Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-29T02:25:14.456Z Has data issue: false hasContentIssue false

Nutrition in Colombian pregnant women

Published online by Cambridge University Press:  05 January 2012

Olga L Sarmiento*
Affiliation:
Department of Public Health, School of Medicine, Universidad de los Andes, Carrera 1a 18A-10, Bogotá, Colombia
Andrea Ramirez
Affiliation:
Department of Public Health, School of Medicine, Universidad de los Andes, Carrera 1a 18A-10, Bogotá, Colombia
Belén Samper Kutschbach
Affiliation:
Profamilia, Department of Research and Evaluation, Bogotá, Colombia
Paula L Pinzón
Affiliation:
Department of Public Health, School of Medicine, Universidad de los Andes, Carrera 1a 18A-10, Bogotá, Colombia
Sandra García
Affiliation:
School of Government, Universidad de los Andes, Bogotá, Colombia
Angie C Olarte
Affiliation:
Department of Public Health, School of Medicine, Universidad de los Andes, Carrera 1a 18A-10, Bogotá, Colombia
Tatiana Mosquera
Affiliation:
Department of Public Health, School of Medicine, Universidad de los Andes, Carrera 1a 18A-10, Bogotá, Colombia
Eduardo Atalah
Affiliation:
Department of Nutrition, Faculty of Medicine, Universidad de Chile, Santiago, Chile
Gabriel Ojeda
Affiliation:
School of Government, Universidad de los Andes, Bogotá, Colombia
Yibby Forero
Affiliation:
National Institute of Health (Instituto Nacional de Salud), Bogotá, Colombia
*
*Corresponding author: Email [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Objective

The present study aimed to evaluate the nutritional status of pregnant women in Colombia and the associations between gestational BMI and sociodemographic and gestational characteristics.

Design

Cross-sectional study. A secondary analysis was made of data from the 2005 Demographic and Health Survey of Colombia.

Setting

Bogotá, Colombia.

Subjects

Pregnant adolescents aged 13–19 years (n 430) and pregnant women aged 20–49 years (n 1272).

Results

The gestational BMI and sociodemographic characteristics of the adolescents differed from those of the pregnant adult women. Thirty-one per cent of the adolescents were underweight for gestational age, compared with 14·5 % of the adult women. Eighteen per cent of adolescents were overweight for gestational age, in contrast to 37·3 % of adult women. The overall prevalence of anaemia was 44·7 % and the prevalence of low serum ferritin was 38·8 %. Women within the high quintiles of the wealth index (prevalence odds ratio (POR) = 0·56; 95 % CI 0·34, 0·91, P < 0·02) had lower odds of being underweight. Women who received prenatal care (POR = 2·17; 95 % CI 1·48, 3·09, P < 0·001) and were multiparous (POR = 2·10; 95 % CI 1·43, 3·15, P < 0·0 0 1) had higher odds of being overweight. Women in extended families (POR = 0·63; 95 % CI 0·50, 0·95, P < 0·025) had lower odds of being overweight.

Conclusions

Underweight in pregnant adolescents and overweight in adult women coexist as a double burden in Colombia. Factors associated with malnutrition among pregnant women and adolescents should be considered for future interventions in countries experiencing nutritional transition.

Type
Research paper
Copyright
Copyright © The Authors 2011

Currently, the double burden and nutritional paradox of simultaneous underweight and overweight is present throughout the developing world and mostly affects women(Reference Uauy, Albala and Kain1, Reference Swinburn, Sacks and Hall2). Likewise, the phenomenon of nutritional transition, which refers to the shifting burden of obesity from socio-economically privileged groups to the poor, has been observed over recent decades in several low- and middle-income countries, particularly among women(Reference Uauy, Albala and Kain1, Reference Doak, Adair and Bentley3, Reference Mendez, Monteiro and Popkin4).

Nutritional status among pregnant women is known to be a determinant of health for both the women and their newborns(Reference Abenhaim, Kinch and Morin5). Worldwide, the prevalence of maternal undernutrition ranges from 10 % to 19 %, being more common among poor populations(Reference Black, Allen and Bhutta6).

Undernutrition in pregnancy is associated with an increased risk of maternal and infant morbidity and mortality. Short-term consequences of undernutrition include intra-uterine growth restriction, small-for-gestational-age infants, preterm labour and perinatal death(Reference Abenhaim, Kinch and Morin5, Reference Ramakrishnan7). Long-term consequences include stunting among children, which leads to shorter adult height, lower intellectual ability, reduced adult income and increased risk of chronic diseases such as hypertension, coronary disease, insulin resistance, diabetes mellitus and hyperlipidaemia(Reference Ramakrishnan7Reference Phillips, Barker and Hales10).

