|Year : 2020 | Volume
| Issue : 4 | Page : 158-165
Socioeconomic and demographic factors associated with low birth weight in Nepal: Data from 2016 Nepal demographic and health survey
Benojir Ahammed1, Md Maniruzzaman1, Farzana Ferdausi2, Md Menhazul Abedin1, Md Tanvir Hossain3
1 Statistics Discipline, Khulna University, Khulna, Bangladesh
2 Jhikargacha Upazila Health Complex, Jashore, Bangladesh
3 Sociology Discipline, Khulna University, Khulna, Bangladesh
|Date of Submission||06-Mar-2020|
|Date of Decision||11-Aug-2020|
|Date of Acceptance||12-Aug-2020|
|Date of Web Publication||8-Sep-2020|
Statistics Discipline, Khulna University, Khulna
Source of Support: None, Conflict of Interest: None
Introduction: Low birth weight (LBW) is an essential component for child mortality, and it also has dangerous effects on the mother's health. This study attempted to estimate the prevalence of the LBW among Nepalese children as well as to identify its socioeconomic and demographic determinants. Methods: For this study, 2016 Nepal Demographic and Health Survey data was used; 2,618 women having child were considered as respondents under precise specifications. The LBW of children was defined as birth weight <2500g. Descriptive statistics and multivariate logistic regression model were used to determine the risk factors of LBW based on the adjusted odds ratio (AOR) along with 95% confidence interval (CI) and P value (P < 0.05). Results: The overall prevalence of LBW in Nepal was 12.9% (95% CI: 11.6%–14.6%). The results of the multivariate analysis show that twin children (AOR: 22.538; 95% CI: 8.706–58.343) and female children (AOR: 1.444; 95% CI: 1.132–1.841) had a higher risk of LBW. Maternal age was also an important factor affecting LBW as findings suggest that the LBW tend to decrease with an increase of mother's age. Findings also indicate that children of the educated father with higher wealth status, maternal intake of iron tablets/syrup during pregnancy, and families having more than one child were safeguarding against LBW in Nepal. Conclusion: Risk factors of LBW are still problematic and unresolved in Nepal. Therefore, the implementation of social as well as health awareness programs, including maternal, neonatal and child health, are expected to introduce to curb LBW.
Keywords: Children, low birth weight, Nepal, risk factors
|How to cite this article:|
Ahammed B, Maniruzzaman M, Ferdausi F, Abedin MM, Hossain MT. Socioeconomic and demographic factors associated with low birth weight in Nepal: Data from 2016 Nepal demographic and health survey. Soc Health Behav 2020;3:158-65
|How to cite this URL:|
Ahammed B, Maniruzzaman M, Ferdausi F, Abedin MM, Hossain MT. Socioeconomic and demographic factors associated with low birth weight in Nepal: Data from 2016 Nepal demographic and health survey. Soc Health Behav [serial online] 2020 [cited 2023 Jun 6];3:158-65. Available from: https://www.shbonweb.com/text.asp?2020/3/4/158/294535
| Introduction|| |
Child mortality has a significant contribution to the overall deaths in the contemporary world. Both government and non-government organizations are fighting in the past few decades to reduce child mortality across the globe and made remarkable progress in child survivability. Globally, the total number of under-five death was 5.4 million in 2017, a 58% decrease from that of 1990s, a substantial proportion of these losses recorded in low-and middle-income countries. Among various factors contributing to the unexpected demises of young children, birth weight the utmost significant measure for determining neonatal and infant survival is one of the most critical one with instant health outcomes, and both developed and developing countries are experiencing severe problems in meeting maternal and child health in this regard.
