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Keywords:

  • low birthweight;
  • small-for-gestation;
  • preterm;
  • population attributable risk
  • faible poids de naissance;
  • petit poids pour l’âge gestationnel;
  • prématuré;
  • risque attribuable à population
  • bajo peso al nacer;
  • pequeño para la edad gestacional;
  • prematuro;
  • riesgo atribuible poblacional

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

Objective  To examine the determinants of low birthweight (LBW), small-for-gestation (SGA) and preterm births in Lombok, Indonesia, an area of high infant mortality.

Methods  Data from The Supplementation with Multiple Micronutrient Intervention Trial (SUMMIT), a double-blind cluster-randomised controlled trial, were analysed. The odds ratio of factors known to be associated with LBW, SGA and preterm birth was assessed and adjusted for the cluster design of the trial using hierarchical logistic regression. Determinants included constitutional, demographic and psychosocial factors, toxic exposure, maternal nutrition and obstetric history and maternal morbidity during and prior to pregnancy. Population attributable risks of modifiable determinants were calculated.

Results  A cohort of 14 040 singleton births was available for analysis of LBW, with 13 498 observations for preterm births and 13 461 for SGA births. Determinants of LBW and SGA were similar and included infant’s sex, woman’s education, season at birth, mothers’ residence, household wealth, maternal mid-upper arm circumference (MUAC), height and a composite variable of birth order and pregnancy interval. Socioeconomic indicators were also related to preterm births and included mother’s education, residence and household wealth, while nutritional-related factors including low MUAC and birth order and interval were associated with preterm birth but not maternal height. Nausea was protective of preterm birth, while diarrhoea was associated with higher odds of preterm birth. Oedema during pregnancy was protective of SGA but associated with higher odds of preterm delivery. Around 33%, 13% and 13% of the determinants of LBW, SGA and preterm births were preventable.

Conclusion  Women’s education, maternal nutrition and household wealth and family planning are key factors to improving birth outcomes.

Objectifs:  Examiner les déterminants du faible poids de naissance (FPN), du petit fœtus pour l’âge gestationnel (PFAG) et des naissances prématurées à Lombok, Indonésie, une zone à forte mortalité infantile.

Méthodes:  Les données de l’Etude d’Intervention par la Supplémentation en Multiples Micronutriments (SUMMIT), un essai contrôlé en double aveugle, randomisé en grappes, ont été analysées. Les rapports de cotes des facteurs connus pour être associés au FPN, au PFAG et à la naissance prématurée ont étéévalués et ajustés selon la conception de l’échantillon de l’essai, en utilisant la régression logistique hiérarchique. Les déterminants comprenaient des facteurs constitutionnels, démographiques et psychosociaux, l’exposition toxique, la nutrition maternelle, les antécédents obstétricaux et la morbidité maternelle avant et pendant la grossesse. Les risques attribuables à la population pour les déterminants modifiables ont été calculés.

Résultats:  Une cohorte de 14.040 naissances uniques était disponible pour l’analyse du FPN, avec 13.498 observations pour les naissances prématurées et 13.461 pour les naissances PFAG. Les déterminants du FPN et du PFAG étaient similaires et comprenaient le sexe du nourrisson, l’éducation de la femme, la saison de la naissance, la résidence des mères, la richesse des ménages, le périmètre brachial de la mère, la taille et une variable composite de l’ordre des naissances et des intervalles de grossesses. Des indicateurs socioéconomiques étaient également liés à des naissances prématurées et comprenaient l’éducation de la mère, la résidence et la richesse des ménages, tandis que des facteurs nutritionnels connexes comprenant le faible périmètre brachial, l’ordre et l’intervalle des naissances étaient associés à la prématurité, mais pas la taille maternelle. Les nausées avaient un effet protecteur pour la prématurité, alors que la diarrhée a été associée à un risque plus élevé de naissance prématurée. L’œdème pendant la grossesse avait un effet protecteur pour le PFAG, mais était associéà un risque plus élevé d’accouchement prématuré. Environ 33%, 13% et 13% respectivement des déterminants du FPN, du PFAG et des naissances prématurées étaient évitables.

Conclusion:  L’éducation des femmes, la nutrition maternelle, la richesse des ménages et la planification familiale sont les facteurs clés à l’amélioration des résultats de la naissance.

Objetivos:  Examinar los determinantes del nacimiento de neonatos con bajo peso al nacer (BPN), pequeños para la edad gestacional (PEG) y de partos prematuros en Lombok, Indonesia, un área con una alta mortalidad infantil.

Métodos:  Se analizaron datos del ensayo SUMMIT (The Supplementation with Multiple Micronutrient Intervention Trial), un ensayo doble ciego, aleatorizado en conglomerados y controlado. Se evaluó la razón de posibilidades de aquellos factores que se conoce están asociados con el BPN, PEG y los partos prematuros, y se ajustó para el diseño en conglomerados del ensayo utilizando una regresión logística jerárquica. Entre los determinantes incluidos había factores constitucionales, demográficos y psicosociales, exposición tóxica, nutrición materna e historia obstétrica, y morbilidad materna durante y antes del embarazo. Se calculó riesgo atribuible poblacional de los determinantes modificables.

