To help close the gender gap, we need to dig deeper into the data

12 September 2016

Portrait of Professor Robyn Norton

by Professor Robyn Norton
Professor of Global Health

Oxford Martin Senior Alumni FellowRobyn Norton is co-founder and Principal Director of The George Institute for Global Health, a not-for-profit medical research institute that aims to increase the provision of safe, effective and affordable healthcar...

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"We cannot close the gender gap without first closing the data gap” - these were the perhaps the defining words used by Melinda Gates, at the Women Deliver conference in Copenhagen in May, 2016 when she announced that the Bill and Melinda Gates Foundation would be committing US$80m to help reduce the gaps in data on women and girls.

The Foundation’s commitment provides financial backing to the announcement, at the same meeting, by a global consortium of government and non-government partners (including the UK government) to accelerate progress in meeting the UN Sustainable Development Goal (SDG) of achieving gender equality by 2030, through an increased focus and increased resources to tackle the core gender data challenges.

These announcements and commitments recognise the importance of high quality, comparable and regularly reported, sex-disaggregated data, to pick up where differences exist between women and men, identify the underlying causes and evaluate the impact of interventions to reduce them. They are an essential foundation for smart policy and the lack of such data has arguably contributed to the slower than necessary progress in achieving gender equality.

The specific focus of the Gates Foundation support is aimed at improving the collection of national household survey data in low and middle-income countries (LMICs). The voices of women are often absent in these surveys, as traditionally the data have been collected from the family breadwinners – usually the male members of the family – and so have disregarded the (unpaid) contributions of female family members.

Gaps in civil registration and vital statistics (CRVS), including data on births, deaths and marriages, are also a focus of the new commitments. While the ultimate aim is to address gender gaps in CRVS, up to 60% of all deaths occurring worldwide are not recorded. Improving CRVS for both men and women is thus an important first step, with Bloomberg Philanthropies recently providing US$100M to establish a Data for Health initiative aimed at doing just that.

But it's not just the collection of data that needs to be addressed. Closing the gender data gap will also require improvements in the analysis and reporting of existing data. Sex-disaggregated analyses of health data, in particular, have the potential to 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 impact women differently to men, and could help prevent millions of premature deaths.

Take for example, recent findings on dementia. While our understanding of the causes of dementia is still fairly rudimentary, we do know that the prevalence is greater in women than men, so understanding why this is the case is important for the development of prevention strategies. Recent analyses of data from over two million people have shown that individuals with type 2 diabetes may be at 60% increased risk of dementia. Examination of these data separately for women and men, however, suggests the additional risk of vascular dementia may be even greater for women.

Sex-disaggregated analyses of healthcare data are also important for reducing other gender data gaps. Such analyses have the potential to show whether disparities exist in access, and in the pathways and quality of healthcare for women and men. Recent analyses of data from individuals presenting to GPs in the UK from the Clinical Practice Research Datalink (CPRD), have shown that that the diagnostic interval between symptomatic presentation and cancer diagnosis is longer for women than men in six of the gender non-specific cancers: bladder (12 days longer); colorectal (10 days longer), gastric (14 days longer), head and neck (31 days longer), lung (8 days longer), and lymphoma (19 days longer). Longer diagnostic intervals are associated with poorer outcomes, but the good news is that, thanks to the data, we can take steps to address these disparities.

The momentum to address the gender data gap is growing. At the United Nations General Assembly in New York this month, the potential of data to help achieve the SDGs, and in particular gender equality, especially for women and girls in low and middle income countries (LMICs), will be a key part of the agenda. A focus on sex-disaggregated analyses and reporting of existing data should be as much a part of the agenda as the collection of new data.