World Health Organization (WHO) has defined low birth weight (LBW) as weight <2.5 kg at birth,, while other international possibilities suggest that the measurement should be taken preferably within the first hour of life before significant postnatal weight loss has occurred., Globally, 15%–20% of all infants born with weight deficiency, which inadvertently is contributing to 40%–60% of newborn mortality, hence, WHO is aiming at attaining a 30% reduction of the total number of infants born with an LBW by 2025., It is needless to say that babies born alive before 37 weeks of pregnancy posed high risk of LBW resulting 1.1 million demises. Moreover, LBW also rises the hazard for noncommunicable diseases in later life., LBW, with its short and long-run outcome, is one of the most pronounced hurdles to attain the Sustainable Development Goals (SDGs). The SDGs aimed at reducing the neonatal mortality rate by 12 per thousand live births and under-five mortality rate by 25 per thousand live births by 2030. However, achieving the targets remained a challenge for low-and middle-income countries because a large number of LBW babies are born in these countries.,,
The LBW is, indeed, a basic problem in developing countries. Asian countries, for example, account for 75% of the total LBW followed by Africa (20%) and Latin America (5%). Among Asian countries, South Asia has the highest incidence (27%) of LBW, whereas only 6% reported in Eastern Asia. Nepal is one of the poorest countries of South Asia and about one-fourth of its population are living below the poverty line. In Nepal, the prevalence of LBW is relatively high as documented from various hospital and community-based studies. The LBW rate was 18% in 2009–2013; however, it rose to 27% in 2015, of which LBW babies at term constituted 70%, and the rests constituted preterm babies., In Nepal, the child mortality rate was 33.7 deaths per 1000 live births in 2017, and over 75% of these newborn deaths occurred due to LBW.,
Numerous factors were explored to comprehend the causes of LBW in different countries.,,,,, Majority of these studies, however, focused on the maternal factors, sociocultural and nutritional risk factors, and only handful studies considered the socioeconomic and demographic variables. In Nepal, on the contrary, the socioeconomic and demographic factors, such as mothers antenatal care (ANC) visit, mothers working status, father's education, wealth index, intake of iron tablet/syrup during pregnancy, delivery by caesarean, twin children, and ecological zone, have not been well studied. Hence, it is essential to assess the effects of socioeconomic and demographic factors together to comprehend the complex nature of LBW, which will lead to awareness at an individual level as well as to implement public based interventions, such as media operations, health education messages, and national level policy directions. In this study, the objective was to find out the risk factors of LBW in Nepal. The findings of this study are expected to provide pertinent information for policymakers, program planners and other stakeholders, which in turn, may help to design and implement appropriate interventions at different levels to prevent LBW and for the betterment of women and child health.
| Methods|| |
Data source and sample design
This study utilized a cross-sectional dataset extracted from the fifth NDHS conducted in 2016. Details of survey design and data collection techniques have been described in the survey reports. NDHS collected nationally representative demographic, socioeconomic and health data in every five years. The target respondents for this survey were women of age between 15 and 49 years; hence, all residents meeting the criteria mentioned above in a household were eligible to participate. The NDHS used a stratified sampling strategy to enroll participants from both urban and rural areas. In the first stage, 383 wards were selected with probability proportional to the ward size and with independent selection in each sampling stratum. In the second and final stage, a fixed number of 30 households per cluster was selected with an equal probability systematic selection from the newly-created household listing. A total of 11,473 sampled households (7294 households from urban, 4179 households from rural) were selected for the sample, and among them, overall, 98.5% of the households responded. Within each selected household, all women aged between 15 and 49 years were eligible to be respondents for the survey. Data were collected according to a standard protocol. Three core survey questionnaires were used in this survey. The questionnaire was administered to all eligible members of the household by face-to-face interview by trained interviewers. The household questionnaire was administered to the respondents, who reported to be the head of the household. Data from the most recent child born within the household were included in this study.
The outcome variable for this study was birth weight expressed in a binary form (0 for normal birth weight (NBW) and 1 for LBW). The birth weight data were collected by measuring the children's weight. The birth weights of the children were recorded in grams. The NBW was considered as more than or equal to 2500 g. The newborn child whose weight was <2500g was considered as LBW.
Different socioeconomic and demographic factors were considered as explanatory variables based on previous studies conducted in Nepal as well as other countries. Prior studies found that maternal age,,,,,,, mother's education,,,,,, number of ANC visits,,, father's education, household wealth index,,,, intake of iron tablet/syrup during pregnancy, number of children, sex of children,, ecological zone, and place of residence, have substantially influenced the LBW. Despite their efforts to examine the determinants of LBW, there remains a lack of evidence about factors associated with LBW in Nepal. In this study, an attempt has been initiated to fill the existing gaps in the literature to assess the determinants of LBW in Nepal using data from NDHS 2016. This study also considered some additional factors, such as mother's working status, delivery by cesarean, twin-child, religion, to measure their impact on LBW. Risk factors of LBW were grouped into socioeconomic (mother's education and working status, husband's education, wealth index, delivery by caesarean, ecological zone, type of place of residence, and religion of the respondents) and demographic (maternal age, ANC visit, intake of iron tablets/syrup during pregnancy, number of children, twin-child, and sex of children) characteristics. Based on a broad literature review and availability of the dataset, we considered 14 explanatory variables along with categories which are discussed in [Table 1].
