Perspectives

When a child is born, especially in underserved settings, the first minute of their life can too often be their last.

In 2019, it was estimated that worldwide, 17.5 neonates per every 1,000 live births died, a figure that has come down significantly from 1990, when it was 36.6, but that still remains too high. As troubling, of the 47 percent of every under-five death in the world, one third occur on the day of birth. In low- or middle-income countries, like my native Uganda, where infections like malaria or syphilis are common and labor and delivery conditions are not always adequate, those averages are even higher.

Ensuring a safe birth is an essential part of preventing neonatal mortality. Deliveries in facilities with skilled birth attendants and enabling environments to provide high quality care can save newborns and facilitate care in high impact interventions (such as safe cord care, early initiation of breastfeeding, kangaroo mother care), circumvent complications (via the receiving of antibiotics for infections), and navigate emergencies (via newborn resuscitation) – all elements that, left unmitigated, can lead to neonatal deaths.

In low- and middle-income countries, preventing neonatal deaths to achieve the Every Newborn Plan SDG targets of less than 12/1000 live births by 2030 will not be easy. Unlike with older kids, where much progress has been made based on what could be termed “simple” interventions (immunisations, use of insecticide treated bed nets, control of diarrhea, etc.), reducing neonatal deaths will require improved clinical services because a baby who is not breathing can die within a minute. Thus, to save babies will require clinical systems that are able to respond “within a minute”. In other words, reducing neonatal deaths to achieve SDG targets is the real test of the performance of health systems in low- and middle-income countries. Therefore, systems must be well managed, very responsive and coordinated in order to be institutionalized for effective care, to give newborns a better fighting chance. That also means systems that are more affordable, less complex and offer better intervention design, putting women at the center.

Health systems in LMICs are not always managed to optimal efficiency. The centralization of care can create economies of scale, but too often the trade-offs are gaps at the district and local level, where delivery mechanisms become bottlenecked or fragmented and the continuum of care – from antenatal through postpartum – is interrupted. Resources, both capital and human, are often mismanaged, focusing disproportionately on delivery – as it’s regularly deemed the most important intervention. However, in settings like Uganda this is not enough. Here, we have a high burden of diseases in women (malaria, malnutrition, anemia, syphilis, and NCDs) which increase the risk of neonatal deaths (via stillbirths, low birth weight, prematurity, and birth asphyxia). In addition, circumstances often make women come late for delivery care. Thus, antenatal care is as important as care at birth. In addition, even after a successful delivery, many babies die following discharge because of poor postnatal care. It is therefore important that high quality care be provided throughout the continuum of care from antenatal, through delivery and postnatal care, including respectful care.

Women, who are inherently at the center of neonatal health, are not always fully empowered to seek proper care. This begins with proper family planning to both enable women to only have babies they seek, but also help systems remain unconstrained. It expands to prenatal care, to ensure women arrive at delivery facilities long before labor puts their child at risk. And it extends to caring for babies post-delivery, to ensure they can thrive in the critical first 28 days, in their first five years of life, and beyond.

Another challenge for newborn care is implementation fragmentation. Whereas as of recent there has been an increase in resources for newborn care and research, we see a total lack of coordination leading to project pressure on districts and hospitals in what we call “pilotitis”. This uncoordinated implementation puts pressure on the already overstretched human resources (such as midwives) and creates waste and lack of focus to deliver critical care where it’s needed most. Compounding that are global targets that are often established with optimistic ambition, but yet inadequate support to deliver on them especially at country and local level, given each country’s unique local context.

It is true that we need projects, but to be effective, they must be well designed and well implemented. Our work at the Makerere University’s Maternal Newborn Child Health Centre of Excellence has found that too often, failed institutionalization is what causes poor program implementation. Maintaining adequate supplies, lack of supervision, motivation and reporting, as well as poor stewardship, conflicting guidelines, lack of accountability, and limited autonomy at the local level often lead to programs’ poor performance – negatively impacting neonatal health.

From 2006 to 2020, we implemented the Maternal and Newborn Scale-Up project, aiming to improve newborn care in six hospitals that served four million people over a five-year period. To test our hypothesis, our program operated on key institutionalization tenets: the promotion of strong leadership involvement and engagement; the emergence of champions; the alignment to policy and structures; the use of mainly local resources, such as hospital space, no additional health workers, commodity availability from the hospital’s own resources, and routine data systems; the capitalization on health workers’ and community’s demand and perceived benefit; and the undertaking of implementation over a long period (five years) with full local staff and leaders’ involvement. The result was good governance. Good management. And ultimately, institutionalization with better care for newborns.

