PUB015-6: Assessment 2

Public Health Intelligence: PUB015-6
Assessment 2: Epidemiological Report
Risk factors for neonatal mortality in Uganda
Date: 7th December 2020
Word Count: 2721
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Table of Contents
Introduction ………………………………………………………………………………………………………. 3
Literature Review……………………………………………………………………………………………….. 4
Maternal Education Level ………………………………………………………………………………………. 4
Maternal Age………………………………………………………………………………………………………… 4
Skilled Birth Attendants …………………………………………………………………………………………. 5
Methods……………………………………………………………………………………………………………. 6
Secondary Dataset ………………………………………………………………………………………………… 6
Statistical Analysis …………………………………………………………………………………………………. 6
Results ……………………………………………………………………………………………………………… 7
Participants ………………………………………………………………………………………………………….. 7
Risk Factors for Neonatal Mortality …………………………………………………………………………. 7
Discussion ……………………………………………………………………………………………………….. 10
Risk Factors for Neonatal Mortality ……………………………………………………………………….. 10
Strengths and Limitations …………………………………………………………………………………….. 11
Conclusion and Recommendations………………………………………………………………………. 13
References ………………………………………………………………………………………………………. 14
Appendix: SPSS Output Tables…………………………………………………………………………….. 18
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Introduction
Uganda is one of poorest developing countries in the World, with 19.7% of the population
reported to be living below the poverty line, while 34.6% of the population live on less than
$1.90 US per day (World Bank, 2016). In response to the health problems caused by poverty,
the United Nations (UN) developed eight Millennium Development Goals (MDG) with the aim
of eradicating poverty in developing countries by 2015 (United Nations Millennium Summit,
2000). The fourth of these MDG focused on reducing child mortality rate (CMR), which is
defined as death before a child reaches the age of five years old and is expressed as the
number of deaths per 1000 live births (United Nations, 2011).
One of the greatest risk periods for a child is during the first four weeks of their life, with the
death rate in this period known as neonatal mortality rate (NMR) (United Nations, 2011).
Between 1990 and 2019, the CMR in Uganda decreased from 182.1 to 45.8, which represents
a reduction of 74.8% (UNICEF, 2020c). Over the same time period, NMR decreased in Uganda
at a slower rate, from 38.8 to 20.0, which represents a reduction of 48.5% (UNICEF, 2020b).
When the NMR for Uganda is expressed as a percentage of CMR, the rate has more than
doubled from 21.3% in 1990 to 43.6% in 2020, which shows that NMR should increasingly
become the focus of programmes designed to reduce CMR (Lawn, Cousens and Zupan, 2005).
As a point of comparison, NMR in England and Wales was 2.8 in 2018, which represents 26.9%
of CMR, with this percentage also increasing since 1990 when it was 22.0% of CMR (Office for
National Statistics, 2020).
The main causes of neonatal mortality in Uganda were identified in an early hospital study as
birth asphyxia, respiratory distress and aspiration syndromes, very low birthweight, infection,
anaemia and congenital malformations (Mukasa, 1992). However, although these are the
acute causes of neonatal mortality, underlying risk factors lead to mothers giving birth in
conditions without adequate medical care (Lawn, Cousens and Zupan, 2005). The aims of this
epidemiological report are twofold: the initial aim is to identify risk factors for neonatal
mortality in Uganda based on an appraisal of the literature, which will enable key risk factors
to be identified. The second aim is to take the identified risk factors and evaluate their
importance with respect to neonatal mortality in Uganda using an analysis of a suitable
secondary dataset.
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Literature Review
This literature review section identifies the main risk factors for neonatal mortality in Uganda.
The search was performed using MEDLINE and CINAHL databases, using a combination of
MeSH terms and keywords. There is no MeSH term for neonatal mortality, therefore the
MeSH terms ‘Infant Mortality’ OR ‘Perinatal mortality’ were combined with the keyword
neonatal using the Boolean operator AND. This search was combined with a second search
that used the MeSH term Uganda to restrict the search to articles on neonatal mortality in
Uganda. The combined search yielded 34 articles after duplicates were removed. Three main
risk factors were identified for neonatal mortality in Uganda, with the key findings for these
risk factors presented in the following sections of the literature review.