In addition, pregnant women are at risk of multiple micronutrient deficiencies, including deficiencies of Fe, folic acid, riboflavin and vitamins A, D, B6 and B12(Reference Haider, Yakoob and Bhutta9). Fe deficiency is the most prevalent and neglected nutrient deficiency in the world, affecting 30 % of the world's population and 56 million pregnant women(11, Reference Lee and Okam12). In developing countries, inadequate intake of Fe coupled with increased requirements during pregnancy exacerbate maternal deficiency and potentiate adverse effects for the mother and the child(Reference Lee and Okam12).

The prevalence of overweight and obesity during pregnancy has been increasing rapidly in both developed and developing countries, affecting women of all ages(Reference Yogev and Catalano1316). In the USA the prevalence ranges between 10 % and 39 %(Reference Ehrenberg, Dierker and Milluzzi17, Reference LaCoursiere, Bloebaum and Duncan18). In Latin America, Brazil reports a prevalence of 22 %(Reference Santos, Barros and Matijasevich19), and in some African countries obesity is as high as 14 %(Reference Rayis, Abbaker and Salih20, Reference Villamor, Msamanga and Urassa21).

Short-term consequences of obesity during pregnancy include spontaneous abortion, recurrent miscarriage, congenital anomalies like neural tube and cardiac defects, pre-eclampsia, gestational diabetes, preterm birth and stillbirth(Reference Leddy, Power and Schulkin22). The risk of gestational diabetes mellitus increases twofold for overweight women and eightfold for obese women(Reference Siega-Riz and King23, Reference Reece24). In the peripartum period, there is an increased prevalence of Caesarean section, induced labour, receiving oxytocin, slower progression through labour and complications of Caesarean section such as wound infection and excessive blood loss.

The long-term consequences include retaining weight gain after pregnancy, maternal progression to type 2 diabetes, childhood obesity and, later in life, higher blood pressure and type 2 diabetes(Reference Siega-Riz and King23, Reference Anderson, Waller and Canfield25, Reference Poston, Harthoorn and Van Der Beek26). Infants born to overweight and obese women are more likely to suffer fetal macrosomia and shoulder dystocia(Reference Leddy, Power and Schulkin22, Reference Siega-Riz and King23, Reference Poston, Harthoorn and Van Der Beek26, Reference Seligman, Duncan and Branchtein27).

There is growing recognition among clinicians and public health workers that the approaches used to prevent and respond to health problems among pregnant women need to account for the different health problems within adults and adolescents(Reference Ramakrishnan7, 28). Importantly, adolescent pregnancy in developing countries has been associated with poor nutrition and an increased severity of micronutrient deficiencies(28).

In recent decades, adolescent pregnancy has become an important health issue in developing countries. The adolescent fertility rate in developed countries was 29 births per 1000 women, while in developing countries it was 133 births per 1000 women. In Colombia, the adolescent fertility rate increased significantly from 70 per 1000 in 1990 to 90 per 1000 in 2005(28).

For these reasons, it is necessary to assess the nutritional status of the pregnant population, while accounting for differences between adolescents and adults. Therefore, the present analysis aimed to estimate the prevalence of underweight and overweight among pregnant adolescents and pregnant adult women, and to assess the associations between gestational BMI and sociodemographic and pregnancy factors(28).

Colombia is a unique setting in which to explore malnutrition in pregnant women and adolescents, because the prevalence of both underweight and overweight is relatively high and the nutritional transition is apparent from the fact that the obesity gradient has already tipped against women in the poorest households. The findings of our analysis could thus guide future interventions and public health policies for pregnant women in developing countries experiencing nutritional transition.

Experimental methods

Setting

In 2005, Colombia had an estimated population of almost 43 million. Approximately half were females, 19 % were between 10 and 19 years of age and 43 % were between 20 and 49 years of age. About 17 % of the population lived on less than $US 2 per day and 7 % on less than $US 1 per day, 89·9 % knew how to read and write, 37·2 % had finished elementary school and 31·7 % had completed high school(29, 30).

Sampling design and study population

We conducted a secondary analysis of cross-sectional data from the 2005 Demographic and Health Survey of Colombia (Encuesta Nacional de Demografía y Salud (ENDS)) and the National Nutritional Survey (Encuesta Nacional de la Situación Nutricional en Colombia (ENSIN))(28). The Demographic and Health Surveys are conducted in seventy-five countries worldwide as part of a project by the US Agency for International Development (coordinated by ICF Macro International)(31).

ENDS applied a multistage, stratified, population-based cluster sampling design to a national sample of 37 211 households stratified by cluster (household segments) that included 41 344 women of reproductive age (13–49 years old). From this sample 1702 (4·1 %) were pregnant at the time of interview. The survey obtained complete anthropometric information for 1620 women, the sample for the present analysis.

