Data availability

Does a country have the data needed to accurately track and report on progress and monitor health trends? Often, the answer isn’t as simple as yes or no. Some countries may have an abundance of data, but that data is lacking in quality. Other countries may have good quality data, but not for all relevant topics.

For global monitoring of HRSDGs, an indicator may be reported using either ‘primary data’ or ‘comparable estimates’.

• Primary data comes from country health information systems (including administrative reporting, household surveys, etc.). Data is reported as is, or with modest adjustment. 
• Comparable estimates are data that have been adjusted or modelled to allow comparisons between countries or over time. Comparable estimates are produced for countries with underlying primary data and, in some cases, also for those without.

Regardless of the type of statistics used, it is important to have recent country-level data; either (a) to be used directly, for indicators reported using primary data, or (b) to be used as underlying data, for indicators reported using comparable estimates. For this visualization, we use three categories of availability: recent, old or no data.

For indicators reported using ‘primary data’, the three categories are defined simply as follows:

•  No data:  no country-level data, or from before 2010.
•  Old data:  country-level data from 2010-2014.
•  Recent data:  country-level data from 2015 or later.

For indicators reported using ‘comparable estimates’, the three categories are defined based on the availability of underlying primary data:

•  No data:  estimate generated without direct underlying country-level data since 2000.
•  Old data:  estimate generated using underlying country-level data from 2000 up to five years from the year of the estimate.
•  Recent data:  estimate generated using underlying country-level data within four years of the year of the estimate.

The best is to have recent data. However, as we can see from the charts below – and as demonstrated by the WHO’s World Health Statistics 2020 report – there is still a lack of this data in many parts of the world.

This chart confirms that many health-related SDG indicators are lacking crucial data. It allows us to see the World Health Statistics 2020 indicators by the number of countries that have data (recent, old or no data) for each of them. ‘Not relevant’ applies where that indicator is not applicable (e.g. the Malaria indicator is only applicable to the 88 countries affected by this disease).

But it also reveals something far more significant. There is a huge variance in the amount of recent data across the different indicators. For example, 100% of countries have recent underlying data to estimate ‘Air pollution’, whereas only 9% have recent data for ‘Essential medicines’ that are available and affordable on a sustainable basis.

Here we turn our focus to countries – grouped by income level using World Bank classification (2018) – to see what percentage of health-related SDGs have recent data. 

There are two key takeaways. Firstly, most higher-income countries have more recent data for the HRSDGs, clustering around 60-80%. However it’s clear there are big differences not just between but also within income groups.

Secondly, there are 35 countries (five high income, 22 middle income and eight low income) where over half the indicators lack recent data.

It is important to note that it is likely that more data exist at the country level but was not available for use for global monitoring of HRSDGs. This could be due to the timing of data compilation by WHO and other agencies responsible for global monitoring, or due to data quality.

Put simply: if we lack data, we lack understanding.

Data sheds light on complex situations. Without it, problems remain half-seen or completely hidden. Without good or complete data, we are making decisions in the dark. Many governments still lack adequate and disaggregated data to fully understand the needs of the most vulnerable people.

From unmasking previously obscured disparities in health to developing informed policies and prioritising interventions, data can drive communities to achieve both equality and equity.

Furthermore, data should be used to monitor changes from the beginning of the century through the Millennium Development Goals (MDGs) and the SDGs for accountability. So more data can also mean more accountability.

Even when data is available, it can have limitations. Incomplete coverage, inconsistencies, time lags, numbers that are not nationally representative and lack of disaggregation are just a few of the challenges to face.

Under-5 mortality rate represented below is an example of the need for data disaggregation by sex.

We can see that aggregated ‘both sex’ data (both sexes combined) is more often reported to a higher quality and is more readily available than disaggregated sex-specific data (broken down by male and female).

This is a data availability issue. It means we are unable to fully identify and address inequalities in these countries where disaggregated data is not available.

This Under-5 mortality example is taken from the World Health Statistics 2019 report, but it serves to show an important overall trend: there is more recent data available for ‘both sexes’ data than sex-specific data.

