==== Front Rev Bras Ginecol Obstet Rev Bras Ginecol Obstet 10.1055/s-00030576 RBGO Gynecology & Obstetrics 0100-7203 1806-9339 Thieme Revinter Publicações Ltda Rio de Janeiro, Brazil 30142664 10.1055/s-0038-1667341 180115 Original Article Maternal Factors Associated with Low Birth Weight in Term Neonates: A Case-controlled Study Fatores Maternos Associados ao Baixo peso de Nascimento em Neonatos a Termo: um Estudo de Caso-controleMahecha-Reyes Eduardo 1 Grillo-Ardila Carlos Fernando 2 1 Department of Health Sciences, Universidad Surcolombiana, Neiva, Colombia 2 Department of Obstetrics and Gynecology, Universidad Nacional de Colombia, Bogotá, Colombia Address for correspondence Eduardo Mahecha-Reyes Departamento de Ciencias Médicas, Universidad SurcolombianaCarrera 20 No. 5 B-36, NeivaColombiaedmahecha97@gmail.com 8 2018 1 8 2018 40 8 444449 02 4 2018 28 5 2018 https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Objective To identify maternal factors associated with the presence of low birth weight in term neonates. Methods Matched hospital-based case-controlled study performed in a high complexity institution located in the city of Neiva, Colombia. The study included women with term gestation and singleton live fetuses. Patients with prior diseases, coming from other regions, with pregnancy resulting from assisted reproduction, or with a diagnosis of fetal abnormality or aneuploidy were excluded. Low birth weight was the dependent variable, and the independent variables that were analyzed were maternal sociodemographic and clinical characteristics. Adjusted and non-adjusted odds ratios (aOR and OR) together with the 95% confidence intervals (95% CI) were reported. Results The study included 270 participants (90 cases and 180 controls). Controlling for maternal age, educational level, socioeconomic and civil status, social security and the presence of maternal disease during gestation, it was found that weight gain (aOR 0.77, 95% CI 0.70–0.85) and the absence of prenatal care (aOR 8.20, 95% CI 3.22–20.87) were among the factors associated with low birth weight. Conclusions The absence of weight gain and of prenatal care are factors associated with the presence of low birth weight in term neonates and should be considered in clinical practice. Resumo Objetivo Identificar fatores maternos associados à presença de baixo peso ao nascer em neonatos a termo. Métodos Estudo de caso-controle realizado em uma instituição de alta complexidade localizada na cidade de Neiva, Colômbia. O estudo incluiu mulheres com gestação a termo e fetos vivos únicos. Pacientes com doenças prévias, provenientes de outras regiões, com gravidez resultante de reprodução assistida, ou com diagnóstico de anormalidade fetal ou aneuploidia foram excluídos. O baixo peso ao nascer foi a variável dependente, e as variáveis independentes analisadas foram as características sociodemográficas e clínicas maternas. Razões de chance ajustadas e não ajustadas (RCa e RC) juntamente com os intervalos de confiança de 95% (IC 95%) foram relatadas. Resultados O estudo incluiu 270 participantes (90 casos e 180 controles). Controlando a idade materna, nível escolar, socioeconômico e civil, segurança social e a presença de doença materna durante a gestação, constatou-se que ganho de peso (RCa 0,77, IC 95% 0,70–0,85) e ausência de pré-natal (RCa 8,20, IC 95% 3,22–20,87) estavam entre os fatores associados ao baixo peso ao nascer. Conclusão As ausências de ganho ponderal e de pré-natal são fatores associados à presença de baixo peso ao nascer em recém-nascidos a termo e devem ser considerados na prática clínica. Keywords case-controlled studies developing countries low birth weight risk factors term birth Palavras-chave estudos de caso-controle países em desenvolvimento baixo peso ao nascer fatores de risco termo de nascimento ==== Body pmcIntroduction The World Health Organization (WHO) defines low birth weight as weight at birth lower than 2,500 g.1 However, birth weight is determined by two critical considerations: gestational age at delivery and the rate of fetal growth.2 Consequently, low weight determining factors may differ between a preterm and a term neonate because, in the former, low weight is usually explained by prematurity, while in the latter, it is result of intrinsic and/or extrinsic factors that impact on developmental potential.1 2 Therefore, it comes as no surprise that low birth