But like this example from Uganda, there are many others from LMICs-that show how capacity, as well as stewardship and autonomy within health systems are essential to creating change for the health of newborns. Systems that are effective may help guarantee the sustainability of that change. It’s why we need national governments in LMICs to lead and invest in better systems, ensuring donors are mobilized for additional resources while also fostering alignment and shared accountability. Because as defining as any child’s first 60 seconds can be, the timeline that leads to them – and that predicts their outcome – starts long before a mother goes into labor and concludes long after a baby is born. Creating resilient healthy systems is one of the best ways we can give those 60 seconds for newborns in Uganda and beyond, their best shot.

About the author

Dr. Peter Waiswa is a Ugandan medical doctor trained in Public Health. Currently, Dr. Waiswa is an Associate Professor at Makerere University School of Public Health, College of Health Sciences, Uganda and also a visiting Researcher at Karolinska Institutet, Sweden. He is the Founder and Coordinator of the INDEPTH Network Maternal and Newborn Research Group in Accra, Ghana, and the Makerere University Maternal and Newborn Centre of Excellence in Uganda. Dr. Waiswa is a member of the WHO Advisory Board for Maternal, newborn, child and adolescent health.

Investigating local variation in child health is a critical first step towards understanding the successes of the U5M Global Health Exemplar countries. There is a lot of variation in access to primary healthcare within countries. Children in one village may have access to a health clinic and school, and five miles away on the other side of a river, children may have neither. All of this impacts their health.

In 2019, we mapped child deaths at the subnational level for 99 countries, including the Exemplars. The results help answer a fundamental question: how did each country deliver on its mandate to equitably improve the health of all its citizens?
The answer is complex but reveals the importance of looking at subnational equity rather than just country-wide progress.

The figure below shows estimates of U5M across second-level administrative unit, called districts or counties in many countries, in 2000 (grey) and 2017 (blue). Each dot represents a second-level administrative unit, with the black diamond in the center representing the median U5M across all divisions in a given country and year.

The top chart, which plots the absolute U5M in each country and year, shows the absolute geographic variation within each country. In all of the Exemplar countries, the absolute difference in U5M between the best- and worst-performing units declined between 2000 and 2017. In Senegal, this disparity more than halved, shrinking from 136 deaths/1,000 live births in 2000 to 48 deaths/1,000 live births in 2017. In Nepal, Bangladesh, and Rwanda, the unit with the highest mortality rate in 2017 was still lower than the unit with the lowest mortality in 2000. Our analysis demonstrates that the Exemplar countries are outstanding both for their substantial declines in U5M across all units as well as their improvements in absolute inequality since 2000.

However, when assessing geographic variation in U5M relative to the country average, a more complex picture emerges. The distribution of U5M on the second chart, which has been normalized to the median mortality rate across each country-year, shows relative inequality over time. Unlike absolute inequality, relative geographic inequality has remained stable or even increased in the Exemplar countries since 2000.

The particular successes and challenges of implementing evidence-based interventions (EBIs) for child health in each of the Exemplar countries can shed light on why relative geographic inequality persists even in the face of substantial U5M declines.

• In Rwanda, the Exemplar country with the lowest relative inequality, strong central governance and donor coordination allowed for rapid scale-up of interventions: for example, coverage of the pneumococcal conjugate vaccine (PCV) reached 97% one year after the beginning of the national inoculation program.
• Conversely, in Nepal, mountainous regions with poor infrastructure hindered the national implementation of ambitious programs to improve neonatal and child health.
• In Ethiopia, dramatic subnational differences in access to healthcare services remained: in 2016, only 15% of households in the Somali region had access to facility-based delivery, as opposed to 97% in Addis Ababa.

By examining local variation in both implementation and outcomes, the Global Health Exemplars project offers a starting point for understanding how countries have tackled stubborn geographic disparities in child health. Across most of the study countries, nationwide vaccination programs and preventative neonatal interventions successfully reduced within-country inequities in child health.