Resultados:  Se dispuso de una cohorte de 14,040 partos únicos para el análisis de BPN, con 13,498 observaciones de nacimientos prematuros, y 13,461 neonatos PEG. Los determinantes de BPN y PEG eran similares, e incluían el sexo del recién nacido, la educación de la madre, la estación en la que se nacía, el lugar de residencia de la madre, el nivel de riqueza del hogar, la circunferencia de la parte superior del brazo de la madre (CSBM), la altura y una variable compuesta con el orden de nacimiento e intervalo de embarazos. Los indicadores socioeconómicos también estaban relacionados con los nacimientos prematuros, e incluían la educación de la madre, el lugar de residencia y el nivel de riqueza, mientras que los factores nutricionales relacionados incluían una CSBM pequeña, y el orden de nacimiento e intervalo, estaban asociados con un nacimiento prematuro, pero no la altura materna. Las nauseas protegían frente a un nacimiento prematuro, mientras que la diarrea estaba asociada a una mayor probabilidad de un parto prematuro. La presencia de edema durante el embarazo protegía de PEG, pero estaba asociada con una mayor probabilidad de un parto prematuro. Alrededor de un 33%, 13% y 13% de los determinantes de BPN, PEG, y parto prematuro respectivamente, eran prevenibles.

Conclusión:  La educación de la mujer, la nutrición materna, la riqueza del hogar y la planeación familiar son factores claves para mejorar los resultados del parto.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

Birthweight and duration of gestation are important indicators to predict the future health and survival of newborns. Globally, 60–80% of neonatal deaths occur among low birthweight (LBW) infants (Lawn et al. 2005), and the contribution of preterm birth to early neonatal and neonatal deaths is estimated to be 61% (Ngoc et al. 2006) and 75%, respectively (Yasmin et al. 2001). Prevalence of LBW in Indonesia is around 9–11% (Badan Penelitian dan Pengembangan Kesehatan 2010; Requejo et al. 2010) with no noticeable reduction over several years; around 13% of infants were born smaller than average (Badan Penelitian dan Pengembangan Kesehatan 2008). Reliable estimates for prematurity in Indonesia are not available, but an estimated 33 258 infants in Indonesia died owing to complications of preterm birth (Black et al. 2010), which would account for 41% of all neonatal deaths in the country (Requejo et al. 2010).

Low birthweight may result from foetal growth retardation, short duration of gestation or a combination of both. Therefore, determinants of LBW may differ between populations depending on whether LBW is mostly due to growth restriction or prematurity. Knowledge on the determinants of LBW, SGA and preterm births is important for developing effective health policies to reduce infant mortality. Several determinants of LBW (Kramer 1987), SGA (Zambonato et al. 2004; Panaretto et al. 2006) and preterm births (Goldenberg et al. 2008; Chan & Lao 2009) have been reported. LBW and SGA were mostly associated with maternal nutritional status, socioeconomic status, infection and general morbidity, while for preterm delivery, intrauterine infection was also an important mechanism. However, research on the determinants of LBW, SGA and preterm births in Indonesia is sparse. This article examines the determinants of LBW, SGA and preterm births and identifies key modifiable risk factors for Lombok, an island with one of the highest infant mortality levels in Indonesia. Specifically, the analyses aim to identify the relative importance of a variety of maternal, health services and environmental factors on the risk of LBW, SGA and preterm birth and how the related factors vary across these different birth outcomes.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

Data used for these analyses were from The Supplementation with Multiple Micronutrient Intervention Trial (SUMMIT), a cluster-randomised controlled trial (ISRCTN34151616) conducted in Lombok, Indonesia, in 2001–2004. The SUMMIT covered West, Central and East Lombok districts, and the rainy season in Lombok was generally from November to March. Midwives were randomised to give either multiple micronutrient (MMN) or iron/folic acid supplements (IFA) to women who self-reported their pregnancies at any gestation and consented to enrol in the trial.

Pre-supplementation data were taken within 72 h of enrolment in the SUMMIT by trained maternal data collectors (MDCs). These data included the first date of the last menstrual period (LMP), pregnancy and health history, complications during pregnancy, socioeconomic information and maternal anthropometry. Maternal mid-upper arm circumference (MUAC) was measured using a non-stretchable UNICEF arm circumference insertion tape, height was measured with a microtoise (UNICEF No. 0114400 Height measuring instrument), and weight measurement was taken with a Uniscale (UNICEF Supply Division, Copenhagen, Denmark). Whenever possible, birthweight was recorded by a trained community facilitator, an MDCs, or a midwife within 1 h of delivery, using a Uniscale or infant digital scale (Tanita model BD585; Tanita Corporation, Tokyo, Japan, or Seca model 334; Seca Corporation, Hamburg, Germany). A complete description of the SUMMIT methods is available elsewhere (SUMMIT Study Group et al. 2008).

Data were selected with the following criteria: singleton live birth with birthweight measured within 72 h of birth using Uniscale or infant digital scale; and measurement was taken by a trained SUMMIT staff member or a midwife. For the analysis, we adapted a framework of major divisions of risk factors for LBW (Kramer 1987), which included six major factors: constitutional, demographic and psychosocial, toxic exposure, maternal nutrition and obstetric history, maternal morbidity during pregnancy and maternal history of morbidity.

Statistical analysis

All the information included in the analysis was taken at enrolment. Based on the women’s recall, we collected information about previous pregnancy outcomes, birth interval, anti-malaria drug consumption, history of pre-pregnancy morbidity and morbidity during pregnancy, together with anaemia. Gestation was calculated from the LMP. Pregnancy interval was estimated by subtracting the year of the last pregnancy and the year of the LMP of the current pregnancy. Wealth index was calculated using principal component analysis (PCA) of 12 household assets (Filmer & Pritchett 1998): the floor area of the house, ownership of bicycle, boat, car, horse cart, kiosk, refrigerator, vendor, house, motorbike, television set and radio. The score obtained from the PCA was used as a continuous variable in the model.