weight neonates have a worse prognosis in terms of survival and neural development.3 According to The United Nations International Children's Emergency Fund (UNICEF), more than 20 million infants are born with low weight in the world, accounting for 15% of all births. Of these, more than 95% are born in middle- and low-income countries, with those in Asia, Africa and Latin America being the most frequently affected.4 Prevalence in these countries is twice as high as the ones observed in developed countries, reflecting the inequities faced by pregnant women in those regions of the world where adverse conditions, such as malnutrition, poor weight gain, anemia or pregnancy-related disorders, like hypertension, possibly explain the observed frequencies.1 5 Preventing low birth weight is a public health priority, and one of the goals for the new millennium.4 Reducing the frequency of low birth weight pregnancies could have a positive impact on infant mortality in middle- and low-income countries, which are the ones with the largest shortage of resources required for the care of these neonates.4 Consequently, efforts aimed at identifying risk factors associated with this condition are mandatory.6 Knowledge of the factors that influence low birth weight will contribute to the timely identification and early intervention in pregnant women at risk.7 Therefore, the objective of this study was to identify maternal factors associated with the presence of low birth weight in term neonates born in 2015 and 2016 in a high complex institution in the city of Neiva, Colombia. Methods This was a retrospective, analytical case-controlled study conducted at Hospital Universitario Hernando Moncaleano Perdomo, a high complexity institution located in the city of Neiva, serving the population of southwestern Colombia. The subjects included were women with term gestations and singleton live fetuses, who were treated at the participating institution between January 2015 and October 2016. Patients with any existing diseases before pregnancy (such as hypertensive vascular disease, nephropathy, diabetes mellitus, thrombophilia, heart, neurodegenerative, cancer or autoimmune diseases), coming from a different geographic area, with a gestation resulting from assisted reproduction, or with a confirmed diagnosis of major fetal abnormality or aneuploidy were excluded. Cases were defined as live neonates with a weight at birth under 2,500 g and a gestational age of 37 weeks of gestation or more. Gestational age was estimated based on the last menstrual period or ultrasound. Term neonates weighing 2,500 g or more were considered controls. The cases were identified using entries in the database of the epidemiological surveillance system corresponding to low birth weight, and the information was compared with the newborn vital statistics registry. In Colombia, the birth of a neonate with low weight is a public health event and reporting is mandatory. Controls were selected from the vital statistics database of the participating institution and the quality of the data was verified by means of a clinical record review. The cases and controls were selected by random sampling until the required sample size was completed. Sample size estimation was based on the approach suggested by Freeman, consisting of the use of the event of interest by variable. In this way, the sample size for a non-conditioned logistic regression was determined to be 10 * (k + 1), in which k is the number of study variables.8 According to this principle, and given that for this study, a priori, research into the potential association between low birth weight and maternal age, weight gain, educational level, socioeconomic and marital status, social security, gestational age at the start of prenatal care, and the presence of maternal disease during gestation had been proposed, at least 90 cases and an equal number of controls were required (n = 180). However, it was decided to select 2 controls for every case to increase the power. Thus, the required sample size for the study was 270 participants. The data were analyzed using the Stata software package, version 15 (StataCorp. College Station, TX, USA). Descriptive statistics were applied to clinical and sociodemographic variables. The central trend and scatter were estimated for continuous data, and proportions and frequency measurements were used for qualitative data. The