The Nepal Demographic and Health Survey (NDHS) was approved by the Nepal Health Research Council and ICF Macro International Review Board in the USA. The NDHS data were collected according to ethical standards. The Inha University School of Medicine granted ethics approval. The data is freely available online at https://dhsprogram.com/data/available-datasets.cfm.
At the preprocessing stage of data, missing observations were dropped from analysis. Descriptive statistics were used to present the socioeconomic and demographic characteristics of the respondents. Data then weighted to account for the sampling design and prevalence along with 95% confidence interval (CI) was calculated for LBW using the prior stated cut-points. The Chi-square analysis was performed to examine the bivariate association between the LBW and explanatory variables. Variables, which were found significant at 5% level of significance, were entered into logistic regression analysis. The multivariate logistic regression analysis was conducted to investigate how much the explanatory variables were influencing the LBW based on the adjusted odds ratio (AOR). For all cases, 5% level of significance was considered.
| Results|| |
[Table 2] presents the socioeconomic and demographic characteristics, unweighted distribution and weighted distribution of the respondents. In this study, birth weight was measured for a total of 2,618 children. Among them, 307 (11.7%) children had experienced LBW, and rest (2,311; 88.3%) experienced NBW. More than one-third (34.6%) of the mothers were between 20 and 24 age group and only 9.8% of the respondents were from 15 to 19 years age group. Most of the mothers (62.1%) and fathers (74.6%) of the children had secondary and higher education. Overall, 53.4% of the mothers had ever given birth to 2 or more children. While 54.2% of the mothers had received ANC <4times. Most (95.9%) of the mothers took iron tablets/syrup during pregnancy, whereas only 13.5% of the mothers gave birth by caesarean. Around 1% of the mothers had twin child, and most of the child were male (56.2%). Around half (49.8%) of the respondents lives in Terai region, whereas two-third (66.4%) of the respondents were from the urban area.
|Table 2: The basic characteristics and prevalence of the selected factors and low birth weight based on socioeconomic and demographic factors, Nepal Demographic and Health Survey, 2016 (n=2618)|
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The prevalence of LBW was low amongst mothers or fathers who had secondary and higher education or experienced an ANC visit (4 or more) during pregnancy or children from households with the top level of wealth or mothers took iron tablets/syrup during pregnancy. The prevalence of LBW was higher (18.1%) among the mothers who were young (15–19 years) and having single children (13.4%). Mothers having twin (62.5%) and female (13.3%) children had a higher prevalence of LBW. The prevalence of LBW was higher among children from rural (13.0%) and Terai (13.9%) areas.
Association of explanatory variables with low birth weight
[Table 2] reveals that bivariate analysis of the different socioeconomic and demographic characteristics with LBW. A total of 14 explanatory variables was used in bivariate analysis, and among them nine variables, namely maternal age (P = 0.001), ANC visits (P = 0.006), father's education (P = 0.003), wealth index (P = 0.029), intake of iron tablets/syrup during pregnancy (P = 0.004), number of children (P = 0.021), twin-child (P < 0.001), sex of children (P = 0.003) and ecological zone (P = 0.001), found to have statistical significance. The multivariate logistic regression analysis was executed using the significant explanatory variables from the Chi-square test.
[Table 3] shows the results of the multivariate logistic regression analysis. In the fully adjusted model, maternal age, mother's education, wealth status, intake of iron tablets/syrup during pregnancy; the number of children, twin-child, and sex of children were marked as significant factors for LBW. Logistic regression reveals that chances of LBW decreased as the age of mother increased. Children whose father received secondary and higher education have experienced the least LBW (AOR: 0.594; 95% CI: 0.409–0.862) compared to fathers with no education. The odds of having LBW of children were 0.578 times (AOR: 0.578; 95% CI: 0.350–0.854) lower among higher wealth status families compared to that of the poorest. Intake of iron tablets/syrup during pregnancy was negatively associated with LBW (AOR: 0.455; 95% CI: 0.303–0.685). A family having multiple children had 0.686 times (AOR: 0.686; 95% CI: 0.512–0.920) lower odds of LBW babies compared to the families with one child. The twin children were 22.538 times more likely to have LBW compared to the single child (AOR: 22.538; 95% CI: 8.706–58.343). A female child had 1.444 higher odds (AOR: 1.444; 95% CI: 1.132–0.841) of having LBW compared to a male child.