There was some spiking of birthweight values at 500 g intervals. Hence, we defined LBW as birthweight ≤2500 g as it better approximated the area of the Gaussian curve and kernel density plots of birthweight distribution compared to using a cut-off of <2500 g. SGA as a measure of IUGR was defined as birthweight <10th centile of the gestational age- and sex-specific US reference for foetal growth (Alexander et al. 1996) as there was no gestational age-specific foetal growth available for Indonesian or Asian babies. Infants born at <37 weeks of gestation were categorised as preterm. In the analysis of SGA and preterm delivery, data with implausible gestation were excluded using a previously described method (Fall et al. 2009).

Pregnancy interval was combined with parity into a composite variable of birth order and interval following previously used categories (Titaley et al. 2008). MUAC, rather than BMI, was selected to represent nutritional status as it was independent of gestation (WHO 1995). When wealth index, height or MUAC was selected in the final model, categorical variables were used for ease of interpretation. Wealth was made into quintiles, short stature was defined as height <145 cm, and low MUAC was defined as MUAC <23.5 cm. We combined MUAC and height into a single determinant with four categories: thin only (MUAC < 23.5 cm and height ≥145 cm), short only (height <145 cm and MUAC ≥ 23.5 cm), thin and short (MUAC < 23.5 cm and height <145 cm) and neither thin nor short (MUAC ≥ 23.5 cm and height ≥145 cm). Primiparous women were considered to have no prior history of miscarriage, stillbirth and child mortality, and birth interval from the last pregnancy was set at zero for these women.

Variables with the proportion of those at risk <1% were excluded from the analysis to reduce the probability of chance findings. Only variables with P-values <0.25 in the univariate analysis were initially included in the multivariate analysis. Backward elimination of non-significant variables (P ≥ 0.05) was then used to select variables for the final models. We hypothesised possible interactions between types of supplement and maternal nutritional status (BMI, MUAC and height), as well as between nutritional status and wealth index. We tested for interactions only when these suspected factors were retained in the final model. The predefined criterion for significant effect modifiers was P < 0.01. P-values were calculated using likelihood ratio tests or Wald tests. The odds ratio and 95% confidence interval (CI) for LBW, SGA and preterm birth were calculated and adjusted for the cluster design of the trial, by hierarchical logistic regression with SAS PROC GENMOD.

We selected two modifiable determinants of each outcome, woman’s education and nutritional status (MUAC or MUAC and height). For LBW, preterm birth was also considered a modifiable determinant. Household wealth and birth orders would be difficult to modify and therefore were included as social factors.

Partial population attributable risks (PAR) was calculated using a statistical program specific for cohort studies (Spiegelman et al. 2007). Fixed variance-covariance matrix from PROC GENMOD was used in the calculation. For this analysis, woman’s education was categorised (≤6 or >6 years).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

There were 14 040 observations analysed for the LBW outcome, and 13 498 observations for the preterm outcome, after excluding data with implausible or missing values for gestation. For the SGA analysis, an additional 37 observations were excluded owing to unknown infant sex. Table 1 shows the characteristics of the women in the study. Similar patterns were seen in the smaller subsets of data (data not shown). Most women in the study had some primary education but their husbands had a slightly longer duration of education (Table 1). The proportions of women who smoked, used betel, alcohol or traditional medicine were very low (<1%). There were no LBW and SGA babies born to the group of women consuming alcohol, and no LBW babies in women consuming traditional medicine, therefore odds ratio could not be estimated for these outcomes. None of the toxic exposure factors were included in the final model for any of the outcomes. About half of the study population received MMN and the other half received IFA.

Table 1.   Population characteristics of the study cohort
VariablesTotal N N Proportion (%) or Mean ± SD% LBW
Overall 14040 9.9
Genetic and constitutional
 Male Infant14003731052.28.8
Demographic and psychosocial
 Woman’s age (years)13666 25.4 ± 5.9 
 Man’s age (years)13032 30.4 ± 7.3 
 Woman’s education (years)13837 6.9 ± 3.6 
 Man’s education (years)13195 7.6 ± 3.9 
 Born in rainy season14040687749.010.5
 Region14040   
  East 733452.29.2
  West 348824.810.3
  Central 321822.910.9
 Wealth index13726   
  Very rich 274220.07.5
  Rich 274920.09.3
  Middle 276120.110.6
  Poor 271319.811.1
  Very poor 276120.111.2
 Woman herself decided health seeking13985547439.19.9
 Woman’s income higher than husband’s137285413.910.7
 Having polygamous husband138293312.49.9
Toxic exposure
 Smoking13860200.120.0
 Consume betel13873930.77.5
 Drink Alcohol1387820.010.0
 Taking traditional medicine2672250.90.0
Nutrition and obstetric
 Consumed multiple micronutrients14040715851.09.5
 BMI at enrolment11612 22.4 ± 3.0 
 Mid-upper arm circumference12281 246.5 ± 25.4 
 Maternal Height13303 150.0 ± 4.9 
 MUAC and Stature11600   
  MUAC ≥ 23.5 cm and height ≥145 cm 693459.787.47
  MUAC < 23.5 cm and height ≥145 cm 304326.2312.06
  MUAC ≥ 23.5 cm and height <145 cm 9568.2413.6
  MUAC < 23.5 cm and height < 145 cm 6675.7520.24
 Gestational duration14024 38.8 ± 2.9 
 Prior miscarriage1389413589.88.5
 Prior stillbirth138941911.47.3
 Stillbirth in the last pregnancy13894950.710.5
 Prior child mortality13894223816.19.6
 Birth rank and interval13883   
  2nd or 3rd birth rank, birth interval >2 years4323432331.16.9
  1st birth rank5315531538.313.3
  2nd or 3rd birth rank, birth interval ≤2 years1724172412.49.7
  ≥4th birth rank, birth interval >2 years1827182713.28.2
  ≥4th birth rank, birth interval ≤2 years6946945.07.6
Maternal morbidity during pregnancy
 Nausea138911114580.29.7
 Vomiting13887871362.79.7
 Diarrhoea1388710797.810.8
 Gastric irritation13886302021.710.1
 Phlegm13886222616.011.3
 Coughing blood138861190.95.9
 Breathlessness1388511458.210.4
 Fever13882383627.611.2
 Difficulty urinating138867555.410.1
 Pain when urinating1388710887.811.0
 Headache13889984970.910.1
 Vision night blindness1388511518.310.0
 Weakness13886673148.59.5
 Anaemia13862238817.210.5
 Oedema138873452.58.1
 Seizure13887810.69.9
 Hypertension138841411.07.8
 Taking anti-malarial treatment13876670.59.0
History of maternal morbidity
 Breathlessness13886186613.411.2
 Difficulty urinating13878226516.310.1
 Pain when urinating13891318322.910.2
 Weakness13888617944.59.8
 Anaemia13873439831.710.2
 Hepatitis138905433.910.5
 Heart disease138882181.610.1
 Epilepsy13883640.515.6
 Diabetes138881481.110.1
 Hypertension138853322.49.0
 Asthma138842571.810.1

