"We cannot close the gender gap without first closing the data gap."
This was perhaps the defining statement at the Women Deliver conference in Copenhagen in May 2016. It was made by Melinda Gates, in announcing that the Bill and Melinda Gates Foundation would be committing $80 million to help reduce the gaps in data on women and girls.
At the same meeting, a global consortium of government and non-government partners announced accelerated progress toward meeting the UN Sustainable Development Goals (SDGs) of achieving gender equality by 2030 through an increase in focus and resources aimed at addressing the core gender data challenges.
The timing of this announcement is exciting, because the Gates Foundation's commitment provides the consortium's initiatives significant financial backing.
These announcements and commitments recognize the importance of high quality, sex-disaggregated data in determining, first, whether differences exist between women and men; second, the underlying causes of these differences; and third, the impact of interventions to reduce them. Sex-specific data is an essential foundation for smart policy -- and the lack of such data has contributed to slower progress in achieving gender equality.
The specific focus of the Gates Foundation's support is aimed at improving the collection of national household survey data in low and middle-income countries (LMICs), which involves both the collection of new data as well as improving existing data collection processes. The voices of women are often absent in these surveys, which, traditionally, have collected data from the family breadwinners -- usually male -- and as a result have disregarded the (unpaid) contributions of female family members.
Gaps in civil registration and vital statistics (CVRS), including data on births, deaths and marriages, are also a focus of the new commitments. The ultimate aim is to address gender gaps, but improving CVRS for both men and women is an important first step. Toward this end, Bloomberg Philanthropies recently provided $100 million to establish a "Data for Health" initiative.
Closing the gender data gap, however, won't be achieved just through improvements in data collection. We must also improve the way existing data are reported and analysed.
Sex-disaggregated analyses of health data, in particular, can go beyond simply identifying differences in the incidence and prevalence of disease or risk factors. Such analyses enable the identification of risk factors that may be unique to women, or risk factors that may affect women differently than men.
Sex-disaggregated analyses of healthcare data are also important for reducing gender data gaps. Do disparities exist in access to and quality of healthcare for women and men?
A recent analysis of information presented to the Clinical Practice Research Datalink in the UK suggests yes. What they've shown is that the diagnostic interval between symptomatic presentation and cancer diagnosis is longer for women than men for several gender non-specific cancers. Longer diagnostic intervals are associated with poorer outcomes, meaning, sadly, that if you're a woman, you're more likely to die than a man with the exact same disease.
The good news is that the momentum to address the gender data gap is growing.
At the UN General Assembly in New York this month, the potential of data to facilitate achievement of the SDGs and in particular the potential for data to facilitate gender equality -- especially for women and girls in LMICs -- will be key agenda issues.
The collection of new data is undeniably important. But as we pursue this, we in the global health community must ensure that a focus on sex-disaggregated analyses and reporting of existing data is just as much a part of the agenda.