|Table 3: Unadjusted and adjusted odds ratios for factors associated with low birth weight according to socioeconomic and demographic factors, analysis of Nepal Demographic and Health Survey, 2016 (n=2618)|
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| Discussion|| |
The purpose of this paper was to explore the socioeconomic and demographic determinants of LBW in Nepal. Findings reveal that around one per eight children (12.9%) experienced LBW in Nepal, which is relatively high compared to other countries., LBW was associated with different socioeconomic and demographic characteristics, such as maternal age, father's education, wealth index, intake of iron tablets/syrup during pregnancy, the total number of children, twin-child, and sex of children. Our findings show that the prevalence of LBW in Nepal corresponded with some studies conducted in Nepal Medical College, Nepal (11.9%), and Afghanistan (15.5%).
The prevalence of LBW in nationally representative study was lower than the several studies conducted in different regions of Nepal, such as Dhulikhel Hospital, Nepal (21.6%), obstetrics and gynecology ward of Janakpur Zonal Hospital, Janakpur, Nepal (21.56%), College of Medical Sciences and Teaching Hospital, Bharatpur, Nepal (23.6%), and Tribhuvan University Teaching Hospital, Kathmandu, Nepal (33.33%). The prevalence of LBW was also lower compared to several other studies conducted in different regions or countries outside of Nepal, such as a tertiary care hospital in Uttar Pradesh, India, Ethiopia (17.3%), Bangladesh (23.22%), and least developed and developing countries (19.0%). A study conducted in gynecology and obstetrics ward in Bharatpur hospital, Chitwan, Nepal, found the prevalence of LBW around 9.4%, which is also lower than our present study. This difference could be explained by variations in the study set up and socioeconomic and demographic differences. This study found that as the age of mothers increased the likelihood of LBW decreased subsequently. The findings of the present study supported by many existing studies,, that suggesting older mother is less likely to have babies with LBW, because older mothers are less likely to deliver LBW children due to the maturity of the reproductive organs. The current study found that fathers, who had secondary and above education, were less likely to experience LBW children compared to fathers with no education. The finding is consistent with other studies carried out in, Poland, Bangladesh, and Indonesia. One possible explanation for the findings could be that better-educated fathers are relatively more conscious and cautious about taking care of their pregnant wives that eventually help the latter to nurture and deliver healthy children; however, further studies – whether qualitative or quantitative– in details is essential to examine the sociocultural circumstances.
This study also showed that children from richest households were less likely to have LBW, and such a result is in line with different international studies.,, This finding needs to be treated with caution; however, families with less financial capital may likely have the least access to proper health care as well as nutritious food facilities. Despite poor economic status, if a woman could maintain a functional nutritional status and avoid potential medical complications during pregnancy may give birth to a healthy baby. The current study also showed that the intake of iron tablets/syrup by the mother during pregnancy was associated with LBW. Mothers who took iron tablets/syrup during pregnancy, the risk of LBW of their children were lower compared to the mothers who did not take iron tablets/syrup during pregnancy. This finding is consistent with the findings of previous study carried out in some developing countries, such as Cambodia, Indonesia, Jordan, and Pakistan.
This study also demonstrated that the number of children of mothers was a significant factor of LBW. These findings were confirmed by other studies showing that families with more than one child were less likely to suffer from LBW in Malawi and some other developing countries. The reason for this number of children divergence is not well established, but it is believed that more than one child is more influential in making any mother more cognizant about the previous problem of LBW. Likewise, twin-child was also found to have a significant influence on LBW. Women having twins were more likely to experience LBW compared to women delivering a single child at once. The pregnancy with twins has a higher risk for prematurity, and prematurity may lead to several problems of LBW. Such findings are unique as no other existing studies evidently explored the dynamics of twin-child and LBW. This study also demonstrated that the sex of children was a significant factor for LBW, and the findings are in line with other studies,, suggesting that female children were significantly more likely to suffer from LBW than their male counterparts.
Strengths and limitations
This study has several strengths. First, this study used the 2016 NDHS data with considerably large sample size and higher statistical power. The dataset was the most recent and nationally representative that covers all regions and administrative cities of Nepal. The dataset also used validated and standardized survey tools to interview survey participants. Finally, this data was verified through records, removing the opportunity for recall bias.