The original questionnaire contained both household and individual components and was administered with computer-assisted personal interview technology. All protocols were approved by the Profamilia Institutional Review Board on Research Involving Human Subjects.

Outcome measurement

The maternal weight and height used to calculate BMI were measured by trained personnel with calibrated equipment. The outcome BMI for gestational age was calculated using the algorithm developed by Atalah et al.(Reference Atalah, Castillo and Castro32, Reference Atalah and Castro33). The algorithm was based on a reference table of BMI from the 10th to the 42nd week of gestation. The FAO/WHO BMI classification for non-pregnant women was used for the first 10 weeks of gestation and contained the following categories: underweight (BMI < 18·50 kg/m2), normal weight (BMI = 18·50–24·99 kg/m2), overweight (BMI = 25·00–29·99 kg/m2) and obese (BMI ≥ 30·00 kg/m2)(34). According to data from Thomson and Billewicz, the average cumulative weight gain in the first 10 weeks of gestation is 600 g, equivalent to 0·25 kg/m2 for women of 1·55 m(Reference Billewicz and Thomson35). The ‘ideal’ increase in weight for women with normal BMI is estimated to be 20 % of the pre-pregnancy weight, which is associated with lower maternal and fetal morbidity and mortality in most studies(Reference Ehrenberg, Dierker and Milluzzi17Reference Santos, Barros and Matijasevich19, Reference Villamor, Msamanga and Urassa21, Reference Siega-Riz and King23, Reference Gale, Javaid and Robinson36Reference Leddy, Power and Schulkin38). This is equivalent to 11 kg for a woman with a height of 1·55 m and a BMI of 23·0 kg/m2 (about 4·5 kg/m2). The slope of the curve along the pregnancy is assumed to have a sigmoid shape, with the greatest increase between the 20th and 30th weeks(Reference Billewicz and Thomson35, Reference Gale, Javaid and Robinson36, Reference Leddy, Power and Schulkin38), and it should be higher in emaciated women and lower in pregnant women with overweight and obesity. This algorithm was validated in 1997 with a cohort of 665 pregnant women in six selected clinics in Chile(Reference Atalah, Castillo and Castro32). The algorithm has been used in at least four nutritional surveys in Latin American countries(Reference Atalah, Castillo and Castro32, Reference Grandi, Luchtenberg and Sola39Reference Benjumea44).

Measures of independent variables

The sociodemographic characteristics included age (adolescents aged 13–19 years v. adults aged 20–49 years), education (no formal education or elementary school v. high school or higher education), wealth index (WI; 1st and 2nd quintiles v. 3rd quintile v. 4th and 5th quintiles), family structure (single person or nuclear family v. extended family), marital status (single v. married or member of an unmarried couple) and urbanicity (urban v. rural). The age cut-off point for adolescence was determined according to WHO recommendations(45). The WI was developed by the World Bank for world populations, as a measure of wealth distribution across a country, taking into account respondents’ household assets, amenities and services(31).

The pregnancy-related characteristics included health insurance status (no v. yes), prenatal care during pregnancy (no v. yes), trimester of pregnancy and parity (nulliparous v. multiparous).

Hb and serum ferritin (SF) concentrations were also measured in subsamples of 667 and 571 pregnant women. Hb was measured by the Hemocue method and adjusted by altitude as recommended by the International Nutritional Anemia Consultative Group(46). Anaemia was defined as having Hb < 11 g/dl. SF was determined by chemiluminescence, using an automatic analyser (ADIVIA Centaur®). Low SF level was used as an indicator of Fe deficiency and the cut-off for depleted Fe stores was defined as SF < 12 μg/l(11, Reference Ciok and Leibschang47, Reference Sullivan, Mei and Grummer-Strawn48). Our analysis excludes women with levels of C-reactive protein above 1.2 mg/dl(11, Reference Ciok and Leibschang47, Reference Sullivan, Mei and Grummer-Strawn48).

Statistical methods

Our analytic strategy involved several steps. First, we described the nutritional status of pregnant women based on the gestational BMI (underweight v. normal weight v. overweight). Second, we conducted bivariate and multivariate analyses using unordered multinomial logistic regression models to assess the association between independent variables with the categories of gestational BMI, with the normal category as the reference. Collinearity between independent variables was evaluated using regression diagnostic tests. All analyses were conducted using the statistical software packages SAS version 9·1 (SAS Institute Inc., Cary, NC, USA) and STATA version 9·0 (StataCorp LP, College Station, TX, USA) with appropriate weighting and adjustment for the sampling design.