Determinants of low birthweight

In the final model, being a male infant was protective of LBW (Table 2). Neither the age of the woman nor her husband was a significant predictor of LBW. Women with some high school education (≥10 years of education) were associated with 13% lower odds of having LBW (OR: 0.87; 95% CI: 0.70–1.08, P = 0.20) than women with no primary education. The women’s and her husband’s education can replace each other as a predictor of LBW, both with a 2% reduction in the odds of LBW with each year increase in education. Therefore, the woman’s education was retained in the final model as it was deemed a more useful marker for programme implementation instead of her husband’s education.

Table 2.   Determinants of low birthweight
DeterminantsOR95% confidence interval P-value*Adjusted OR (n = 11378)95% confidence interval P-value*
  1. *Calculated from likelihood ratio tests, except for the testing of individual levels with the reference group for categorical variables, which was calculated using Wald tests.

Male infant0.780.70–0.88<0.00010.760.67–0.86<0.0001
Woman’s age (years)0.960.95–0.97<0.0001   
Husband’s age (years)0.970.96–0.98<0.0001   
Woman’s education (years)0.980.97–1.000.020.980.96–1.000.02
Man’s education0.970.96–0.98<0.0001   
Born in rainy season1.161.03–1.300.011.221.07–1.390.001
Region  0.01  0.005
 EastRef  Ref  
 West1.190.99–1.430.071.211.00–1.480.054
 Central1.221.04–1.440.021.251.05–1.500.01
Wealth Index  <0.0001  0.0003
 Very richRef  Ref  
 Rich1.261.03–1.530.021.251.00–1.570.05
 Middle1.471.20–1.790.00021.401.11–1.770.005
 Poor1.531.27–1.85<0.00011.321.06–1.650.01
 Very Poor1.571.30–1.89<0.00011.441.16–1.790.0008
Consumed multiple micronutrients0.930.81–1.080.10   
Maternal MUAC <235 mm1.741.57–1.93<0.00011.471.31–1.65<0.0001
Maternal Height <145 cm1.931.70–2.20<0.00011.931.67–2.22<0.0001
Duration of Gestation (weeks)0.880.87–0.90<0.00010.880.87–0.90<0.0001
Prior miscarriage0.820.68–0.990.06   
Prior stillbirth0.700.39–1.270.21   
Birth order and interval     <0.0001
 2nd or 3rd birth rank, birth interval >2 yearsRef  Ref  
 1st birth rank2.071.82–2.36<0.00011.951.66–2.29<0.0001
 2nd or 3rd birth rank, birth interval ≤2 years1.451.21–1.74<0.00011.361.09–1.700.006
 ≥4th birth rank, birth interval >2 years1.211.01–1.460.041.120.91–1.380.28
 ≥4th birth rank, birth interval ≤2 years1.120.84–1.480.450.950.70–1.300.74
Nausea during pregnancy0.890.78–1.010.08   
Phlegm during pregnancy1.181.04–1.360.02   
Fever during pregnancy1.221.09–1.360.001   
Pain when urinating in pregnancy1.120.92–1.380.20   
Headache during pregnancy1.070.94–1.220.22   
Weakness during pregnancy0.920.82–1.030.15   
History of breathlessness1.160.98–1.380.05   

After adjusting for other factors, there were 22% increased odds of LBW in babies born in the rainy season. Women in West Lombok had 21% higher odds of having LBW babies than women in East Lombok. Women in West Lombok had 25% higher odds of delivering LBW babies, but these effects were not statistically significant. There was a steady reduction in the odds of delivering a LBW baby with increasing wealth. Compared to the very rich families, babies from the poor and very poor families had 32% and 44% higher odds of being born LBW.

In the final model, women with low MUAC had 47% higher odds of having LBW babies (OR = 1.47; 95% CI: 1.31–1.65, P < 0.0001). Short women had almost twice the odds of having LBW babies (OR = 1.93; 95% CI: 1.67–2.22, P < 0.0001). The combination of MUAC and stature showed that women with MUAC ≥ 23.5 cm and height ≥145 cm had the lowest odds of LBW, while being thin (MUAC < 23.5 cm) or short (height < 145 cm), or both increased the likelihood of having a LBW baby. Thin and short women had almost triple the odds of delivering a LBW baby (OR = 2.80, 95% CI: 2.30–3.41, P < 0.0001).

