Page 1 of 13
Journal for Studies in Management and Planning
Available at
http://internationaljournalofresearch.org/index.php/JSMaP
e-ISSN: 2395-0463
Volume 01 Issue 02
March 2015
Available online: http://internationaljournalofresearch.org/ P a g e | 90
Determinants of Low Birth Weight Neonates: A Case
Study of Tamale Metropolis in Ghana
R. Puurbalanta 1, A. O. Adebanji 2
1University for Development Studies, Faculty of Mathematical Sciences, Department of
Statistics, Navrongo Campus, Ghana
E-mail: pubarichard@yahoo.com
Tel: +0233244546726
2 Kwame Nkrumah University of Science and Technology, Faculty of Physical Sciences, College
of Science, Department of Mathematics, Kumasi, Ghana
E-mail: tinuadebaji@yahoo.com
Tel: +0233241860372
ABSTRACT
Low Birth Weight (LBW), a birth weight less than 2.5kg, is an important public health problem
because LBW infants are at greater risk of mortality and morbidity in early infancy (WHO,
2004; UNICEF, 2004). The rate of LBW in the Northern Region consistently ranks high among
the ten regions in Ghana, and Tamale metropolis has the highest percentage of LBW births
among the twenty districts in the Northern Region, and this is a major concern for health care
providers given the high cost of caring for LBW infants. In this study, logistic regression model
was used to identify the determining variables in predicting LBW babies in the metropolis. The
model was based on the birth records of 500 mothers of singleton neonates resident in the
Tamale metropolitan area of the Northern Region of Ghana from November 2010 to January
2011. The significant model coefficients were Gestation (p-value = 0.0008), Household size (p- value = 0.0160), Maternal food intake (p-value = 0.0002), Maternal health (p-value = 0.0000),
Passive smoking (p-value = 0.0003) and Type of fuel used for cooking (p-value = 0.0418). A test
of predictive ability of the model showed correct classifications of 93% for normal birth weight
infants and 76.8% for LBW infants. The likelihood ratio and Nagelkerk R2
tests showed positive
correlation between the predictors and LBW. Using the Hosmer and Lemeshow test of goodness
of fit, a p-value 0.206 was obtained and thus the null hypothesis that the model fits the data well
could not be rejected.
Keywords
Birth Weight, Logistic, Predictors, Maternal, neonate
INTRODUCTION
The presumption that women of childbearing age should not possess any special skills in order to
give birth to normal and healthy babies may be untrue, especially for women in Tamale
metropolis in the Northern Region of Ghana. Available records show that the metropolis
consistently records higher rates of LBW infants in the region (Northern Region Reproductive
Page 2 of 13
Journal for Studies in Management and Planning
Available at
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e-ISSN: 2395-0463
Volume 01 Issue 02
March 2015
Available online: http://internationaljournalofresearch.org/ P a g e | 91
and Child Health (NRRCH) annual reports, 2007, 2008 and 2009, unpublished).
In informal conversations, many health experts in the region agreed with reports by UNICEF and
WHO (2004) and UNICEF (2005) that high rate of LBW is a major problem in developing
countries yet the issue and factors influencing it has not received the much-needed attention. The
result being that some expectant mothers undermine foetal welfare and thus many births result in
LBW babies.
Unavailability of adequate information on the causes of LBW to the general public, especially
expectant mothers has compounded the situation. In this evidence-based era, this is a real
concern, as no evidence will be available for policy decision-making. This underscores the need
to identify and highlight the main risk factors that cause LBW, with the ultimate aim of
eliminating it.
LBW prevalence is a major public health issue since it is considered the single most important
predictor of infant mortality, particularly in the first month of life (Blanc et al, 2005), and is a
significant factor in many adverse child health and development outcomes (WHO, 2004).
Risk factors include maternal age, alcohol abuse, smoking, lack of pre-natal care, gestational age,
and maternal ill health (Allen et al, 2001; Steyn et al, 2006; Knopik, 2009; Salmasi et al, 2010).
In this study, logistic regression model was used to investigate the determining factors of LBW
neonates and postulating a predictive model of the likelihood of a pregnancy resulting in a LBW
neonate in the metropolis.
MATERIALS AND METHODS
Study Design and Data Sources
The study was a cross-sectional design. Data included all singleton births over a period of three
months (November 2010 to January 2011). Five hospitals randomly selected in the metropolis
served as study sites. They are the Choggu Clinic, Tamale Central Hospital, Tamale West
Hospital, The SDA Hospital and Fulera Maternity Home. The above community-based hospitals
provide both antenatal care and counseling services to expectant mothers. The locations of these
hospitals in the metropolis portray respondents of variable socio-economic backgrounds.
After obtaining the consent of the women to partake in the study, a structured questionnaire was
used to elicit data pertaining to mother’s socio-economic and reproductive-obstetrical
information. Maternal and newborn medical history such as number of antenatal visits,
gestational age, maternal health, maternal weight gain during pregnancy, neonate’s sex and birth
weight recorded in the folders of mothers at the medical facility during the course of pregnancy
were all captured by the questionnaire.
Sampling Techniques
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e-ISSN: 2395-0463
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March 2015
Available online: http://internationaljournalofresearch.org/ P a g e | 92
In order to meet the data requirement for this study, all singleton births to mothers who had
medical history recorded at the medical facility during the course of their pregnancy were
sampled. Mothers had to be ordinarily resident in the metropolis to be eligible for the study.
Study Area
Tamale metropolis
Study Population
All mothers who delivered between November 2010 and January 2011 in the metropolis.
Determination of Sample Size
Using a guideline provided by Hosmer et al (2000) that the minimum number of cases per
independent variable is 10, with a preferred ratio of 20 to 1, a sample of 500 mothers were
selected for the study.
Study Variables
We define our outcome variable as a binary response corresponding to the risk of a neonate
being born LBW. That is,
1, if neonate is LBW
0, otherwise
Explanatory variables include maternal age, marital status, parity, cigarettes smoking by the
mother during pregnancy, maternal educational attainment, family income, gestational age,
passive smoking, type of fuel used by the mother for cooking, type of residence, number of
antenatal visits, maternal health, maternal food intake, household size, maternal weight gain
during pregnancy, employment status, occupation, history of previous LBW, birth spacing and
new-born sex.
Logistic regression model was used to identify risk factors for LBW by estimating the odds
ratios (OR) and their 95% confidence interval (CI). A multivariable analysis was conducted to
control for confounders. This analysis was carried out by the simultaneous method, where all
variables were entered at once. A 0.05 level of significance was used and the data processed and
analysed using Epi info3.4.1 software.
Logistic Regression Model for Binary Data
When we have a binary response variable , and a vector of associated covariates , the
preferred mathematical model to deal with the complex and generally nonlinear
interrelationships among the many variables is the binary logistic regression model (Agresti,
2007). Logistic regression combines the independent variables to estimate the probability that an
event of interest will occur, that is, a subject will be a member of one of the groups defined by
the dichotomous dependent variable.