Results

Study population

Of all the pregnant women sampled, 24·7 % were adolescents, with an average age of 17·16 (sd 1·45) years, and 75·3 % were adults, with an average age of 27·39 (sd 5·47) years. Sixty-eight per cent of all the pregnant women reported having attended school beyond the elementary level, 47·3 % were in the first and second WI quintiles and 50·7 % had extended families. Most women were married or a member of an unmarried couple (73·6 %), lived in urban areas (69·8 %), reported having health insurance (65·3 %) and reported receiving prenatal care during pregnancy (75·7 %). Forty-one per cent were in their second trimester of pregnancy, and 60·5 % were multiparous (Table 1).

Table 1 Sociodemographic and pregnancy characteristics among pregnant women in Colombia, Demographic and Health Survey (ENDS) and National Nutritional Survey (ENSIN), 2005

SF, serum ferritin.

*Unweighted sample size. Sample sizes may not sum to total N due to missing data.

Pregnant adolescents differed significantly from pregnant adult women in certain sociodemographic and pregnancy characteristics (Table 2). Adolescents were more likely to live in extended families and to be nulliparous. Although not statistically significant, 53·1 % of adolescents were in the first and second WI quintiles and 22·5 % were multiparous. In contrast, adult women were more likely to be married or a member of an unmarried couple, to have health insurance and to report receiving prenatal care during pregnancy.

Table 2 Sociodemographic and pregnancy characteristics of pregnant adolescents and adult women in Colombia, Demographic and Health Survey (ENDS) and National Nutritional Survey (ENSIN), 2005

SF, serum ferritin.

Prevalence of underweight and overweight among pregnant women

Overall, 18·6 (95 % CI 16·1, 21·4) % of all pregnant women were underweight, 48·8 (95 % CI 45·3, 52·3) % were of normal weight and 32·6 (95 % CI 29·0, 36·3) % were overweight. Among the subsamples of pregnant women with Hb and SF measurements, the mean Hb level was 11 g/dl and the mean SF level was 24 μg/l. Almost half of the women had anaemia, and 38·8 % had depleted Fe stores (Table 1).

The adolescents and the adult women differed in their gestational BMI: while 31·2 % of the adolescents were underweight, 14·5 % of the adult women were underweight. In contrast, 18·0 % of the adolescents were overweight compared with 37·3 % of the adult women (Fig. 1, Table 2). Although both the adolescents and the adult women showed a high prevalence of anaemia and Fe store deficiency, the adolescents exhibited a pattern of higher deficiencies, though not one that was statistically significant (Table 2).

Fig. 1 Nutritional status by BMI among pregnant adolescents () and adult women () in Colombia, Demographic and Health Survey (ENDS) and National Nutritional Survey (ENSIN), 2005. Values are prevalence with 95 % confidence intervals represented by vertical bars

Underweight-related factors

In the bivariate analysis, adolescent pregnant women had higher odds of being underweight compared with adult pregnant women. Women in the highest WI quintiles, who had health insurance and were multiparous had lower odds of being underweight compared with their comparison groups (Table 3).

Table 3 Bivariate associations between nutritional status and sociodemographic and pregnancy characteristics among pregnant women in Colombia, Demographic and Health Survey (ENDS) and National Nutritional Survey (ENSIN), 2005

POR, prevalence odds ratio; Ref., referent category.

In the multivariate analysis, the associations between underweight and being an adolescent and being in the WI highest quintile remained statistically significant (Table 4).

Table 4 Multivariate associations between nutritional status and sociodemographic and pregnancy characteristics among pregnant women in Colombia, Demographic and Health Survey (ENSIN) and National Nutritional Survey (ENDS), 2005

POR, prevalence odds ratio; Ref., referent category.

Overweight-related factors

In the bivariate analysis, adolescent pregnant women had lower odds of being overweight compared with adult pregnant women. Women who attended prenatal care, were multiparous and were in the third trimester of pregnancy had higher odds of being overweight than their comparison groups. In contrast, pregnant women living in extended families had lower odds of being overweight compared with pregnant women living in nuclear families (Table 3).

In the multivariate analysis, associations between overweight and being an adolescent, living in an extended family, reporting prenatal care during pregnancy and multiparity remained statistically significant (Table 4).

Discussion

Our findings provide evidence of the double burden within the pregnant population. Adolescents were more likely to be underweight and adults were more likely to be overweight. Overall, our results indicate that only about half of all pregnant women in Colombia had normal gestational BMI. Low gestational BMI was present in one-fifth of pregnant women, most of whom were adolescents. One-third of pregnant women, most of whom were adult women, were overweight.