However, this study has also had several limitations. For example, data of the study were cross-sectional that restricts the interpretation of the causality of factors associated with LBW. This study focused on socioeconomic and demographic variables in their interviews, ignoring other factors, such as genetic, environmental, and community-level variables because of unavailability. Therefore, a significant proportion of study samples were excluded from the study. This study only considered women aged between 15 and 49 years which did not cover people of all age groups. As the instruments, used to measure birth weight, were not calibrated or validated by the survey team, this could also cause some misclassification, though this misclassification is more likely to be nondifferential.
| Conclusion|| |
LBW is now a global concern because it is one of the major causes of under-five child mortality. This study provides national population-based estimates and found that LBW (11.7%) still exists among the children of Nepal. The study found that twin-child and female children are at higher risk of LBW. On the contrary, high maternal age, intake of iron/syrup by mothers during pregnancy and mothers having more than one child reduce the chance of LBW in Nepal. The underlying causes of LBW, however, remain an important issue for further research. Socioeconomic and demographic gaps need to be addressed through the proper policy action in order to reduce LBW among Nepalese population.
Availability of data and material
The all data sets are available to the public at online: https://dhsprogram.com/data/available-datasets.cfm.
The authors appreciate the Demography and Health Survey program for free access to the original data. The authors are also grateful for the contribution of Statistics Discipline under Science, Engineering and Technology School of Khulna University, Khulna 9208, Bangladesh.
Conflicts of interest
There are no conflicts of interest.
| References|| |
World Health Organization. Global Health Observatory (GHO) Data: Infant Mortality. Situation and Trends. World Health Organization; 2016.
Hug L, Sharrow D, You D. Levels & Trends in Child Mortality: Report 2017. Estimates Developed by the UN Inter-Agency Group for Child Mortality Estimation; 2017.
Kramer MS. Determinants of low birth weight: Methodological assessment and meta-analysis. Bull World Health Organ 1987;65:663-737.
World Health Organization. The Epidemiology of Infertility: Report of a WHO Scientific Group Meeting Held in Geneva: World Health Organization; 30 June, 4 July, 1975.
Singh G, Chouhan R, Sidhu K. Maternal Factors for Low Birth Weight Babies. Med J Armed Forces India 2009;65:10-2.
Park K. Park's Textbook of Preventive and Social Medicine. Preventive Medicine in Obstet, Paediatrics and Geriatrics; 2005.
UNICEF. The State of the World's Children 2009: Maternal and Newborn Health. UNICEF; 2008.
McGuire S. World Health Organization. Comprehensive implementation plan on maternal, infant, and young child Nutrition. Geneva, Switzerland, 2014. Adv Nutr 2015;6:134-5.
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.
Lee BX, Kjaerulf F, Turner S, Cohen L, Donnelly PD, Muggah R, et al
. Transforming our world: Implementing the 2030 agenda through sustainable development goal indicators. J Public Health Policy 2016;37 Suppl 1:13-31.
Mahumud RA, Sultana M, Sarker AR. Distribution and determinants of low birth weight in developing countries. J Prev Med Public Health 2017;50:18-28.
Sachdev HP. Low birth weight in South Asia. Int J Diab Dev Countries 2001;21:13-33.
Brämer GR. International statistical classification of diseases and related health problems. Tenth revision. World health statistics quarterly. Rapport Trimestriel de Statistiques Sanitaires Mondiales 1988;41:32-6.
Park K. Preventive medicine in obstetrics, paediatrics and geriatrics. In: Park's Textbook of Preventive and Social Medicine. Jabalpur: M/s Banarsidas Bhanot Publishers; 2000.
Bhaskar RK, Deo KK, Neupane U, Chaudhary Bhaskar S, Yadav BK, Pokharel HP, et al
. A case control study on risk factors associated with low birth weight babies in Eastern Nepal. Int J Pediatr 2015;2015:1-7.
World Health Organization. World Health Statistics 2016: Monitoring Health for the SDGs Sustainable Development Goals. World Health Organization; 2016.
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.
Taywade ML, Pisudde PM. Study of sociodemographic determinants of low birth weight in Wardha district, India. Clin Epidemiol Global Health 2017;5:14-20.
Kader M, Perera NK. Socio-economic and nutritional determinants of low birth weight in India. N
Am J Med Sci 2014;6:302-8.
Khan JR, Islam MM, Awan N, Muurlink O. Analysis of low birth weight and its co-variants in Bangladesh based on a sub-sample from nationally representative survey. BMC Pediatr 2018;18:100.
