Common themes about data gaps

Good quality data can help to drive informed decision making about implementation strategies and targeted investment.

In maternal and child health, the power of data to enhance accountability was made clear by the galvanising effect of the ‘Countdown to 2015’ movement, which monitored progress towards Millennium Development Goals (MDGs) 4 and 5, notably through improved measurement of the coverage of life-saving interventions around the world.

As we transition to the era of Sustainable Development Goals, it is important that this progress is sustained, but also that the measurement agenda is pushed forward and data gaps are narrowed. For example, until recently, investments in data for newborn and adolescent health have been low relative to their burden and potential for impact.

Here are some common themes about data gaps.

Common themes about data gaps across the lifecycle

Civil registration and vital statistics systems are often poor and require investment, particularly in high burden settings. Counting is essential: many countries today still lack timely data about how many women, children and adolescents die, when and why they die, and the numbers who survive and need effective interventions.

Better data to inform the implementation of programmes so that investments become more efficient is frequently lacking. These data need to be both timely and relevant at sub-national levels, and accessible to programme leaders who have the data literacy skills required to make data-informed decisions.

Survey data that measures population-level indicators is vital, but so too is the strengthening of routinely collected data that can be summarised more frequently through programme monitoring and through health information systems.

Inequalities between rich and poor populations

Inequities between rich and poor populations are pronounced for women, children and adolescents, despite intentions to deliver pro-poor services.

It is important that data systems are designed to reveal and track these inequities so that improvements to health are measured for all population groups and to maintain accountability for action that benefits those in poverty. Going forward, investment is needed in data collection that can be disaggregated for equity analysis.

Highlighted data gaps at each stage of the lifecycle

Adolescent

Many data gaps exist in adolescent health, in part because the health of young people has only recently been prioritised at the global level. Beyond the need to design data collection tools that specifically target the priorities in adolescent health, three design issues also lead to critical data gaps:

• Existing data collection systems frequently leave young people invisible as they fall between target child and adult populations, and sample sizes are not adequate to confidently estimate outcomes for this group
• There is a lack of standardisation in the measurement of adolescent outcomes, meaning that comparisons within and between countries can be limited
• The age of consent for participating in interviews can have the unintended effect of limiting young people’s engagement with surveys, particularly for younger adolescents aged 10–14 years.

Maternal

Continuing to invest in the measurement of maternal mortality is essential, but as survival rates improve this will necessitate increasingly large samples of women and greater financial investment. Near-miss methodologies are important to support the measurement of maternal health, but better routine reporting and maternal death reviews are also vitally important.

Currently, only a very small minority of the coverage indicators prioritised for global tracking target maternal health interventions, in part because they tend to be clinical and delivered in health facilities. Thus improved facility-based reporting mechanisms are needed rather than exclusively relying on women’s reports during surveys. Finally, the prioritisation of quality of care, including respect for women, urgently requires improvement in valid measurement methods that can be applied at scale.

Newborn

Preterm birth is the leading cause of neonatal mortality yet the ability to accurately estimate the gestation of pregnancies or gestational age at birth, severely limits the potential to deliver life-saving interventions to those in need. Accurate estimation of the number of newborns with severe bacterial infections, and the number who receive appropriate treatment for those infections, is similarly limited.

Improved data on stillbirths is an urgent priority. Further to these data gaps, the newborn experiences the same data limitations as the mother, especially with regard to the measurement of quality of care.

Child

Many of the successes in improved child survival have been accompanied by better data tracking of childhood interventions, even in high burden settings, for example, vaccination coverage and malaria prevention and treatment.

As for the other stages of the lifecycle, better country-owned systems for reporting must be developed, sustained, and include disaggregation by equity indicators. New data methods that can be scaled-up to estimate the child who thrives will be essential in the SDG era.