Maternal Education Level
Several of the studies reported that maternal education level had an effect on neonatal
mortality. In one study it was reported that each additional year of schooling for a mother in
Uganda decreased the chances of a child dying by 16.6 % (Andriano and Monden, 2019).
Likewise, in another cross-sectional study using multiple surveys in Uganda, the quality of
antenatal care was reduced in the lowest socioeconomic groups, including those with limited
education (Benova et al., 2018). In another cross-sectional study, it was reported that in
mothers with post-primary education, the adjusted odds ratio (aOR) for the likelihood of NMR
was lower than in those with none (aOR = 0.68, 95% CI: 0.46, 0.98) (Kananura et al., 2017).
Based on these findings, maternal education level will be retained as a risk factor for neonatal
mortality in this report.
Maternal Age
The age of the mother was also associated with increased NMR in several studies. For
instance, in one study, mothers aged 40 and above had increased NMR when compared to
25-29 year olds (OR=2.99, 95%CI: 1.31, 6.83), while those aged 15-19 years had lower NMR
(OR=0.10, 95%CI: 0.01, 0.77) (Kananura et al., 2016). In another study, in-hospital neonatal
mortality was greater in mothers aged 35 years and over (aOR=4.5; 95% CI: 1.35, 15.31) than
in mothers aged 25-34 years old (Egesa et al., 2020). Maternal age was also associated with
increased NMR in a study using 2011 DHS data, with increased NMR for mothers aged 35-49
years (aOR 2.34, 95% CI 1.28, 4.25) when compared to mothers aged 18-34 years old (Kujala
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et al., 2017). Based on these findings, maternal age will be retained as a risk factor for
neonatal mortality in this report.
Skilled Birth Attendants
The WHO defines a skilled birth attendant (SBA) as including “accredited health professionals
such as midwifes, doctors or nurses” (World Health Organisation, 2004, p. 1). Indeed, the
recommended WHO model for antenatal care includes the presence of an SBA at the birth
(Villar et al., 2001). However, it was reported in a literature review that only 45.2% of births
in Uganda were in the presence of an SBA (Moyer, Dako-Gyeke and Adanu, 2013). In another
multi-country study using 2011 data, including Uganda, the presence of an SBA decreased
NMR in Latin America, the Caribbean and Asia, but was reported to increase the risk in Africa
(Singh, Brodish and Suchindran, 2014). In many studies of NMR in Uganda, rather than the
presence of an SBA at birth, it is the place of delivery that is used as surrogate measure, with
delivery in a healthcare facility implying the presence of an SBA (Kananura et al., 2016;
Atusiimire et al., 2019). Given the doubt surrounding the presence of an SBA as a risk factor
in Uganda for NMR, this variable will be included as a risk factor in this report.
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Methods
Secondary Dataset
The secondary dataset used for this report was obtained from the Demographic and Health
Surveys (DHS), which is a programme funded by the United States Agency for International
Development (USAID) to improve understanding of global health in developing countries
(Corsi et al., 2012). The Ugandan dataset for the 2016 DHS survey was used for this report
(Uganda Bureau of Statistics (UBOS) and ICF, 2018). Ethical approval for the DHS study was
obtained from the Institutional Review Board of the Inner City Fund (ICF) and the ethics
Committee of the Ministry of Health in Uganda, with this approval extended to include
secondary analysis of the data approved by the DHS. Accordingly, permission to use the
Ugandan DHS dataset for this report was requested from the DHS Program (Authorisation
145058). The 2016 DHS survey in Uganda was a nationally representative sample, with
participants randomly chosen from all regions of Uganda between June and December 2016.
The dataset can be downloaded from https://dhsprogram.com/data/available-datasets.cfm.