frequency and distribution were examined for categorical variables, and a normal distribution and variance homogeneity were analyzed for continuous variables. For the categorical variables, differences between cases and controls were tested using the Fisher exact test or the Pearson Chi-squared test. Continuous variables were compared using the Mann-Whitney test, given the absence of a normal distribution. A univariate logistic regression was applied to assess the association between candidate variables and low birth weight. A multivariate logistic model was built to incorporate the clinical and sociodemographic variables mentioned above. A non-conditional logistic regression was performed to adjust for the presence of potential confounding factors, the predictive ability of the model was estimated, and the goodness of the adjustment was assessed using McFadden R2. However, to identify the most parsimonious model, a second analysis was performed using the stepwise backward approach, to which the Bonferroni correction was applied, thus adjusting the significance level.9 For the second model, the independent variables associated with the outcome of interest were preserved, with a significance level lower than 0.005.9 Confidence intervals were estimated, and adjusted and non-adjusted odds ratios (aOR and OR) are presented as the association measure. The study protocol was approved by the Ethics Committee of the participating institution (Reference 009–006). Results There were 4,882 term deliveries in the participating institution during the study period. Of this total, 3.0% were neonates with low birth weight for gestational age. Once the target population was identified, the cases and controls were selected by random sampling until the required sample size was completed. For each selected case and control, we verify the inclusion and exclusion criteria before their incorporation into study. Finally, the study population was then assembled and consisted of 90 cases and 180 controls. In terms of the characteristics of the population analyzed, the mean age was 23 years, and there was a predominance of secondary education level, low socioeconomic bracket, free union as marital status, affiliation with the subsidized healthcare regime, and urban place of residence. Regarding clinical characteristics, 54.0% of the women were multiparous and 61.4% had attended 5 or more prenatal care visits; 62.9% were vaginal deliveries, the mean gestational age at the time of delivery was 38.4 weeks, and the mean birth weight was 2,965 g (SD ± 552 g). Table 1 summarizes the baseline characteristics of the population by group (cases or controls). Table 1 Description of the sociodemographic and clinical characteristics of cases and controls Variable Mean Cases (n = 90) Controls (n = 180) p value Mean Range Mean Range Age 22.0 (14–42) 24.1 (13–45) p = 0.00a Variable measured n (%) n (%) p value Educational level p = 0.29b None 0 (0.00) 1(0.56) Primary 20 (22.22) 30 (16.67) Secondary 61 (67.78) 138 (76.6) Technical/University 9 (10.00) 11 (6.11) Socioeconomic status p = 0.00b Low 58 (64.44) 139 (77.22) Medium 27 (30.00) 40 (22.22) High 5 (5.56) 1 (0.56) Marital status p = 0.89c Single 39 (43.33) 79 (43.89) Free union 43 (47.78) 88 (48.89) Married 8 (8.89) 13 (7.22) Social security p = 0.52b Subsidized 83 (92.22) 154 (85.56) Contributive/Special 6 (6.67) 23 (12.78) No payment capability 1 (1.11) 3 (1.67) Place of residence p = 0.37c Urban 64 (71.11) 137 (76.11) Rural 26 (28.89) 43 (23.89) Gestational age at the start of prenatal care p = 0.00c First trimester 30 (33.33) 86 (47.78) Second trimester 16 (17.78) 48 (26.67) Third trimester 20 (22.22) 39 (21.67) No prenatal care 24 (26.67) 7 (3.89) Disease during pregnancy p = 0.00c No 58 (64.44) 143 (79.44) Yes 32 (35.56) 37 (20.56) Sex of the newborn p = 0.79c Female 48 (53.33) 93 (51.67) Male 42 (46.67) 87 (48.33) a Mann-Whitney test for mean differences. b Fisher exact test. c Pearson Chi-squared test. The two groups were similar, except in terms of socioeconomic status, gestational age at the start of prenatal care, and the presence of disease during pregnancy. Table 2 summarizes the frequency and type of disease by group (cases or controls). Maternal age was significantly different between the groups, although the difference observed was not clinically relevant. For the neonates (data not shown), the mean gestational age at