As one example, strong community health worker (CHW) programs allowed countries to deliver care to rural, remote, and pastoral communities—by sustainably growing the skills and compensation available to CHWs, countries have an opportunity to increase geographic access to critical interventions for child health. Applied in a locally-appropriate context, these findings and others can help countries realize the promise of healthy children nationwide.

Authored by: Professor Simon Hay, Director, Local Burden of Disease group, Institute for Health Metrics and Evaluation Link to Bio

A survey by my organization, the International Center for Equity in Health (ICEH), found that the following countries achieved the most marked reductions in socioeconomic inequalities in under-five mortality: Democratic Republic of Congo, Sierra Leone, Mozambique, Kyrgyzstan, Comoros, Kenya and Malawi.

To access the full study, please click here.

The study relied on approximately 400 survey datasets from either Demographic and Health Surveys (DHS) or Multiple Indicator Cluster Survey (MICS). For more information on the methodology employed by these surveys: DHS and MICS. Both types of survey programs are highly comparable in terms of sampling and questionnaires. A few national surveys that are not standard DHS or MICS were included when their data allowed estimates of under-five mortality.

Indicators

U5MR (the under-five mortality rate) was calculated as follows. Women aged 15 to 49 years in the survey samples were asked about pregnancies and deliveries in the years preceding the survey, including characteristics such as birth date, sex of the children, and survival status. If a child was not alive, the age at death was recorded. U5MR estimates were based on the ten years preceding the survey. The ten-year period is required for calculating death rates by wealth quintile (see below) with sufficient precision. Mortality rates are presented as the number of deaths per 1,000 live births. For a survey that took place in 2018, for example, the mid-point of the U5MR estimate was five years earlier, or 2013. This is important to bear in mind when interpreting the results, particularly when comparing them with estimates of current mortality levels.

The socioeconomic position (SEP) of households is based on asset indices, obtained from information on household appliances, characteristics of the building materials, presence of electricity, water supply and sanitary facilities, among other variables. Because relevant assets and their importance may vary in urban and rural households, separate principal component analyses are carried out in each area, which are later combined into a single score using a scaling procedure based on linear regression where the dependent variable is a score obtained for all households, excluding items not applicable to either urban or rural areas. The resulting score, which is comparable between urban and rural households, is then divided into quintiles.

The slope index of inequality (SII) is a measure of absolute inequality, derived from mortality rates according to family wealth quintiles. A value of -10, for example, indicates that U5MR is estimated to be 10 deaths per thousand lower at the top of the socioeconomic scale than at its bottom.
For each country, we used data from all available surveys to estimate the annual change in the SII using weighted least square regression with sample sizes as the weights. Annual change was then divided by the value of the SII in the earliest survey for each country and expressed as a percentage.

Results

A total of 58 countries had more than one survey over time for which it was possible to estimate U5MR by quintile. Of these:

• 10 countries were excluded because the most recent survey was in 2011 or earlier.
• Ethiopia was excluded because the SII in the earliest survey was slightly positive (higher mortality among children from wealthy families) and became slightly negative over time, indicating that inequality increased.
• 7 countries (Albania, Burundi, Chad, Liberia, Tajikistan, Togo and Zimbabwe), had negative SII at baseline which increased over time, also indicating that inequalities worsened.
• 10 countries (Armenia, Colombia, Gabon, Honduras, Jordan, Lesotho, Maldives, Namibia, Tanzania and East Timor) presented SII values that were very small – less than 5 deaths per 1,000 – at baseline, and were also excluded.

The table below shows the remaining 30 countries, ranked according to the percent of the equity gap in U5MR closed.

The top performers were DR Congo, Sierra_Leone, Mozambique, Kyrgyzstan, Comoros, Kenya and Malawi.

Limitations

When interpreting these results, the following limitations must be considered.

• Data were only available for 58 countries.
• The number of surveys per country varied, as did the time elapsed from the first to the latest surveys. Countries with a larger number of surveys spread over a longer time period allow a more precise estimate of time trends than countries with fewer surveys over a shorter time.
• Mortality rates, as mentioned above, refer to the 10-year period prior to each survey, therefore there is a lack of recent data on inequality. Countries that made recent progress, e.g. after 2010, may not be detected among the top performers.
• Like all statistical estimates, U5MR and SII are affected by sampling error; the Excel table provides information on the standard errors of all estimates

Authored by: Dr. Cesar Victora, Emeritus Professor of Epidemiology, Federal University of Pelotas, Link to Bio