Statistical Analysis
All statistical analyses were performed using SPSS Statistics (v26, IBM Corporation, Armonk,
New York, USA). Each of the three risk factors identified in the literature review was
transformed into a categorical variable. Education classifications used the DHS categories of
no education, primary, secondary, and higher education. Three age groups were created by
determining the cut-offs to create three groups with as close to equal numbers as possible,
which were 15-25 years, 26-34 years, and 35-49 years. Birth attendants were classified into
two groups, depending on whether a skilled birth attendant (nurse, midwife or doctor) was
present. Chi-squared analysis was used to determine the association of each risk factor with
neonatal mortality, while logistic regression was used for multivariate analysis of all risk
factors combined. Confidence intervals of the odds ratios obtained from chi-squared and
logistic regression analysis were used to determine significant differences between groups,
with 95% confidence levels used (Gardner and Altman, 1986).
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Results
Participants
The number of women included in the 2016 Ugandan DHS survey was 18,506, of which 13,745
had given birth to at least one live child (74.3%). Of these mothers, 1345 (9.8%) had
experienced neonatal mortality. There were 4,256 mothers aged 15-25 years (31.0%), 4739
(34.5%) aged 26-34 years, and 4750 (34.6%) aged 35-49 years. With respect to education,
1933 (14.1%) had no education, 8,305 had primary education (60.4%), 2663 (19.4%) had
secondary education, while 844 (6.1%) had higher education. In total, 9,919 mothers had an
SBA present at the birth (72.2%), while 3,826 (27.8%) had an unskilled attendant or nobody
present at the birth.
Risk Factors for Neonatal Mortality
Maternal Education Level
There was a significant effect of education level on neonatal mortality ( c2= 111.6, df=3,
p=0.000). When compared to those with no education, which was taken as the reference
group, there was a significantly decreased risk of neonatal mortality in mothers who had no
education, primary education, and secondary education, with each increase in education level
corresponding to a lower risk (Table 1).
Table 1: Effect of education level on neonatal mortality

No education 14.3% Reference group
Primary 10.3% c2= 25.04 df=1, p=0.000 0.69 (0.60, 0.80)
Secondary 6.6% c2= 71.9 df=1, p=0.000 0.42 (0.35, 0.52)
Higher 3.9% c2= 55.9 df=1, p=0.000 0.24 (0.17, 0.35)
Maternal Age
There was a significant effect of age-group on neonatal mortality ( c2= 245.7, df=2, p=0.000).
There was a significantly increased risk of neonatal mortality for older mothers, with those
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aged 26-34 years and 35-49 years having a greater risk of neonatal mortality when compared
to the reference group aged 15-25 years (Table 2).
Table 2: Effect of age group on neonatal mortality

15-25 5.5% Reference group
26-34 8.5% c2= 30.6 df=1, p=0.000 1.60 (1.36, 1.90)
35-49 15.0% c2= 201.2 df=1, p=0.000 3.06 (2.61, 3.57)
Skilled Birth Attendants
With respect to SBA, there was a significant effect on neonatal mortality ( c2= 30.1, df=1,
p=0.000), with the presence of SBA associated with a significantly lower risk of neonatal
mortality (Table 3).
Table 3: Effect of the presence of a skilled birth attendant on neonatal mortality

Skilled 8.9% Reference group
Non-skilled
or nobody
12.0% c2= 29.9 df=1, p=0.000 1.40 (1.24, 1.57)
Multivariate Analysis
When all risk factors were entered into a multivariate logistic regression analysis to adjust for
covariates, both age group and education remained significant predictors of neonatal
mortality (Table 4). However the presence of an SBA was no longer significantly associated
with neonatal mortality rate.
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Table 4: Logistic regression analysis of age group, education level and the presence of a skilled
birth attendant for neonatal mortality
Risk factor Group Chi-squared test Unadjusted odds ratio and 95% CI
Education
level
No education Reference group
Primary c2= 4.6 df=1, p=0.033 0.85 (0.73, 0.99)
Secondary c2= 24.9 df=1, p=0.000 0.59 (0.48, 0.73)
Higher c2= 41.6 df=1, p=0.000 0.29 (0.20, 0.43)
Age-group
15-25 years Reference group
26-34 years c2= 32.7 df=1, p=0.000 1.64 (1.38, 1.94)
35-49 years c2= 159.4 df=1, p=0.000 2.99 (2.53, 3.55)

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Discussion
Risk Factors for Neonatal Mortality
The education level of mothers was a significant factor in neonatal mortality, even after
adjusting for other risk factors, with each increase in education level corresponding to a
decreased risk of experiencing neonatal mortality. Similar results have been reported in other
studies, with lower neonatal mortality observed in mothers with at least secondary education
level when compared to those with primary education level or lower (Kananura et al., 2017).