Allowing for other factors, for each week increase in duration of gestation, the odds of LBW were significantly reduced by 12%. The composite variable of birth order and interval was a significant predictor of LBW. Compared to the second or third baby with interval of >2 years between pregnancies, the first child had 95% greater odds of being born LBW. There were no interactions between wealth index and MUAC nor wealth index and height in the LBW models.

Determinants of small-for-gestation

After adjusting for other factors, male infants had an increased risk of SGA. Neither the age of the woman nor her husband was a significant predictor of SGA. Women with some high school education (≥10 years of education) had 12% lower odds of having SGA babies (OR = 0.88; 95% CI: 0.73–1.07, P = 0.20) than women with no primary education. Women’s education was associated with 2% reduced odds of SGA births for each year in education (Table 3).

Table 3.   Determinants of small-for-gestational-age
DeterminantsOR95% confidence interval P-value*Adjusted OR (n = 10964)95% confidence interval P-value*
  1. *Calculated from likelihood ratio tests, except for the testing of individual levels with the reference group for categorical variables, which was calculated using Wald tests.

Male infant1.171.07–1.290.00091.191.08–1.320.001
Woman’s age (years)0.960.96–0.97<0.0001   
Husband’s age (years)0.980.97–0.98<0.0001   
Woman’s education (years)0.990.98–1.000.080.980.96–0.990.005
Husband’s education0.980.97–1.000.01   
Born in Rainy Season1.131.02–1.250.0091.181.06–1.320.0008
Region  0.007  0.002
 East1.00  1.00  
 West1.070.92–1.250.381.120.95–0.380.19
 Central1.181.01–1.380.0321.231.03–1.470.03
Household Wealth  <0.0001  0.003
 Very rich1.00  1.00  
 Rich1.341.13–1.590.00091.361.11–1.670.003
 Middle1.421.21–1.67<0.00011.341.10–1.630.003
 Poor1.461.23–1.73<0.00011.341.09–1.650.006
 Very Poor1.451.23–1.71<0.00011.431.17–1.740.0004
Woman’s income higher than husband’s1.190.92–1.530.11   
Polygamy0.780.55–1.090.14   
Maternal MUAC <235 mm1.471.33–1.63<0.00011.281.14–1.43<0.0001
Height <145 cm1.871.67–2.10<0.00011.761.55–2.00<0.0001
Prior stillbirth0.760.49–1.200.25   
Prior child mortality0.850.75–0.980.02   
Birth order and interval  <0.0001  <0.0001
 2nd or 3rd birth rank, birth interval >2 years1.00  1.00  
 1st birth rank1.871.66–2.10<0.00011.871.64–2.14<0.0001
 2nd or 3rd birth rank, birth interval ≤2 years1.181.01–1.390.041.190.99–1.440.06
 ≥4th birth rank, birth interval >2 years1.020.87–1.200.831.040.86–1.260.66
 ≥4th birth rank, birth interval ≤2 years1.020.78–1.340.881.010.76–1.350.92
Diarrhoea during pregnancy1.160.97–1.400.06   
Fever during pregnancy1.111.01–1.220.04   
Oedema during pregnancy0.710.49–1.030.070.510.29–0.910.01
Hypertension during pregnancy0.710.44–1.160.22   
History of breathlessness1.110.97–1.260.11   
History of pain when urinating1.060.94–1.190.19   
History of heart disease0.630.43–0.920.030.600.37–0.970.03

Allowing for other factors, there were 18% increased odds of SGA in babies born in the rainy season. Women in West Lombok had 12% higher odds of delivering SGA babies than women in East Lombok. Women in West Lombok had 23% higher odds of having SGA babies but this effect was not statistically significant. Compared to the very rich families, babies from the poor and very poor families had 34% and 43% higher odds of being born SGA.

In the final model, women with low MUAC had 28% higher odds of having SGA (OR = 1.28; 95% CI: 1.14–1.43, P < 0.0001). Meanwhile, short women had 76% higher odds of having SGA babies (OR = 1.76, 95% CI: 1.55–2.00, P < 0.0001). The combination of MUAC and stature showed that women with MUAC ≥ 23.5 cm and height ≥145 cm had the lowest odds of SGA, while being thin (MUAC < 23.5 cm) or short (height < 145 cm), or both increased the likelihood of having SGA babies. Thin and short women had twice the likelihood of having SGA (OR = 2.14, 95% CI: 1.80–2.56, P < 0.0001). Compared to the second or third baby with interval of >2 years between pregnancies, the first child had 87% greater odds of being born SGA. There were no interactions between wealth index and MUAC nor wealth index and height in the SGA models. Oedema during pregnancy and history of heart disease were protective of SGA, after adjustment for other factors in the model.

Determinants of preterm births

No association was found between infant sex and preterm births or between age of the woman or her husband and preterm births. In the final model, women with some high school education (≥10 years of education) had 36% lower odds of having a preterm baby (OR = 0.64; 95% CI: 0.54–0.77, P < 0.0001) compared to women with no primary education. Women’s education was significantly associated with preterm birth with a 3% reduced odds for each year in education. There were reduced odds of preterm births with increasing wealth but categorisation of level of wealth reduced the P-value (Table 4).

Table 4.   Determinants of preterm births
DeterminantsOR95% confidence interval P-valueAdjusted OR (n = 11614)95% confidence interval P-value
  1. *Calculated from likelihood ratio tests, except for the testing of individual levels with the reference group for categorical variables, which was calculated using Wald tests.