In developing countries the nutritional transition and the double burden of nutrition are well recognized(Reference Uauy, Albala and Kain1, Reference Doak, Adair and Bentley3, Reference Popkin and Gordon-Larsen49). Among Latin American women there is evidence of an increase in the prevalence of overweight, which in many countries has exceeded 30 %(Reference Barria and Amigo50). This pattern of overweight coexists with a tendency to diminish the prevalence of underweight(Reference Barria and Amigo50). Our analysis of pregnant women provides, in part, evidence of this pattern. We found that the population in the lowest WI quintile had the highest prevalence of underweight, but that there were no differences in overweight by WI quintiles. In fact, among the pregnant population, in the lowest WI quintile 22 % were underweight and 31 % were overweight, while in the highest WI quintile 15 % were underweight and 36 % were overweight. In addition, we did not find the consistent differences seen in nutritional status between rural and urban population in other low- and middle-income countries(Reference Ha do, Feskens and Deurenberg51). This pattern could be the reflection of entering a later stage of nutritional transition, in which changes in diet and physical activity behaviours are leading to overweight and obesity(Reference Popkin and Gordon-Larsen49). Future studies assessing physical activity patterns and food consumption among pregnant women will help us to discern the differences in nutritional transition observed among pregnant Colombian women.

It is concerning that the prevalence of underweight is higher within the poorest and youngest population. In fact, 53 % of adolescents were in the lowest WI quintile. In addition, about one out of five adolescents in the lowest WI quintile were underweight, compared with one out of seven adolescents in the highest WI quintile. This finding illustrates that the strong association between poverty and undernutrition persists in Colombia. Consistent with other developing countries, in Colombia poverty is one of the main factors associated with maternal undernutrition(Reference Benjumea44, Reference Muller and Krawinkel52). In Colombia, despite public and private efforts, the tendency towards teenage pregnancy accompanied by low gestational BMI has not changed and the proportion of pregnant adolescents has even increased, from 13 % in 1990 to 22 % in 2005(29). It is also important to underscore that our analysis showed that 22·5 % of the adolescents in our sample have already had at least one pregnancy. Undernutrition and multiple pregnancies could both increase the probability of giving birth to low-birth-weight infants, thus creating an intergenerational effect on nutritional status and a transgenerational cycle of undernutrition and poverty(Reference Black, Allen and Bhutta6, Reference Muller and Krawinkel52).

In addition, in the present study undernutrition was also reflected in the measurements of Hb and SF which showed that 44·7 % of the women had anaemia. These estimates, however, did not differ between adolescents and pregnant women. This significant nutritional deficiency is consistent with the prevalence of anaemia during pregnancy in non-industrialized countries(11, Reference Stoltzfus53) and reflects the enduring problem of nutritional deficiencies in Colombia. Programmes of supplementation with multiple micronutrients during pregnancy may be a reasonable public health strategy.

In contrast, we found that overweight and obesity were more prevalent among adult women. These prevalences are within the lower bound of overweight and obesity prevalences reported by the WHO in other Latin American countries (Bolivia: 49·7 %; Brazil: 43·0 %; Chile: 57·7 %; Dominican Republic: 38·3 %; Peru: 43·4 %)(34). We also found that multiparous females were more likely to be overweight. Weight increases gradually throughout adult life, and some studies have shown that excessive gestational weight gain is associated with postpartum weight retention and failure to lose pregnancy-related weight, both important predictors of obesity in midlife(Reference Gore, Brown and West37). Overweight was also associated with living in extended families, which could be a reflection of food redistribution within families and should be evaluated in future studies.

The double burden of undernutrition among adolescents and overweight among women indicates the need to design and implement health and educational policies that promote healthy diets and physical activity during pregnancy. Therefore, it is important to emphasize the need for direct interventions to prevent excess or low gestational weight, to promote and maintain adequate pre-pregnancy healthy weight and to guarantee an adequate nutritional status during pregnancy in order to break the intergenerational transmission of unwanted health outcomes(Reference Reece24, Reference Oken54).

In this context, we underscore the potential impact and relevance of nutritional programmes within the Colombian National Policy on Sexual and Reproductive Health, and the National Food and Nutritional Plan, which address malnutrition problems. In the private sector, widespread national programmes that include family planning and prenatal care health services offered by Profamilia could be determinant in preventing malnutrition outcomes in the pregnant population. In the public sector, the Colombian Institute for Family Welfare has maternal and child health and nutritional programmes, including day-care centres, nutritional programmes for pregnant and lactating women, and infant growth and development monitoring(28, Reference Siega-Riz, Siega-Riz and Laraia55). Unfortunately, despite indications that overweight has increased, even exceeding underweight among women over the last decades, public health policies and programmes targeting pregnant women to date have continued to focus mainly on reducing undernutrition(Reference Atalah and Castro33, Reference Atalah, Castillo and Gomez56).