the time of birth was 37.9 weeks for the cases and 39.0 weeks for the controls, while the average weight was 2,328 g (SD ± 166 g) for the cases and 3,282 g (SD ± 371 g) for the controls. Of the low-weight neonates, 78.2% were admitted to the kangaroo program and 3.2% to the neonatal intensive care unit. Table 2 Frequency and type of disease during pregnancy by group (cases or controls) Disease during pregnancy Cases (n = 90) Controls (n = 180) p-value n (%) n (%) No 58 (64.4) 143 (79.4) p  = 0.00 a Yes 32 (35.5) 37 (20.5) By type Hypertensive disorder 19 (21.1) 17 (9.4) Perinatal infection 6 (6.6) 7 (3.8) Endocrine disease 2 (2.2) 5 (2.7) Placental-amniotic fluid disease 4 (4.4) 2 (1.1) Anemia 0 (0.0) 4 (2.2) Neurological condition 1 (1.1) 1 (0.05) Pulmonary disease 0 (0.0) 1 (0.05) a Pearson Chi-squared test. The univariate logistic regression revealed that maternal age (OR 0.94, 95% CI 0.90–0.98) and weight gain during pregnancy (OR 0.77, 95% CI 0.70–0.84) behaved as protective factors. The risk was also significantly lower depending on the educational level of the mother (technical/university education OR 0.10, 95% CI 0.01–0.83). On the other hand, the absence of prenatal care (OR 9.82, 95% CI 3.84–25.13) and the presence of maternal disease during pregnancy (OR 2.13, 95% CI 1.21–3.74) behaved as risk factors. Table 3 shows the results of the logistic regression, together with their respective aOR and CI. Table 3 Multivariate analysis of maternal factors associated with low birth weight Variables First modela aOR (95% CI)b Second modela aOR (95% CI) Maternal age 0.91 (0.86–.96) 3c Weight gain 0.77 (0.69–0.86) 0.77 (0.70–0.85) Absence of prenatal care 10.67 (3.49–32.64) 8.20 (3.22–20.87) Abbreviations: aOR, adjusted odds ratio; 95% CI, 95% confidence interval. a Adjusted for variables: level of schooling, socioeconomic bracket, marital status, social security and the presence of maternal disease. b Adjusted odds ratios together with the 95% confidence intervals; c Variable removed from the model: p of 0.01 but greater than 0.005. Following this exploration, a multivariate analysis was performed with the main goal of identifying the clinical or sociodemographic characteristics that could be linked to the presence of low birth weight. An initial model was built, ratifying the role played by maternal age, low weight gain and absence of prenatal care. When the goodness of the adjustment was assessed, the McFadden R2 was 0.27, with a predictive capacity of 80.3%, reflecting a high percentage of correctness.10 However, when the second analysis was performed, only the absence of prenatal care and of adequate weight gain continued to be statistically significant. This time, the McFadden R2 was 0.17, with a predictive capacity of 72.4%, showing an acceptable percentage of correctness.10 Discussion Low birth weight is one of the most important determinants of infant morbidity and mortality because it increases the frequency of adverse perinatal outcomes.11 Identification of factors that may have an impact on potential fetal growth is an excellent opportunity to have an impact on the health conditions of the population.12 There were 4,882 term live births during the observation period, of which 3.0% were low birth weight neonates. The population consisted mainly of young, single mothers with low socioeconomic and education levels, living in urban areas. Only 43% of the women started prenatal care during the first trimester of gestation. In our study, the prevalence of low birth weight was similar to that reported in other Latin American countries, but substantially lower than the one described for countries in Southeast Asia.13 14 15 16 17 18 19 These differences could be explained, at least in part, by the rate of fetal growth, genetic factors and the presence of other extrinsic circumstances not related to pregnancy.7 20 On the other hand, regarding the multivariate analysis, it revealed that maternal weight gain (aOR 0.77, 95% CI 0.70–0.85) behaves as a protective factor, while the absence of prenatal care (aOR 8.20, 95% CI 3.22–20.87) increases the probability of an unfavorable outcome. Our findings are similar to those documented in the literature. Observational studies have shown the association between maternal weight gain and neonatal birth weight.7 For example, it is known that infants born to mothers with poor weight gain are at a higher risk of