This could be related to other elements of socioeconomic status that were not included in the
analysis. According to the latest UNICEF report on Uganda, uneducated women are more
likely to live in poverty, while they are also more likely to have negative attitudes towards
modern medical care during pregnancy, delivery and the postnatal period (UNICEF, 2020a).
Given the role that maternal education appears to play in neonatal mortality, it will be
worthwhile investigating the impact of the educational reforms in Uganda, which in 1997
introduced universal free primary education, followed by universal free secondary education
in 2007 (Chapman, Burton and Werner, 2010).
Age group was also a significant factor in neonatal mortality after adjusting for other risk
factors. These results were expected, with other studies in Uganda also reporting greater
neonatal mortality rates among older mothers (Kananura et al., 2016; Egesa et al., 2020).
Similar results have been reported in other countries, such as Trinidad and Tobago (Cupen et
al., 2017) and Bangladesh (Al Kibria et al., 2018). However, in a UNICEF report using data from
2011, greater levels of neonatal mortality were reported in women aged under 20 when
compared to those aged over 20 (UNICEF, 2020a). Given that the difference in age remained
after adjusting for education level, and that there was a change from the 2011 results, it
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would be worthwhile investigating this relationship in the future once the educational
reforms in Uganda have had a chance to make an impact.
The final risk factor evaluated in this report was the presence of an SBA at the birth. Although
the unadjusted analysis showed an increased risk of neonatal mortality when an SBA was not
present, when age and education level were adjusted for in the logistic regression model,
there was no longer any effect of having an SBA present at the birth. This could be partly
explained by the relatively high rate of SBA presence observed in this study, with 72.2% of
births being attended by an SBA, which represents a steady increase from previous studies in
Uganda in which only 46.2% of mothers reported having an SBA present at birth in 2006
(Moyer, Dako-Gyeke and Adanu, 2013). In a qualitative study, it was suggested that barriers
to change in Uganda with respect to the attitudes to having an SBA present or giving birth in
a healthcare facility were decreasing, with more acceptance of the need for modern
healthcare (Byaruhanga et al., 2011). One of key issues that needs to be resolved with respect
to the presence of SBA is the rural/urban disparity in healthcare in Africa, which was not
evaluated in the present study (Ameyaw and Dickson, 2020).
Strengths and Limitations
The use of a DHS dataset was a strength of this report. The DHS programme is internationally
recognised to be of high quality and provides a nationally representative sample of mothers
in Uganda. Furthermore, the dataset used was the most recent dataset available, with data
collection occurring in 2016. Given that the dataset was large, collected by a highly-skilled
research team and representative of Ugandan mothers, the results can be considered
generalisable. However, despite these strengths, there were some limitations to this report.
The methodology used was retrospective, meaning that mothers were asked about their past
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experiences, which could lead to recall bias, especially given the low education levels of some
of the participants (Neal, Channon and Chintsanya, 2018). The method used to calculate
neonatal mortality was also a limitation, with each mother classified as having had a child die
within the first four weeks after birth, with no differentiation made between those who had
suffered multiple neonatal mortalities. The analysis was also limited to three risk factors, with
other potential risk factors such as geography or religion not included in the analysis.
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Conclusion and Recommendations
The biggest risk factors for neonatal mortality in Uganda that were identified in this study
were education level and age. Of these two factors, education level is the risk factor that could
be modified most easily. The adoption of universal free education in Uganda is a step in the
right direction in this regard. However, it would be worthwhile investigating whether there
are any barriers to attaining universal free primary and secondary education, particularly in
areas of high poverty, such as rural Uganda. The improvement of infrastructure in remote
rural areas is also vital to reduce NMR, as timely critical care is needed, such as resuscitation
equipment for new-borns with breathing difficulties. In addition, the impact of the presence
of skilled nurses, midwives and doctors requires further study given the equivocal findings in
the present study.
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Appendix: SPSS Output Tables
Education Level
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Age Group
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Skilled Birth Attendant
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Multivariate Analysis