As our research design was intended primarily to generate hypotheses, our findings should be interpreted with the following limitations. The study addressed neither pre-pregnancy weight nor weight gain during pregnancy, because of the cross-sectional design with only one anthropometric measurement. Nutritional status was evaluated using BMI based on weight and height measurements and the Atalah algorithm. However, this algorithm is being recommended for national surveys in Latin America(Reference Atalah, Castillo and Castro32). Additionally, we did not assess food intake. We also found that prenatal care was positively associated with gestational overweight, which could be due to the cohort effect given the cross-sectional design of the study or the fact that our variable only reflected attending at least one prenatal care appointment. Therefore, longitudinal research is needed on whether our finding is related to health-care access and quality.

Despite the limitations of the study, it provides new evidence of the double burden as an illustration of the epidemiological and nutritional transition among pregnant women from a middle-income country in Latin America. Although preventing low gestational weight is an urgent matter, preventing overweight during pregnancy is also crucial because of the complications of obesity during pregnancy(Reference Black, Allen and Bhutta6, Reference Leddy, Power and Schulkin22, Reference Poston, Harthoorn and Van Der Beek26). Public health policies and efforts towards preventing underweight in pregnant adolescents and overweight in pregnant adult women should be reinforced.

Acknowledgements

The Demographic and Health Survey of Colombia 2005 was funded by the US Agency for International Development (USAID), the United Nations Population Fund (UNFPA), the Colombian Institute of Family Welfare (ICBF) and Colombia's Ministry of Social Protection. O.L.S., A.C.O., P.L.P. and A.R. received funding from the Epidemiology Group at Universidad de los Andes 1808. The authors declare that they have no conflict of interests. All authors were involved with data interpretation, critical revisions of the paper and provided approval for its publication.