Woman’s age (years)  0.01   
 <20Ref     
 20–240.910.81–1.030.13   
 25–290.850.74–0.970.02   
 30–340.890.77–1.030.11   
 ≥351.080.91–1.270.39   
Woman’s education (years)0.970.95–0.98<0.00010.970.95–0.98<0.0001
Husband’s education0.980.97–0.99<0.0001   
Born in rainy season0.940.87–1.010.14   
Region  0.02  0.17
 EastRef  Ref  
 West1.181.02–1.360.021.110.96–1.290.16
 Central1.120.98–1.270.091.100.95–1.270.20
Household Wealth  0.0004  0.13
 Very richRef  Ref  
 Rich1.080.95–1.230.241.060.92–1.230.39
 Middle1.181.03–1.350.021.140.98–1.330.09
 Poor1.281.12–1.460.00021.211.03–1.410.02
 Very poor1.281.10–1.480.0011.120.95–1.320.18
Women decided her own health seeking0.920.84–1.000.04   
Women’s income higher than husband’s1.150.93–1.430.21   
Consumed betel1.611.06–2.450.03   
Maternal MUAC <235 mm1.131.03–1.240.011.161.06–1.270.003
Maternal height (cm)1.011.00–1.020.21   
Prior miscarriage0.910.80–1.040.220.810.69–0.940.01
Prior child mortality1.080.98–1.200.16   
Birth order and interval  0.10  0.03
 2nd or 3rd birth rank, birth interval >2 yearsRef  Ref  
 1st birth rank1.050.96–1.150.291.020.92–1.140.66
 2nd or 3rd birth rank, birth interval ≤2 years1.120.98–1.290.101.231.07–1.420.004
 ≥4th birth rank, birth interval >2 years1.140.98–1.320.091.120.95–1.320.16
 ≥4th birth rank, birth interval ≤2 years1.231.03–1.470.021.291.06–1.560.009
Nausea during pregnancy0.820.74–0.91<0.00010.800.71–0.90<0.0001
Vomiting during pregnancy0.890.82–0.970.01   
Diarrhoea during pregnancy1.191.03–1.370.011.221.04–1.440.02
Gastric irritation during pregnancy0.920.84–1.000.09   
Weakness during pregnancy0.890.82–0.970.01   
Oedema during pregnancy1.401.10–1.780.011.401.00–1.960.03
History of Weakness0.870.80–0.950.0010.890.81–0.980.01
History of Anaemia0.910.84–0.990.06   

Allowing for other factors, women with low MUAC had 16% higher odds of having preterm babies (OR = 1.16; 95% CI: 1.06–1.27, P = 0.003). Previous miscarriage was a significant determinant in the final model of preterm births with 19% lower odds when the women had a history of miscarriage. The first born child did not have greater odds of being born preterm, but compared to the second or third baby with interval of >2 years, children with birth intervals ≤2 years had a significantly increased odds of preterm birth irrespective of the birth rank.

After adjusting for other variables in the model, nausea was significantly protective with 20% reduction in odds of preterm delivery. History of weakness was protective of preterm births but oedema during pregnancy had a higher risk. Estimates of these determinants remained the same when supplement type was included in the final model (data not shown).

Population attributable risks

The relative contribution of each factor was approximated in Figures 1–3 where the size of each slice is proportional to the partial PAR of each factor. The individual PAR for each factor of LBW and SGA added up to over 100% because the factors examined were not mutually exclusive. The full PAR for LBW, SGA and preterm models were estimated at 79% of the LBW cases, 89% of the SGA cases and 52% of the preterm cases, respectively. After normalising to a full 100% of PAR, around 33%, 13% and 13% of all PAR of LBW, SGA and preterm births, respectively, were preventable (Figures 1–3). When non-modifiable and social determinants were held fixed, interventions to increase women’s education to more than 6 years alone could reduce LBW cases by 8%, SGA by 6% and preterm births by 11%. Reducing risks of short women would reduce LBW and SGA cases by 11% and 10%, respectively, while reducing risks in low MUAC women would only reduce LBW, SGA and preterm cases by 14%, 8% and 4%. For social determinants, improving household wealth to reach the highest quintiles would reduce LBW, SGA and preterm cases by 23%, 24%, 11%, respectively. Eliminating risks in the first pregnancy, second and third pregnancy with short interval and all fourth or more pregnancies would reduce LBW, SGA and preterm cases by 30%, 27% and 6%, respectively. Eliminating risks in woman with all five determinants would reduce LBW and SGA cases by 61% and 56%, while eliminating risks in woman with four determinants would reduce preterm cases by 29% (Table 5).

image

Figure 1.  Relative contribution of modifiable, social factors and non-modifiable determinants with impacts on LBW. Social factors = birth order and interval, household wealth; Non-modifiable factors = infant sex, season, residence.

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image

Figure 2.  Relative contribution of modifiable, social factors and non-modifiable determinants with impacts on SGA. Social factors = birth order and interval, household wealth; Non-modifiable factors = infant sex, season, residence.

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image

Figure 3.  Relative contribution of modifiable, social factors and non-modifiable determinants with impacts on preterm delivery. Social factors = birth order and interval, household wealth; Non-modifiable factors = residence.

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Table 5.   Partial population attributable risks for several modifiable and social determinants of low birthweight, small-for-gestational-age and preterm delivery
Modifiable determinantsLow birthweightSmall-for-gestational-agePreterm
PAR95% Confidence intervalPAR95% Confidence intervalPAR95% Confidence interval
  1. *Reference: more than 6 years of education.

  2. †Reference: Very rich.

  3. ‡Reference: height ≥145 cm.

  4. §Reference: Maternal mid-upper arm circumference ≥23.5 cm.

  5. ¶Reference: 2nd or 3rd birth rank with birth interval >2 years.

