Menu
Menu
Menu
Menu
Background: Programmatic and research agendas surrounding neonatal mortality are important to help countries attain the child health related 2030 Sustainable Development Goal (SDG). In Burundi, the Neonatal Mortality Rate (NMR) is 25 per 1000 live births. However, high quality evidence on the over time evolution of inequality in NMR is lacking. This study aims to address the knowledge gap by systematically and comprehensively investigating inequalities in NMR in Burundi with the intent to help the country attain SDG 3.2 which aims to reduce neonatal mortality to at least as low as 12 per 1000 live births by 2030. Methods: The Burundi Demographic and Health Survey (BDHS) data for the periods of 2010 and 2016 were used for the analyses. The analyses were carried out using the WHO’s HEAT version 3.1 software. Five equity stratifiers: economic status, education, residence, sex and subnational region were used as benchmark for measuring NMR inequality with time over 6 years. To understand inequalities from a broader perspective, absolute and relative inequality measures, namely Difference, Population Attributable Risk (PAR), Ratio, and Population Attributable Fraction (PAF) were calculated. Statistical significance was measured by computing corresponding 95% Confidence Intervals (CIs). Results: NMR in Burundi in 2010 and 2016 were 36.7 and 25.0 deaths per 1000 live births, respectively. We recorded large wealth-driven (PAR = -3.99, 95% CI; − 5.11, − 2.87, PAF = -15.95, 95% CI; − 20.42, − 11.48), education related (PAF = -6.64, 95% CI; − 13.27, − 0.02), sex based (PAR = -1.74, 95% CI; − 2.27, − 1.21, PAF = -6.97, 95% CI; − 9.09, − 4.86), urban-rural (D = 15.44, 95% CI; 7.59, 23.29, PAF = -38.78, 95% CI; − 45.24, − 32.32) and regional (PAR = -12.60, 95% CI; − 14.30, − 10.90, R = 3.05, 95% CI; 1.30, 4.80) disparity in NMR in both survey years, except that urban-rural disparity was not detected in 2016. We found both absolute and relative inequalities and significant reduction in these inequalities over time – except at the regional level, where the disparity remained constant during the study period. Conclusion: Large survival advantage remains to neonates of women who are rich, educated, residents of urban areas and some regions. Females had higher chance of surviving their 28th birthday than male neonates. More extensive work is required to battle the NMR gap between different subgroups in the country.
Burundi lies astride Eastern Africa and Central Africa [16, 17] and is the third most populous country in SSA. In 2015, the country had an estimated 435 inhabitants per km2 and by 2040, its booming population is expected to double [17]. With a population of 11 million, it is one of the economic-disadvantaged countries due to existing political instability and violence [17]. Burundi is the least urbanized country in SSA in spite of its high population density and as at 2014, only 12% of the population lived in urban settings [17]. Burundi trails on many human development indicators and with an average per capita consumption of only US$270 per year, the country lies at the bottom of the low-income category [17]. Hunger and malnutrition are predominant in the country despite the country’s economic dependence on agriculture. In 2014, almost 70% of Burundi’s population was found to be undernourished, over three times the then Millennium Development Goals target and 60% of children under-five were suffering from stunting [17]. Over the past two decades, there have been advances in some health indicators such as under-five and maternal mortality rates and vaccination coverage in Burundi [7]. Notwithstanding, maternal, infant, and child mortality rates lag below regional averages due to political crisis in the country, coupled with inequities in health service utilization and financial barriers to healthcare access, which are predominant in low- income and rural households [7]. The Burundi Demographic and Health Survey (BDHS) for the periods of 2010 and 2016 were used for the analyses. Both are nationally representative household surveys. DHS is a rich source of data for several reproductive and child health care service indicators, including mortality in Burundi. The BDHS is carried out every 5 years and so far, three waves have been conducted between 2005 and 2016. Since the 2005 BDHS does not contain data on NMR, we confined our analyses to the 2010 and 2016 rounds. The survey basically covered women aged 15 to 49 years that gave births 5 years prior to the respective surveys, and men aged between 15 to 59 years and children. The response rates for the women were 96.4 and 99% for the 2010 BDHS and 2016 BDHS respectively. The methodology of the BDHS has been clearly described in the respective DHS’s final pdf document and we refer readers to the documents for detailed information on how the surveys were conducted [9, 18]. In brief, DHS is a two-stage population-based study with the first stage being large communities or villages that encompass several households. These are normally known as Enumeration Areas (EAs). EAs are selected through Probability Proportional to Size (PPS) approach so that large EAs have higher chance of being in the sample than small EAs. The EAs were selected from the list of EAs which were prepared in the Burundi Population and Housing Census. The complete list of all the EAs in the country served as sampling frame for the first stage and list of households in the selected EAs served as sampling frame for the second stage, where households are selected from each EA and all eligible individuals within the selected households are studied. The EAs and households are the primary and secondary sampling units respectively. Samples were selected so that they were representative at the national level. Inequality is measured for NMR, which refers to the number of deaths during the first 28 completed days of life per 1000 live births in a given year or another period. The birth histories