Agenda 2030: Challenges and opportunities for better data across all ages

Editor’s note: This op-ed was written by Alex Mihnovits on behalf of Age International, Plan International, Christian Aid and HelpAge International.

The population under 15 years old and over 60 years old represent 3.5 billion people globally — or nearly half the world’s population. But most of the very young and old are excluded from statistics because they are left out of data collection. And even where data is collected, it is often not fully analyzed, reported on or utilized.

Fulfilling the promise of Agenda 2030 for Sustainable Development to leave no one behind represents a challenge. But we are hopeful that much can be done with the proper commitment, tools and resources from national governments, multilateral and civil societies organizations, and other stakeholders.

This week, the U.N. Expert Group Meeting on Data Disaggregation has an opportunity to identify short and long term steps to ensure full data disaggregation at the U.N. and UNICEF Expert Group Meeting on Data Disaggregation. This meeting will gather experts to review current standards and tools are for disaggregating data by sex, age, disability, geography and other characteristics, as required by Agenda 2030. Similarly the U.K. Department for International Development recently gathered expert input on how to better disaggregate data by age, sex and disability.

These initiatives provide a welcome opportunity to identify concrete steps for improving data disaggregation across people’s lives and so in turn to improve our policy responses to their needs.

Missing data

Older populations are often not fully considered in statistics. Take for example, Lilian, 58, a widow from Thika in Kenya who is HIV-positive and looks after her 89-year-old mother, three orphaned grandchildren and two boys who were abandoned by their parents. Yet because she is over 49, she’s excluded from statistics on HIV, including the Demographic and Health Survey and UNICEF’s Multiple Indicator Cluster Survey. Collecting comprehensive information would supply crucial details about the number of older people affected by HIV and lead to better program design and delivery.

Limitations in data gathering and analysis extend beyond development, to learning about the rights and needs of younger and older people in humanitarian situations. A 2011 Tufts University study found “almost no documented and published cases in which lead agencies […] collected sex- and age-disaggregated data properly, analyzed the data in context and used those findings to influence programming.”

Among younger populations, girls are especially vulnerable to sexual violence, but in many cases, they are missing from formal data collection, masking the true scale of the problem. Neema (whose name has been changed) was attending her rural primary school in Kenya, when she was allegedly sexually assaulted by a boy in the village. After complaining, the boys’ parents offered monetary compensation as the age of consent is 15-years-old. This is a common approach to settling child abuse cases in the community but denies children justice because their cases have not been reported to the authorities.

Neema’s case was reported to the Children’s Department via VuruguMapper, a real-time data tracker on allegations of sexual abuse in the community. Currently the case is progressing in court. Community action to identify and collect evidence that can be used to augment official data sources is vital.

Improving data collection

As there is no international consensus about the collection of data, there are a number of challenges in analyzing and comparing data from different sources and across countries.

Disaggregating data can also be statistically challenging when the target group represents a small proportion of the population, as is sometimes the case with older people, people with disabilities, or ethnic minority groups. Small sample sizes mean that often age and gender cohorts may be lumped together within a general larger group.

Additional resources are needed to ensure we have representative information about all population groups if we are to have meaningful policy and programs targeting these particular groups.

DfID initiated an open policy making consultation to improve the collection, analysis, reporting and utilization of data for the young and old. Specific suggestions that came out of the consultation included:

1. Expand existing global surveys.

DHS, MICS, and the World Values Surveys could include early adolescents and people over the age 49. The DHS already has plans to raise upper age limits in Haiti (2016) and Cameroon (2017) to 64, and have no upper age limit (15-plus) in South Africa (2016). These initiatives could provide crucial data on older people’s attitudes and behaviours in relation to HIV. The household survey collects data about all household members, education levels, available facilities; ownership of land and livestock and whether an urban or rural setting. From this, more information can be gleaned about the lives of older people although it is limited.

2. Make maximum use of existing data.

Information about younger and older people already exists in some data sets. Data Mapping on Ageing in Asia and the Pacific Analytical Report shows that out of 25 countries, 24 had censuses, 16 had DHS surveys 2 and 10 had national surveys. These censuses and surveys contain socio-demographic data on older people, showing that data exists — but there needs to more analysis to close knowledge gaps.

3. Develop new data collection, analysis and reporting mechanisms.

There are a number of areas where existing data gathering tools simply do not capture sufficient information. For example, on violence and abuse at all ages; LGBT youth; and old and young people in institutions. New data collection, analysis and reporting mechanisms are required. An example of the response is introduction of new modules on noncommunicable diseases, disability, accident and injury by the DHS.

Where the young and old are concerned, all stakeholders can start by recognizing that we need to gather and analyze data for all stages of a person’s life. An inclusive approach means recognizing the intersection between different stages of a person’s life, gender, disability and location, to name but a few relevant characteristics.

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