Ideally, they would be available to the same degree. Yet instead, 10% of countries have recent ‘both sex’ data but not recent sex-specific data, which is essential to see and understand health inequalities in specific contexts.

When a country has data gaps, it has many serious problems – a great number of which they may not be aware of or be equipped to confront.

The only way to close these gaps is with accurate, timely and comparable data. And this can only be achieved by putting in place or strengthening a number of systems.

Here are three examples of data systems that are a vital requirement to track health trends and address data gaps:

• Civil registration and vital statistics (CRVS)
  CRVS is a data system that tracks statistics on births and deaths. Despite its importance, many parts of the world don’t have a CRVS data system in place. In fact, 80% of the world’s populations have either lower-quality cause-of-death data or no data at all.
  
  WHO is working with Member States and partners to strengthen CRVS systems and provide direct technical support to countries.

• Administrative, health services and facility data
  To track administrative, health services and facility data, several subsystems are required. These include routine health information systems (RHIS), registries and health facility surveys.
  
  WHO offers multiple tools and packages that countries can use to set standards, monitor progress and analyse information. For example, the Data Quality Review Toolkit strengthens RHIS. Health-facility survey modules also assess health facilities’ ability to provide quality health care.

• Population-based surveys
  Household surveys provide vital knowledge about a population. They reveal key insights about health trends and data around inequalities within countries. They also capture important information around social determinants of health (e.g. air quality, sanitation, mental health) that may not be included in traditional health facility surveys. Population-based surveys can be implemented rapidly – including via innovative tools like mobile phone technology – to collect representative data on a number of topics.
  
  The World Health Survey Plus (WHS+) is a multi-topic, multi-mode, multi-platform survey used to address important data gaps and can be tailored to a country's specific needs.

These are just a few of the ways that WHO, along with its partners, is committed to supporting countries as they take ownership of collecting, monitoring and analysing data for effective policy action.

To fully realise and address data gaps within countries, we need to further understand the quality of a country's existing health data systems.

We can now achieve this more effectively than ever before thanks to the SCORE for Health Data Technical Package (Survey, Count, Optimize, Review, Enable). It represents – for the first time in a single, harmonized package – all the essential elements required for health systems strengthening.

This timely resource is designed to identify strengths and weaknesses in a country's data and health information systems and will help guide limited resources to areas with the greatest impact. It will also equip governments with the necessary tools to use data for informed policy design and decision-making.

SCORE lays the foundation for high-quality data in countries, which is a necessary requirement to track progress towards the health-related SDGs and WHO Triple Billion targets. This means data that is accurate, timely, reliable, comparable and available for use.

Time is running out to invest in data and health information systems to address these critical data gaps – and COVID-19 highlights this need even more.

With the potential to jeopardise decades of progress that’s already been made towards the SDGs, COVID-19 has dramatically emphasised the world’s inability for effective emergency response without high-quality and timely data.

This is nothing new, of course. Many of the problems stemming from data gaps have been encountered numerous times over the last five years in tracking the SDGs.

Yet while highlighting the urgent need for data, the pandemic has also impeded many of the traditional methods of data collection. For example, lockdown has hindered national statistical offices’ capacity to monitor the progress on the SDGs.

A recent survey by the United Nations and the World Bank highlighted the struggles faced by these national statistics offices in the fight against COVID-19 and illustrated that:

• 65% of headquarters are partially or fully closed
• 90% have told staff to work from home
• 96% have partially or fully stopped face-to-face data collection

Even more worryingly, 9 in 10 national statistical offices in low- and lower-middle-income countries have seen funding cuts, putting future routine monitoring at risk.

With better-quality data, we get a better understanding of the world and could address health inequalities in a more efficient manner.

This is why urgent investments and partnerships are needed to strengthen data in countries, from increasing capacity and infrastructure to training people and strengthening institutions.

Improved, comprehensive health information systems can solve many of the current challenges in the collection, analysis and use of data. They can better forecast health emergencies, increasing a country's preparedness to respond quickly when required.

With timely, reliable and actionable data we will be able to monitor changes in health indicators to make informed decisions that will accelerate progress towards the SDGs and Triple Billion targets at country, regional and global levels.