data in the DHS has information on the birth dates and age of death of neonates. The analyses involved data on live births that took place 5 years prior to the surveys. Five equity stratifiers: economic status, education, residence, sex and subnational region were used as benchmark for measuring NMR inequality with time over 6 years. Wealth index, which is derived from household assets and features was used to approximate economic status. Principal Component Analysis (PCA) is used to compute wealth index in DHS and classifies it as poorest, poorer, middle, richer, and richest [19]. Educational status of the mother was grouped into no education, primary, and secondary education, while urban and rural were used to define residence. Sex was categorized as male and female, and subnational region into five regions in 2010 namely Bujumbura, North, Centre-East, West, and South and into eighteen regions in 2016 such as Bubanza, Bujumbura Rural and Bururi, just to name few. A two-step approach was followed in measuring inequality in NMR. The first step involved a disaggregation of NMR by five equity stratifiers, namely education, economic status, residence, subnational region and sex. This was followed by an assessment of inequality using four measures of inequality, thus Difference (D), Population Attributable Risk (PAR), Population Attributable Fraction (PAF) and Ratio (R). The D is a simple, unweighted measure of inequality that portrays the absolute inequality between two subgroups. The PAR is a complex, weighted measure of inequality that shows the potential for improvement in the national level of a health indicator that could be achieved if all subgroups had the same level of health as a reference subgroup. The PAF is a complex, weighted measure of inequality that shows the potential for improvement in the national level of a health indicator, in relative terms, that could be achieved if all subgroups had the same level of health as a reference subgroup. The R is a simple, unweighted measure of inequality that shows the relative inequality between two subgroups [20]. Whereas Difference and Ratio are simple measures, the other two are complex measures. R and PAF are relative measures, but D and PAR are absolute summary measures. The selection of summary measures is based on evidence that supports the scientific significance of using both absolute and relative measures in studies involving single health inequality [21]. This is deemed essential due to the likelihood of obtaining different and even contrasting conclusions, which can lead to bias informed decisions when using either relative and absolute inequality measures alone [21]. Further, complex measures are likely to examine inequalities over time when there is a shift in the proportion of population in each dimension of inequality [21]. The difference between complex and simple measures is that whereas the former takes into consideration size of categories of a sub-group, the later does not. Again, in situations where there is a likelihood of population shift, especially during trend analysis, complex measures are more probable to show the true change in equality over time but simple measures are easy to interpret and understand [21]. Hence, in order to provide a more comprehensive analysis, there is the need to combine both simple and complex measures, in addition to relative and absolute measures in an inequality study. The analysis was carried out using the WHO’s HEAT version 3.1 software [20]. Detailed description of the procedures followed for calculating summary measures are available in the HEAT software technical notes [20] and in the WHO handbook on health inequality monitoring [21]. Hence, only a brief description is provided here. For education, D was calculated as NMR in “un-educated” group minus NMR in “secondary education” group, whereas for economic status, it was calculated as NMR in the poorest group minus NMR in the richest group. Similarly, D was calculated as NMR in rural minus NMR in urban populations with respect to place of residence, male minus female for sex, and region with the highest estimate minus the one with the lowest estimate in relation to subnational region. The same subgroups are used to calculate R, but instead of subtracting NMR estimate of one subgroup by the other unlike the case for D, we divide one by another. The NMR estimate for the reference subgroup, yref, and the national average of neonatal mortality rate was used to compute PAR. For ordered dimensions the most-advantaged sub-group describes yref, which in our case are the secondary school and above subgroups for education and richest sub-group for economic status and for binary dimensions like sex and residence, yref refers to the subgroup which has the lowest estimate, which in our case are female and urban residence. For non-ordered dimensions like subnational region, yref points out the subgroup or region with the lowest estimate, PAF were computed by dividing the PAR by the national average μ and multiplying the fraction by 100 (PAF = [PAR / μ] * 100). PAF is zero if no further improvement can be achieved, i.e. if all subgroups have reached the same level of health as the reference group (20). The change in NMR over time was assessed in reference to the 95% Confidence Intervals (CIs) of the different survey years. Whereas an absence of overlapped CIs portrays statistically significant difference between the two CIs, an overlap of CIs is an evidence of absence of inequality. The analyses were performed using publicly available DHS data. Institutions involved in the commissioning, funding and management of the surveys are responsible for ethical procedures in the surveys. To ensure that the survey protocols comply with the U.S. Department of Health and Human Services regulations for the protection of human subjects, all DHS obtain their approval from ICF international in addition to an Institutional Review Board (IRB) in the respective country.
N/A