References

1.Uauy, R, Albala, C & Kain, J (2001) Obesity trends in Latin America: transiting from under- to overweight. J Nutr 131, issue 3, 893S899S.CrossRefGoogle ScholarPubMed
2.Swinburn, BA, Sacks, G, Hall, KD et al. (2011) The global obesity pandemic: shaped by global drivers and local environments. Lancet 378, 804814.CrossRefGoogle ScholarPubMed
3.Doak, CM, Adair, LS, Bentley, M et al. (2005) The dual burden household and the nutrition transition paradox. Int J Obes (Lond) 29, 129136.CrossRefGoogle ScholarPubMed
4.Mendez, MA, Monteiro, CA & Popkin, BM (2005) Overweight exceeds underweight among women in most developing countries. Am J Clin Nutr 81, 714721.CrossRefGoogle ScholarPubMed
5.Abenhaim, HA, Kinch, RA, Morin, L et al. (2007) Effect of prepregnancy body mass index categories on obstetrical and neonatal outcomes. Arch Gynecol Obstet 275, 3943.Google Scholar
6.Black, RE, Allen, LH, Bhutta, ZA et al. (2008) Maternal and child undernutrition: global and regional exposures and health consequences. Lancet 371, 243260.Google Scholar
7.Ramakrishnan, U (2004) Nutrition and low birth weight: from research to practice. Am J Clin Nutr 79, 1721.CrossRefGoogle ScholarPubMed
8.Davies, AA, Smith, GD, May, MT et al. (2006) Association between birth weight and blood pressure is robust, amplifies with age, and may be underestimated. Hypertension 48, 431436.CrossRefGoogle ScholarPubMed
9.Haider, BA, Yakoob, MY & Bhutta, ZA (2011) Effect of multiple micronutrient supplementation during pregnancy on maternal and birth outcomes. BMC Public Health 11, Suppl. 3, S19.CrossRefGoogle ScholarPubMed
10.Phillips, DI, Barker, DJ, Hales, CN et al. (1994) Thinness at birth and insulin resistance in adult life. Diabetologia 37, 150154.CrossRefGoogle ScholarPubMed
11.World Health Organization/UNICEF/United Nations University (2001) Iron Deficiency Anaemia. Assessment, Prevention and Control. A Guide for Programme Managers. Geneva: WHO; available at http://www.who.int/nutrition/publications/en/ida_assessment_prevention_control.pdfGoogle Scholar
12.Lee, AI & Okam, MM (2011) Anemia in pregnancy. Hematol Oncol Clin North Am 25, 241259.CrossRefGoogle ScholarPubMed
13.Yogev, Y & Catalano, PM (2009) Pregnancy and obesity. Obstet Gynecol Clin North Am 36, 285300.Google Scholar
14.Linne, Y (2004) Effects of obesity on women's reproduction and complications during pregnancy. Obes Rev 5, 137143.CrossRefGoogle ScholarPubMed
15.Martorell, R, Khan, LK, Hughes, ML et al. (2000) Obesity in women from developing countries. Eur J Clin Nutr 54, 247252.CrossRefGoogle ScholarPubMed
16. Van EP (2011) Obesity in pregnancy. S D Med Spec No: 46-50.Google Scholar
17.Ehrenberg, HM, Dierker, L, Milluzzi, C et al. (2002) Prevalence of maternal obesity in an urban center. Am J Obstet Gynecol 187, 11891193.CrossRefGoogle Scholar
18.LaCoursiere, DY, Bloebaum, L, Duncan, JD et al. (2005) Population-based trends and correlates of maternal overweight and obesity, Utah 1991–2001. Am J Obstet Gynecol 192, 832839.CrossRefGoogle ScholarPubMed
19.Santos, IS, Barros, AJ, Matijasevich, A et al. (2008) Mothers and their pregnancies: a comparison of three population-based cohorts in Southern Brazil. Cad Saude Publica 24, Suppl. 3, S381S389.CrossRefGoogle ScholarPubMed
20.Rayis, DA, Abbaker, AO, Salih, Y et al. (2010) Epidemiology of underweight and overweight-obesity among term pregnant Sudanese women. BMC Res Notes 3, 327.CrossRefGoogle ScholarPubMed
21.Villamor, E, Msamanga, G, Urassa, W et al. (2006) Trends in obesity, underweight, and wasting among women attending prenatal clinics in urban Tanzania, 1995–2004. Am J Clin Nutr 83, 13871394.CrossRefGoogle ScholarPubMed
22.Leddy, MA, Power, ML & Schulkin, J (2008) The impact of maternal obesity on maternal and fetal health. Rev Obstet Gynecol 1, 170178.Google Scholar
23.Siega-Riz, AM & King, JC (2009) Position of the American Dietetic Association and American Society for Nutrition: obesity, reproduction, and pregnancy outcomes. J Am Diet Assoc 109, 918927.Google ScholarPubMed
24.Reece, EA (2008) Perspectives on obesity, pregnancy and birth outcomes in the United States: the scope of the problem. Am J Obstet Gynecol 198, 2327.Google Scholar
25.Anderson, JL, Waller, DK, Canfield, MA et al. (2005) Maternal obesity, gestational diabetes, and central nervous system birth defects. Epidemiology 16, 8792.Google Scholar
26.Poston, L, Harthoorn, LF & Van Der Beek, EM (2011) Obesity in pregnancy: implications for the mother and lifelong health of the child. A consensus statement. Pediatr Res 69, 175180.Google Scholar
27.Seligman, LC, Duncan, BB, Branchtein, L et al. (2006) Obesity and gestational weight gain: cesarean delivery and labor complications. Rev Saude Publica 40, 457465.CrossRefGoogle ScholarPubMed
28.Profamilia (2005) Encuesta Nacional en Demografía y Salud 2005 (ENDS 2005) – Resultados Generales. http://www.profamilia.org.co/encuestas/01encuestas/2005resultados_generales.htm (accessed December 2011).Google Scholar
29.Departamento Administrativo Nacional de Estadística (2005) Censo General, Colombia 2005. Bogotá: DANE; available at http://www.dane.gov.co/censo/Google Scholar
31.Demographic and Health Survey (2002) DHS and World Bank Use Wealth Index to Measure Socioeconomic Status. DHS+ Dimensions 4, issue 2; available at http://www.measuredhs.com/pubs/pdf/NL42/Vol4no2.pdfGoogle Scholar