being small for gestational age, while those born to mothers with substantial weight gain have a higher probability of being large.21 This association is consistent in low, middle and high-income countries alike.22 Regarding poor prenatal care or absence thereof, the observed association emerges in populations from middle and low-income countries, and it is not completely clear in developed countries.7 23 24 25 26 27 The potential explanation is that early and adequate prenatal care could be of greater benefit in women with less favorable conditions, where the implementation of this intervention could help address harmful behaviors that affect fetal growth (for example, smoking), early detection and treatment of diseases affecting gestation (such as anemia, malnutrition), and promote healthy lifestyle habits that can have a positive impact on the fetal environment.20 28 Finally, although maternal age was eliminated as a variable in the second model, the observed association seems plausible, is clinically relevant, and has been documented in other studies.29 This association could be explained by the conditions of inequity and disadvantage faced by teen mothers when compared with older women. Therefore, not surprisingly, the risk of low birth weight decreases as a function of older maternal age, given that older women may have better socioeconomic conditions and find themselves less at a social disadvantage.30 It is no secret that racial segregation and deprivation are associated with low birth weight.31 Notwithstanding, it needs to be said that this conclusion must be interpreted cautiously, given that the finding could be the result of multiple comparisons. This study has some strengths, starting with the appropriate and widely accepted definition of the cases, which are considered representative of the study population because all the eligible neonates with the outcome of interest were included. In Colombia, reporting of low birth weight events is mandatory, hence there is a low probability of having missed a case.32 33 On the other hand, given matched and independent data recording, there is confidence regarding the reliability of the data and a low probability of error in the definition of cases and controls. Case-controlled comparability was achieved by means of the design and the analysis.34 In the design, stringent inclusion and exclusion criteria were used in an attempt to arrive at a relatively homogenous population. In the analysis, a mathematical model was used to adjust for the presence of confounding factors. Although it is true that excluding the presence of residual confounding factors is not feasible, given the nature of the design, the importance and number of the variables considered allows, in some way, for acceptable case-controlled comparability.35 Finally, the other strength of this study is that exposure was proven through entry in the clinical record and in the vital statistics database of the Health Secretariat, reducing the risk of poor classification.35 This study also has weaknesses. The subjects used as controls came from the same institution and not from the community, making the study prone to selection bias (Berkson fallacy) because, given the origin of the controls, they could be more prone to having certain factors associated with low birth weight, leading to potential distortions for some exposure-disease associations.35 Sample size is yet another weakness of the study. Although a design was developed a priori for estimating sample size, the critical assessment of the confidence intervals points to some degree of inaccuracy.36 Despite its limitations, this study has many practical implications. First, it highlights the need to ensure adequate weight gain during pregnancy. This is important because adequate weight gain during gestation behaves as an indirect indicator of good nutritional status of the pregnant woman. Secondly, the association observed between poor prenatal care and low birth weight should prompt timely and adequate access to medical care during gestation, especially for vulnerable populations. Medical care during the reproductive period is a valuable opportunity to have a positive impact on health conditions by means of education regarding healthy lifestyle and to ensure early detection and treatment of diseases affecting gestation. Finally, the role of an unfavorable socioeconomic environment could play