Kayode GA, Amoakoh-Coleman M, Agyepong IA, Ansah E, Grobbee DE, Klipstein-Grobusch K. Contextual risk factors for low birth weight: A multilevel analysis. PLoS One 2014;9:e109333.
Nobile CG, Raffaele G, Altomare C, Pavia M. Influence of maternal and social factors as predictors of low birth weight in Italy. BMC Public Health 2007;7:192.
Ministry of Health, Nepal. Nepal Demographic and Health Survey 2016. Kathmandu, Nepal: Ministry of Health, Nepal; 2017.
Yadav DK, Chaudhary U, Shrestha N. Risk factors associated with low birth weight. J Nepal Health Res Counc 2011;9:159-64.
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.
Hosain GM, Chatterjee N, Begum A, Saha SC. Factors associated with low birthweight in rural Bangladesh. J Trop Pediatr 2006;52:87-91.
Bondevik GT, Lie RT, Ulstein M, Kvåle G. Maternal hematological status and risk of low birth weight and preterm delivery in Nepal. Acta Obstet Gynecol Scand 2001;80:402-8.
Mondal B. Risk factors for low birth weight in Nepali infants. Indian J Pediatr 2000;67:477-82.
World Health Organization. Children: Reducing mortality. Weekly Epidemiological Record 2014;89:409-20.
Das Gupta R, Swasey K, Burrowes V, Hashan MR, Al Kibria GM. Factors associated with low birth weight in Afghanistan: A cross-sectional analysis of the demographic and health survey 2015. BMJ Open 2019;9:e025715.
Jafari F, Eftekhar H, Pourreza A, Mousavi J. Socio-economic and medical determinants of low birth weight in Iran: 20 years after establishment of a primary healthcare network. Public Health 2010;124:153-8.
Nair NS, Rao RS, Chandrashekar S, Acharya D, Bhat HV. Socio-demographic and maternal determinants of low birth weight: A multivariate approach. Indian J Pediatr 2000;67:9-14.
Matin A, Azimul SK, Matiur AK, Shamianaz S, Shabnam JH, Islam T. Maternal socioeconomic and nutritional determinants of low birth weight in urban area of Bangladesh. J Dhaka Med Coll 2008;17:83-7.
Kayastha S, Tuladhar H. Study of low birth weight babies in Nepal Medical College. Nepal Med Coll J 2007;9:266-9.
Prajapati R, Shrestha S, Bhandari N. Prevalence and associated factors of low birth weight among newborns in a tertiary level hospital in Nepal. Kathmandu Univ Med J (KUMJ) 2018;16:49-52.
Bansal P, Garg S, Upadhyay HP. Prevalence of low birth weight babies and its association with socio-cultural and maternal risk factors among the institutional deliveries in Bharatpur, Nepal. Asian J Med Sci 2019;10:77-85.
Agarwal K, Agarwal A, Agrawal VK, Agrawal P, Chaudhary V. Prevalence and determinants of” low birth weight” among institutional deliveries. Ann Nigerian Med 2011;5:48. [Full text]
Endalamaw A, Engeda EH, Ekubagewargies DT, Belay GM, Tefera MA. Low birth weight and its associated factors in Ethiopia: A systematic review and meta-analysis. Ital J Pediatr 2018;44:141.
Kandel KP, Kafle S. Risk factors associated with low birth weight among deliveries at bharatpur hospital. J Nepal Health Res Counc 2017;15:169-73.
Sebayang SK, Dibley MJ, Kelly PJ, Shankar AV, Shankar AH, SUMMIT Study Group. Determinants of low birthweight, small-for-gestational-age and preterm birth in Lombok, Indonesia: Analyses of the birthweight cohort of the SUMMIT trial. Trop Med Int Health 2012;17:938-50.
Merklinger-Gruchala A, Jasienska G, Kapiszewska M. Paternal investment and low birth weight-the mediating role of parity. PloS one 2019;14:e0210715.
Mumbare SS, Maindarkar G, Darade R, Yenge S, Tolani MK, Patole K. Maternal risk factors associated with term low birth weight neonates: A matched-pair case control study. Indian Pediatr 2012;49:25-8.
Muula AS, Siziya S, Rudatsikira E. Parity and maternal education are associated with low birth weight in Malawi. Afr Health Sci 2011;11:65-71.
[Table 1], [Table 2], [Table 3]
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