Having followed the UN Secretary General’s report on the Data Revolution closely we have argued that the framing of the ‘A World That Counts’ report emphasises global solutions and processes, in particular the Sustainable Development Goals (SDGs), at the expense of national priorities and capacities. I was keen to see how thinking on this ‘revolution’ has influenced the way the SDGs will be monitored.
I am currently involved with a pilot project in two rural districts of Uganda exploring how joined up, disaggregated district and sub-district data can be transformed into usable information for local decision makers. One of our case studies focuses on data that may illuminate why maternal mortality still remains so high.
Our first port of call was to look for data on mortality itself. National data has been published for the Millennium Development Goals and it looks impressive.
Being neither a statistician nor a health expert I assumed we would find this national data reflected at the district level, particularly as Uganda has a very good (though not public) Health Management Information System (HMIS). I was about to learn my first proper lesson in the Data Revolution.
Firstly, women’s organisations in Uganda are still protesting against excessive deaths. Why?
Secondly, there is currently no local data.
Most deaths in rural districts take place at home in the villages, and the clinics aren’t aware of them. In discussion with a statistician from the Ministry of Health we were told that even if the clinics did have the data they were unlikely to report it accurately as it would reflect badly on their service. (This is not true of all the data in the HMIS; it has, for example, very good highly disaggregated data on ante natal care.) The good news is that the Uganda Registration Services Bureau is introducing a new system for recording births and deaths using mobile technology. The bad news is that it will take a while for it to become fully operational.
Here is what the Millennium Development Goals manual has to say about sources and data collection:
“Primary sources of data include vital registration systems, household surveys, reproductive age mortality studies, disease surveillance or sample registration systems, special studies on maternal mortality, and national population censuses. Complete vital statistics registration systems with accurate cause of death estimations are the most reliable data source for calculating maternal mortality and monitoring change over time. However, these are rare in developing countries. Official data are usually available from health service records, but few women in rural areas have access to health services. Therefore in developing countries, survey data, especially those from the Demographic and Health Surveys (DHS) and similar household surveys constitute the most common source of data on maternal mortality.” (Emphasis is my own)
This makes total sense. A pragmatic decision was taken in the short term to rely on calculations from survey data, but a roadmap towards a longer term sustainable solution – registration systems – was also clearly defined.
The Data Revolution approach to this problem couldn’t be clearer: there is a need to invest and build capacity in registration systems recording births and deaths. We are, after all, talking about sustainable development.
Here is what the SDSN’s draft report recommends for the SDG indicator on maternal mortality:
- Comments and limitations: “Metrics are difficult to measure as vital registration and health information systems are often weak in developing countries.”
- Primary data source: “Complete vital statistics registration systems are the most reliable data source, but these are rare in developing countries so household surveys are often used.”
- Disaggregation: “As data systems improve, it will be important to disaggregate by age, geographic location (e.g. urban vs. rural), and income level.”
Not a very revolutionary step forward. The introduction to the report stresses the importance of strengthening capacity, but in the body of the report this is ignored.
The introduction also notes the importance of geographically disaggregated data. The example ‘best case’ scenario presented here is that national statistics should distinguish between ‘urban vs rural’. Is this the Data Revolution that will enable national and local decision makers to take better decisions within their country and compare their data across districts? Is this the Data Revolution that will ensure that health and education services can access geographically disaggregated data to properly manage their resources?
The SDSN’s report appears to be focused on what can be measured by January 2016. Such short-term pragmatic priorities have a way of entrenching themselves. It will become all too easy to lose sight of the bigger, long-term picture. It is critical that those who believe that the Data Revolution is a useful banner under which to deliver sustainable information infrastructures make their voices heard.