out in the association observed between maternal age and low birth weight. Hence the need for highlighting the relevance of providing timely and equitable access to quality healthcare systems. Conclusion Based on the findings of this study, low maternal weight gain and untimely initiation of prenatal care are some of the factors known to be associated with the presence of low birth weight in term neonates. On the other hand, the role of maternal age could also be relevant, considering that this association reflects, at least in part, the conditions of poverty and deprivation of the study population. This study, despite its many strengths, has limitations. Therefore, further studies are required to undertake a more extensive evaluation of the maternal factors associated with the presence of low birth weight among term neonates. Acknowledgments We would like to thank to Jose Alferez Carranza (research assistant), who supported information management. Contributions Conflicts of Interest The authors have no conflicts of interest to declare. Each author participated actively in the planning, execution and conduction of this study. The authors drafted the manuscript, edited, and approved the final, submitted version. None of the authors has a financial or any other conflict of interest. ==== Refs References 1 Kramer M S The epidemiology of low birthweight Basel Karger 2013 1 10 2 Barros F C Barros A J Villar J Matijasevich A Domingues M R Victora C G How many low birthweight babies in low- and middle-income countries are preterm? Rev Saude Publica 2011 45 03 607 616. Doi: 10.1590/S0034-8910201100500001921503557 3 Keram A Aljohani A Low birth weight prevalence, risk factors, outcomes in primary health care setting: a cross-sectional study Obstet Gynecol Int J. 2016 5 05 176. Doi: 10.15406/ogij.2016.05.00176 4 United Nations Children's Fund, World Health Organization. Low Birthweight: Country, Regional and Global Estimates. New York, NY: UNICEF; 2004 https://www.unicef.org/publications/index_24840.html. Accessed October 13, 2017 5 Daza V Jurado W Duarte D Gich I Sierra-Torres C H Delgado-Noguera M Bajo peso al nacer: exploración de algunos factores de riesgo en el Hospital Universitario San José en Popayán (Colombia) Rev Colomb Obstet Ginecol 2009 60 124 134 6 Castaño-Castrillón J J Giraldo-Cardona J F Murillo-Díaz C A Relación entre peso al nacer y algunas variables biológicas y socioeconómicas de la madre en partos atendidos en un primer nivel de complejidad en la ciudad de Manizales, Colombia, 1999 al 2005 Rev Colomb Obstet Ginecol 2008 59 01 20 25 7 Kramer M S Determinants of low birth weight: methodological assessment and meta-analysis Bull World Health Organ 1987 65 05 663 737 3322602 8 Ortega Calvo M Cayuela Domínguez A [Unconditioned logistic regression and sample size: a bibliographic review] Rev Esp Salud Publica 2002 76 02 85 93 12025266 9 Louviere J J Hensher D A Swait J D Adamowicz W Stated Choice Methods: Analysis and Applications New York, NY Cambridge University Press 2000 10 Kleinbaum D G Klein M Logistic Regression: A Self-Learning Text. 3rd ed New York, NY Springer 2011 11 Malin G L Morris R K Riley R Teune M J Khan K S When is birthweight at term abnormally low? A systematic review and meta-analysis of the association and predictive ability of current birthweight standards for neonatal outcomes BJOG 2014 121 05 515 526. Doi: 10.1111/1471-0528.1251724397731 12 Risnes K R Vatten L J Baker J L Birthweight and mortality in adulthood: a systematic review and meta-analysis Int J Epidemiol 2011 40 03 647 661. Doi: 10.1093/ije/dyq26721324938 13 Villar J Belizán J M The relative contribution of prematurity and fetal growth retardation to low birth weight in developing and developed societies Am J Obstet Gynecol 1982 143 07 793 798. Doi: 10.1016/0002-9378(82)90012-67102746 14 Silva A A Barbieri M A Gomes U A Bettiol H Trends in low birth weight: a comparison of two birth cohorts separated by a 15-year interval in Ribeirão Preto, Brazil Bull World Health Organ 1998 76 01 73 84 9615499 15 Villar J Ezcurra E G de La Fuente V G Canpodonico I Preterm delivery syndrome: the unmet need Res Clin Forums. 