Full PAR0.790.54–0.910.890.11–0.990.520.14–0.76
Woman’s education*0.080.01–0.160.060.00–0.120.110.05–0.17
Wealth†0.230.07–0.370.240.11–0.370.11−0.01–0.22
Short stature‡0.110.08–0.140.100.07–0.12  
Low MUAC§0.140.09–0.180.080.04–0.120.040.02–0.07
Birth Order and Interval¶0.300.19–0.410.270.17–0.370.06−0.04–0.16
Woman’s education + wealth0.290.06–0.490.290.08–0.470.200.03–0.37
Woman’s education + short stature0.180.09–0.280.150.06–0.23  
Woman’s education + Low MUAC0.210.10–0.300.130.04–0.230.150.07–0.22
Woman’s education + birth order and interval0.360.18–0.510.310.15–0.460.160.01–0.31
Wealth + short stature0.310.16–0.450.320.18–0.44  
Wealth + Low MUAC0.330.18–0.470.300.16–0.440.150.02–0.27
Wealth + birth order and interval0.460.26–0.620.450.27–0.590.17−0.02–0.34
Short stature + low MUAC0.230.17–0.290.170.10–0.23  
Short stature + birth order & interval0.380.25–0.500.340.22–0.45  
Low MUAC + birth order & interval0.390.25–0.510.330.19–0.450.10−0.02–0.23
Woman’s Education + wealth + short stature0.370.14–0.550.350.15–0.53  
Woman’s education + wealth + low MUAC0.380.16–0.570.340.13–0.520.240.06–0.40
Woman’s Education + wealth + birth order & interval0.500.25–0.690.480.25–0.660.250.02–0.46
Woman’s education + short stature + low MUAC0.290.18–0.400.210.10–0.32  
Woman’s education + short stature + birth order & Interval0.430.25–0.580.380.21–0.53  
Woman’s Education + Low MUAC+ Birth Order & Interval0.440.25–0.600.370.19–0.520.200.03–0.36
Wealth + short stature + low MUAC0.400.25–0.540.370.22–0.50  
Wealth + Short stature + birth order and interval0.520.33–0.670.500.32–0.64  
Wealth + low MUAC + birth order and interval0.530.33–0.680.490.30–0.640.20−0.001–0.39
Short stature + low MUAC + birth order and interval0.460.31–0.580.390.25–0.52  
Woman’s education + wealth + short stature + low MUAC0.450.23–0.620.400.19–0.58  
Woman’s education + wealth + short stature + birth order and interval0.560.31–0.730.530.30–0.70  
Woman’s Education + wealth + low MUAC + birth order and interval0.560.32–0.740.520.28–0.690.290.04–0.50
Woman’s education + short stature + low MUAC + birth order and interval0.500.31–0.650.430.24–0.58  
Wealth + short stature + low MUAC + birth order and interval0.580.38–0.720.540.35–0.68  
Woman’s education + wealth + short stature + Low MUAC + birth order and interval0.610.37–0.770.560.33–0.73  

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

We analysed more than 50 variables for determinants of LBW, SGA and preterm births, including an array of maternal morbidity and toxic exposures not previously reported in other studies, and were able to confirm that maternal education, maternal nutritional status, household wealth and the number of pregnancies and the birth interval were the common determinants of LBW and SGA. Both LBW and SGA were also more likely to occur for babies born in the rainy season, and in Central Lombok. LBW was different from SGA in terms of opposing odds in male infants. Determinants of preterm birth included maternal education, maternal nutritional status, household wealth and the number of pregnancies and the birth interval. Apart from these, we found that nausea, diarrhoea and oedema were also significant factors for preterm birth. Additionally, PAR of birth order and interval alone were highest for LBW and SGA, while the PAR of maternal education and household wealth were highest for preterm births. Combining interventions to improve woman’s education, household wealth, nutritional status and birth number and interval would avoid more than half the cases of LBW and SGA but only over a quarter of preterm births, indicating the existence of other important determinants of preterm births beyond the factors examined in this model.

A study using weight measurement within 15 days of birth reported gestational age, infant’s gender and maternal weight as determinants of newborn weight in Indonesia (Muslimatun et al. 2002). Our study confirmed these and was able to identify other determinants. As expected, our results showed that the first child and subsequent births with short intervals had a higher risk of being born LBW, SGA or preterm, similar to previous reports (Zhu 2005; Elshibly & Schmalisch 2008).

Maternal nutritional status, as measured by height or MUAC, has been known to influence foetal growth and birthweight. Season of birth, household wealth index and residence of women might have also contributed to foetal growth through availability and consumption of food by pregnant women. Babies born in the rainy season may have experienced intrauterine growth during the third trimester that coincides with the time of reduced food availability or increased infection or both. Our results imply that nutritional depletion after the second or third child may increase the odds of LBW and SGA after the risk was reduced between the first and the next child with an interval of >2 years between pregnancies. It is interesting to note that despite the larger agricultural land area and greater rice and horticultural production in Central Lombok (Badan Pusat Statistik Propinsi NTB and BAPPEDA Propinsi NTB 2003), women in Central Lombok faced a greater risk of LBW and SGA than in East Lombok.

Several studies have shown that nausea during pregnancy is associated with risk of preterm birth. Women with severe nausea and vomiting have longer gestation and a lower proportion of preterm birth (Furneaux et al. 2001; Czeizel & Puho 2004). Suggested explanations for the association between adverse pregnancy outcome and nausea and vomiting during pregnancy (NVP) include a hypothesis that linked NVP with protection against dangerous food and with the rise of hCG concentrations secreted by the placental trophoblasts (Furneaux et al. 2001). However, to date, the aetiology of nausea and how it affects pregnancy outcomes is still inconclusive.

