseases and Related Health Problems. 10th
Revision. Geneva: World Health Organization; 1992.
Rajaratnam J, Abel R, Ganesan C, Jayaseelan SA. Maternal anaemia: A persistent problem in rural Tamil Nadu. Natl Med J India 2000;13:242-5.
Siassakos D, Fox R, Draycott T, Winter C. Late Intrauterine Fetal Death and Stillbirth. Guideline No. 55. London, UK: Royal College of Obstetricians and Gynecologists; 2010. p. 2.
Kader M, Perera NK. Socio-economic and nutritional determinants of low birth weight in India. N
Am J Med Sci 2014;6:302-8.
Kumar V. Magnitude and correlates of low birth weight at term in rural Wardha. Online J Health Allied Sci 2010;15:237-78.
Khan A, Nasrullah FD, Jaleel R. Frequency and risk factors of low birth weight in term pregnancy. Pak J Med Sci 2016;32:138-42.
Gebregzabiherher Y, Haftu A, Weldemariam S, Gebrehiwet H. The prevalence and risk factors for low birth weight among term newborns in Adwa general hospital, Northern Ethiopia. Obstet Gynecol Int 2017;2017:2149156.
Nahum GG, Stanislaw H, Huffaker BJ. Accurate prediction of term birth weight from prospectively measurable maternal characteristics. Prim Care Update Ob Gyns 1998;5:193-4.
Anitha CJ, Nair MK, Rajamohanan K, Nair SM, Shenoy KT, Narendranathan M, et al.
Predictors of birthweight – A cross sectional study. Indian Pediatr 2009;46 Suppl: s59-62.
Sekhavat L, Davar R, Hosseinidezoki S. Relationship between maternal hemoglobin concentration and neonatal birth weight. Hematology 2011;16:373-6.
Rasmussen K. Is there a causal relationship between iron deficiency or iron-deficiency anemia and weight at birth, length of gestation and perinatal mortality? J Nutr 2001;131:590S-601S.
Hutter D, Kingdom J, Jaeggi E. Causes and mechanisms of intrauterine hypoxia and its impact on the fetal cardiovascular system: A review. Int J Pediatr 2010;2010:401323.
Ream M, Ray AM, Chandra R, Chikaraishi DM. Early fetal hypoxia leads to growth restriction and myocardial thinning. Am J Physiol Regul Integr Comp Physiol 2008;295:R583-95.
Mahajan SD, Singh S, Shah P, Gupta N, Kochupillai N. Effect of maternal malnutrition and anemia on the endocrine regulation of fetal growth. Endocr Res 2004;30:189-203.
Levy A, Fraser D, Katz M, Mazor M, Sheiner E. Maternal anemia during pregnancy is an independent risk factor for low birthweight and preterm delivery. Eur J Obstet Gynecol Reprod Biol 2005;122:182-6.
Elhassan EM, Abbaker AO, Haggaz AD, Abubaker MS, Adam I. Anaemia and low birth weight in Medani, hospital Sudan. BMC Res Notes 2010;3:181.
Ahmad MO, Kalsoom U, Sughra U, Hadi U, Imran M. Effect of maternal anaemia on birth weight. J Ayub Med Coll Abbottabad 2011;23:77-9.
Sharma SR, Giri S, Timalsina U, Bhandari SS, Basyal B, Wagle K, et al.
Low birth weight at term and its determinants in a tertiary hospital of Nepal: A case-control study. PLoS One 2015;10:e0123962.
Dickute J, Padaiga Z, Grabauskas V, Gaizauskiene A, Basys V, Obelenis V, et al.
Do maternal social factors, health behavior and work conditions during pregnancy increase the risk of low birth weight in Lithuania. Medicina (Kaunas) 2002;38:321-32.
Agarwal A, Agarwal K, Agrawal P, Agrawal V, Chaudhary V. Prevalence and determinants of “low birth weight” among institutional deliveries. Annals of Nigerian Medicine 2011;5:48-52.
Amin N, Abel R, Sampathkumar V. Maternal risk factors associated with low birth weight. Indian J Pediatr 1993;60:269-74.
Khatun S, Rahman M. Socio-economic determinants of low birth weight in Bangladesh: A multivariate approach. Bangladesh Med Res Counc Bull 2008;34:81-6.
Moraes AB, Zanini RR, Riboldi J, Giugliani ER. Risk factors for low birth weight in Rio Grande Do Sul state, Brazil: Classical and multilevel analysis. Cad Saude Publica 2012;28:2293-305.
Valero De Bernabé J, Soriano T, Albaladejo R, Juarranz M, Calle ME, Martínez D, et al.
Risk factors for low birth weight: A review. Eur J Obstet Gynecol Reprod Biol 2004;116:3-15.
Yadav H, Lee N. Maternal factors in predicting low birth weight babies. Med J Malaysia 2013;68:44-7.
Deshmukh JS, Motghare DD, Zodpey SP, Wadhva SK. Low birth weight and associated maternal factors in an urban area. Indian Pediatr 1998;35:33-6.
Phung H, Bauman A, Nguyen TV, Young L, Tran M, Hillman K, et al.
Risk factors for low birth weight in a socio-economically disadvantaged population: Parity, marital status, ethnicity and cigarette smoking. Eur J Epidemiol 2003;18:235-43.
Mumbare SS, Maindarkar G, Darade R, Yenge S, Tolani MK, Patole K, et al.
Maternal risk factors associated with term low birth weight neonates: A matched-pair case control study. Indian Pediatr 2012;49:25-8.
Mavalankar DV, Gray RH, Trivedi CR. Risk factors for preterm and term low birthweight in Ahmedabad, India. Int J Epidemiol 1992;21:263-72.
Fikree FF, Berendes HW. Risk factors for term intrauterine growth retardation: A community-based study in Karachi. Bull World Health Organ 1994;72:581-7.
Acharya D, Nagraj K, Nair NS, Bhat HV. Maternal determinants of intrauterine growth retardation: A case control study in Udupi district, Karnataka. Indian J Community Med 2004;29:181-3. [Full text]
Singh S, Shrestha S, Marahatta S. Incidence and risk factors of low birth weight babies born in Dhulikhel hospital. J Inst Med 2010;32:39-42.
Human Resources for Health. National Rural Health Mission, Ministry of Health and Family Welfare, Government of India. Child Health Division, Ministry of Health and Family Welfare; 2011. Available from: http://www.uhc-india.org/reports/hleg_report_chapter_4.pdf
. [Last accessed on 2018 Jan 05].
[Table 1], [Table 2], [Table 3], [Table 4]