1994 16 9 33 16 Arifeen S E Black R E Caulfield L E Infant growth patterns in the slums of Dhaka in relation to birth weight, intrauterine growth retardation, and prematurity Am J Clin Nutr 2000 72 04 1010 1017. Doi: 10.1093/ajcn/72.4.101011010945 17 Bang A T Baitule S B Reddy H M Deshmukh M D Bang R A Low birth weight and preterm neonates: can they be managed at home by mother and a trained village health worker? J Perinatol 2005 25 01 S72 S81. Doi: 10.1038/sj.jp.721127615791281 18 Osendarp S J van Raaij J M Arifeen S E Wahed M Baqui A H Fuchs G J A randomized, placebo-controlled trial of the effect of zinc supplementation during pregnancy on pregnancy outcome in Bangladeshi urban poor Am J Clin Nutr 2000 71 01 114 119. Doi: 10.1093/ajcn/71.1.11410617955 19 Christian P Khatry S K Katz J Effects of alternative maternal micronutrient supplements on low birth weight in rural Nepal: double blind randomised community trial BMJ 2003 326 (7389):571. Doi: 10.1136/bmj.326.7389.57112637400 20 Valero De Bernabé J Soriano T Albaladejo R Risk factors for low birth weight: a review Eur J Obstet Gynecol Reprod Biol 2004 116 01 3 15. Doi: 10.1016/j.ejogrb.2004.03.00715294360 21 Goldstein R F Abell S K Ranasinha S Association of gestational weight gain with maternal and infant outcomes: a systematic review and meta-analysis JAMA 2017 317 21 2207 2225. Doi: 10.1001/jama.2017.363528586887 22 Han Z Lutsiv O Mulla S Rosen A Beyene J McDonald S D ; Knowledge Synthesis Group. Low gestational weight gain and the risk of preterm birth and low birthweight: a systematic review and meta-analyses Acta Obstet Gynecol Scand 2011 90 09 935 954. Doi: 10.1111/j.1600-0412.2011.01185.x21623738 23 Pinzón-Rondón A M Gutiérrez-Pinzon V Madriñan-Navia H Amin J Aguilera-Otalvaro P Hoyos-Martínez A Low birth weight and prenatal care in Colombia: a cross-sectional study BMC Pregnancy Childbirth 2015 15 118. Doi: 10.1186/s12884-015-0541-025989797 24 Bazyar J Daliri S Sayehmiri K Karimi A Delpisheh A Assessing the relationship between maternal and neonatal factors and low birth weight in Iran; a systematic review and meta-analysis J Med Life 2015 8 (Spec Iss 4):23 31 28316702 25 Vélez-Gómez M P Barros F C Echavarría-Restrepo L G Hormaza-Angel M P Prevalencia de bajo peso al nacer y factores maternos asociados: Unidad de atención y protección materno infantil de la clínica universitaria bolivariana, Medellín, Colombia Rev Colomb Obstet Ginecol 2006 57 04 264 270 26 da Silva T R Nonbiological maternal risk factor for low birth weight on Latin America: a systematic review of literature with meta-analysis Einstein (Sao Paulo) 2012 10 03 380 385. Doi: 10.1590/S1679-4508201200030002323386023 27 Rahman M M Abe S K Rahman M S Maternal anemia and risk of adverse birth and health outcomes in low- and middle-income countries: systematic review and meta-analysis Am J Clin Nutr 2016 103 02 495 504. Doi: 10.3945/ajcn.115.10789626739036 28 ACOG Committee Opinion No 579: Definition of term pregnancy Obstet Gynecol 2013 122 05 1139 1140. Doi: 10.1097/01.AOG.0000437385.88715.4a24150030 29 Dennis J A Mollborn S Young maternal age and low birth weight risk: An exploration of racial/ethnic disparities in the birth outcomes of mothers in the United States Soc Sci J 2013 50 04 625 634. Doi: 10.1016/j.soscij.2013.09.00825328275 30 Restrepo-Méndez M C Lawlor D A Horta B L The association of maternal age with birthweight and gestational age: a cross-cohort comparison Paediatr Perinat Epidemiol 2015 29 01 31 40. Doi: 10.1111/ppe.1216225405673 31 Vos A A Posthumus A G Bonsel G J Steegers E AP Denktaş S Deprived neighborhoods and adverse perinatal outcome: a systematic review and meta-analysis Acta Obstet Gynecol Scand 2014 93 08 727 740. Doi: 10.1111/aogs.1243024834960 32 Cunningham F G Leveno K J Bloom S L Hauth J C Gilstrap L III Wenstrom K D Williams Obstetrics. 22nd ed New York, NY McGraw-Hill 2005 33 Ministerio de Salud y Protección Social. Instituto Nacional de Salud. Protocolo de Vigilancia en Salud Publica Bajo Peso al Nacer a Término. Bogotá: Minsalud; 2014 http://www.dadiscartagena.gov.co/images/docs/saludpublica/vigilancia/protocolos/pro_bajo_peso_al_nacer_a_termino_2014.pdf. Accessed March 10, 2017 34 Wells G A Shea B O'Connell D The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-Analyses. 2013 http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp. Accessed March 10, 2017 35 Szklo M Javier Nieto F Epidemiología Intermedia: Conceptos y Aplicaciones Madrid Díaz de Santos 2003 36 Guyatt G H Oxman A D Kunz R GRADE guidelines 6. Rating the quality of evidence--imprecision J Clin Epidemiol 2011 64 12 1283 1293. Doi: 10.1016/j.jclinepi.2011.01.01221839614