32.Atalah, E, Castillo, C, Castro, R et al. (1997) Proposal of a new standard for the nutritional assessment of pregnant women. Rev Med Chil 125, 14291436.Google ScholarPubMed
33.Atalah, E & Castro, R (2004) Maternal obesity and reproductive risk. Rev Med Chil 132, 923930.Google Scholar
34.World Health Organization (2006) Global Databases on Body Mass Index. WHO: Geneva; available at http://apps.who.int/bmi/index.jspGoogle Scholar
35.Billewicz, W & Thomson, A (1957) Clinical significance of weight trends during pregnancy. Br Med J 1, 243247.Google ScholarPubMed
36.Gale, CR, Javaid, MK, Robinson, SM et al. (2007) Maternal size in pregnancy and body composition in children. J Clin Endocrinol Metab 92, 39043911.Google Scholar
37.Gore, SA, Brown, DM & West, DS (2003) The role of postpartum weight retention in obesity among women: a review of the evidence. Ann Behav Med 26, 149159.Google Scholar
38.Leddy, MA, Power, ML & Schulkin, J (2008) The impact of maternal obesity on maternal and fetal health. Rev Obstet Gynecol 1, 170178.Google Scholar
39.Grandi, C, Luchtenberg, G & Sola, H (2007) Nutrition assessment during pregnancy. A new weight chart. Medicina (B Aires) 67, 677684.Google Scholar
40.Rached-Paoli, I, Henriquez-Perez, G & Azuaje-Sanchez, A (2005) Effectiveness of body mass index in the nutritional diagnosis of pregnant women. Arch Latinoam Nutr 55, 4246.Google Scholar
41.Atalah, E, Rosales, E, Barja, I et al. (1980) Maternal nutrition and fetal growth: Chilean perspectives. Rev Med Chil 108, 351357.Google ScholarPubMed
42.Rached-Paoli, I & Henriquez-Perez, G (2010) Effectiveness of the body mass index in the nutritional diagnosis of pregnant adolescents. Arch Latinoam Nutr 60, 141147.Google ScholarPubMed
43.Mardones, F & Rosso, P (1997) Design of a weight gain chart for pregnant women. Rev Med Chil 125, 14371448.Google ScholarPubMed
44.Benjumea, MV (2007) Diagnostic accuracy of five gestational references to predict insufficient birth weight. Biomedica 27, 4255.CrossRefGoogle ScholarPubMed
45.World Health Organization (2007) Adolescent Pregnancy. Unmet Needs and Undone Deeds. A Review of the Literature and Programmes. WHO: Geneva.Google Scholar
46.International Anemia Consultative Group (2002) Adjusting Hemoglobin Values in Program Surveys. Washington, DC: INACG Secretariat; available at http://pdf.usaid.gov/pdf_docs/PNACQ927.pdfGoogle Scholar
47.Ciok, J & Leibschang, J (1999) The report of UNICEF/WHO Joint Consultation on iron deficiency anemia, Geneva, February 3–5, 1999. Ginekol Pol 70, 573577.Google ScholarPubMed
48.Sullivan, KM, Mei, Z, Grummer-Strawn, L et al. (2008) Haemoglobin adjustments to define anaemia. Trop Med Int Health 13, 12671271.Google Scholar
49.Popkin, BM & Gordon-Larsen, P (2004) The nutrition transition: worldwide obesity dynamics and their determinants. Int J Obes Relat Metab Disord 28, Suppl. 3, S2S9.CrossRefGoogle ScholarPubMed
50.Barria, RM & Amigo, H (2006) Nutrition transition: a review of Latin American profile. Arch Latinoam Nutr 56, 311.Google ScholarPubMed
51.Ha do, TP, Feskens, EJ, Deurenberg, P et al. (2011) Nationwide shifts in the double burden of overweight and underweight in Vietnamese adults in 2000 and 2005: two national nutrition surveys. BMC Public Health 11, 62.Google ScholarPubMed
52.Muller, O & Krawinkel, M (2005) Malnutrition and health in developing countries. CMAJ 173, 279286.Google Scholar
53.Stoltzfus, RJ (2003) Iron deficiency: global prevalence and consequences. Food Nutr Bull 24, 4 Suppl., S99S103.CrossRefGoogle Scholar
54.Oken, E (2009) Maternal and child obesity: the causal link. Obstet Gynecol Clin North Am 36, 361377.Google Scholar
55.Siega-Riz, AM, Siega-Riz, AM & Laraia, B (2006) The implications of maternal overweight and obesity on the course of pregnancy and birth outcomes. Matern Child Health J 10, 5 Suppl., S153S156.CrossRefGoogle ScholarPubMed
56.Atalah, E, Castillo, C, Gomez, C et al. (1995) Malnutrition of the pregnant woman: an overestimated problem? Rev Med Chil 123, 15311538.Google ScholarPubMed
Figure 0

Table 1 Sociodemographic and pregnancy characteristics among pregnant women in Colombia, Demographic and Health Survey (ENDS) and National Nutritional Survey (ENSIN), 2005

Figure 1

Table 2 Sociodemographic and pregnancy characteristics of pregnant adolescents and adult women in Colombia, Demographic and Health Survey (ENDS) and National Nutritional Survey (ENSIN), 2005

Figure 2

Fig. 1 Nutritional status by BMI among pregnant adolescents () and adult women () in Colombia, Demographic and Health Survey (ENDS) and National Nutritional Survey (ENSIN), 2005. Values are prevalence with 95 % confidence intervals represented by vertical bars

Figure 3

Table 3 Bivariate associations between nutritional status and sociodemographic and pregnancy characteristics among pregnant women in Colombia, Demographic and Health Survey (ENDS) and National Nutritional Survey (ENSIN), 2005

Figure 4

Table 4 Multivariate associations between nutritional status and sociodemographic and pregnancy characteristics among pregnant women in Colombia, Demographic and Health Survey (ENSIN) and National Nutritional Survey (ENDS), 2005