Increased odds of LBW, SGA and preterm birth in women having episodes of fever or diarrhoea may have occurred through lower caloric intake and thus reduced energy availability for the foetus. Some studies reported an association between urinary tract infection and preterm delivery (Gravett et al. 2010) but we did not find such an association in our data, although our data only used recall of painful urination. Oedema may have been an indicator of pre-eclampsia in the population. Pre-eclampsia generally necessitates indicated preterm births but we could not differentiate between indicated or spontaneous preterm births in our data. Women reporting oedema and history of heart disease may have also had other morbidity or characteristics such as diabetes or adverse diet that were not measured in the data that may have caused bigger babies. History of weakness may have been caused by anaemia, low calorie intake or hypotension. Previous miscarriage has been reported as a risk factor for preterm birth in subsequent pregnancies (El-Bastawissi et al. 2003; Shah et al. 2009). Unexpectedly, we found that having a miscarriage in a previous pregnancy was protective of preterm delivery. No difference was found in assisted delivery, mean compliance and gestation at enrolment, but women who reported previous miscarriage were 3% more likely to deliver in facilities, which may indicate that women who reported miscarriage might be more conscious about their health and might be able to differentiate between miscarriage and late menstrual cycles.

The large SUMMIT data sets enabled us to thoroughly examine determinants of LBW, SGA and preterm births. Although the data were taken from a cluster-randomised controlled trial of MMN supplementation, whether or not the type of trial supplements was included in the analysis the determinants of LBW, SGA and preterm birth remained the same. Using these data, we were able to follow the course of the women’s pregnancy instead of only examining cross-sectional associations and have a record of birthweight measured within 72 h of delivery.

It must be noted that by using data from the SUMMIT trial, we have selected women that utilised ANC services and the social marketing embedded in the trial increased the frequency of ANC visits and assisted delivery in the study area (Shankar et al. 2009). We could not estimate, however, the total frequency and the gestation of the first ANC visit as the women were able to enrol in the trial at any gestation and any ANC visit. We also selected women whose babies’ weight was measured within 72 h of delivery, which limits the population to those delivering at a facility or living close to a health facility. Nonetheless, even in this selected population, we could still find significant socioeconomic predictors of the birth outcomes examined.

Around 76% of the pregnancy complications recalled occurred before the third trimester. However, we were able to observe increased cases of oedema with increasing trimester, which indicated that our data were able to capture normal trends along gestation.

Although there is ample evidence of the relationship between smoking and its effect on birthweight and preterm delivery (Barros et al. 2010; Savitz & Murnane 2010), we could not find such an association as only an extremely small number of pregnant women smoked. Data on second-hand smoking were unavailable, and therefore we were unable to test its effect.

The prevalence of preterm delivery in the population was high, possibly due to inaccuracy in reporting the LMP. Ultrasound measurement of gestation, however, was unavailable to validate our LMP record. Estimates of the prevalence of preterm delivery from local Lombok data were also not available for comparison. However, we tried to exclude implausible gestation in the preterm and SGA analyses by using a published method (Fall et al. 2009).

Our results offer several possible recommendations to focus programmes to prevent these adverse perinatal outcomes. Although the combination of education, wealth, nutrition and family planning would lead to the greatest reduction in LBW, SGA and preterm births, improving household wealth will be the most difficult factor to correct. Since the time of the study, there has only been a very slow reduction in the percentage of poor households in Lombok (Badan Pusat Statistik Provinsi NTB 2009). Therefore, a combination of education, nutrition and family planning packages might be more desirable with a reduction in only slightly fewer cases compared to a combination of the four interventions. Nutritional interventions are in line with recent recommendations to support balanced protein energy supplementation in food insecure populations and in mothers with low BMI (Barros et al. 2010). Providing locally made protein energy biscuits resulted in up to 136 g increase in mean birthweight, 39% decrease in LBW, 53% and 46% decrease in perinatal and early neonatal mortality (Ceesay et al. 1997). Maternal energy supplementation may even improve the weight of infants by 463 g at 9 months (Kusin et al. 1992), although another trial, which had more than 25% non-compliers, reported no effect of such an intervention (Kardjati et al. 1988). These programmes, together with programmes to educate women to plan their next pregnancy, will reduce adverse pregnancy outcomes including neonatal and infant deaths mediated through LBW, SGA or preterm delivery. The slightly higher risk associated with region indicated that these programmes are needed urgently especially for women living in Central Lombok.

In conclusion, low levels of women’s education, maternal undernutrition, low household wealth and lack of family planning are the key mediating factors that need to be addressed to improve birth outcomes. Interventions to improve these factors should continue to be encouraged.

Acknowledgement

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

The authors would like to acknowledge Roy Tjiong, Reny Bunjamin, Iswidhani Peeters, and Husni Muadz for their comments and general support; the SUMMIT field investigators: Aditiawarman, Mandri Apriatni, Benyamin Harefa, Josephine K. Kadha, Marcella Pierce, Dini Prihatini, Ari Sulastri, Adzan Sabil and Damayanti Soekarjo for their contribution and continuous support for the study; and the senior management team of Helen Keller International-Indonesia for general support. The SUMMIT was supported by the Turner Foundation, UNICEF, the Centre for Health and Human Development and the USAID-Indonesia (grant no 497-G-00-01-00001-00) to Helen Keller International-Indonesia. AusAID supported SKS’s PHD scholarship at the University of Sydney. None of the funding source had any role in the design, analysis, interpretation, manuscript preparation or publication of this paper.

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  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References
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