Factors that Affect Collection and Use of Sex- and Age-Disaggregated Data

MEASURE Evaluation has released three publications to help donors, governments, and groups working on health information systems to better understand factors that affect collection and use of sex- and age-disaggregated data.

© 2007 Bonnie Gillespie, courtesy of Photoshare
© 2007 Bonnie Gillespie, courtesy of Photoshare
MEASURE Evaluation, funded by the United States Agency for International Development, has released three publications to help donors, governments, and groups working on health information systems (HIS) to better understand factors that affect collection and use of sex- and age-disaggregated data. 


Such gender-related data are critical to understanding how HIV/AIDS may manifest or impact different population groups and whether programs are meeting the unique needs of such groups. Inadequate availability of sex- and age-disaggregated data makes it harder to examine access to treatment across the HIV cascade.[1]

Sex- and age-disaggregated data, as well as gender-sensitive indicators that specifically measure gender equality, are also important for efficient and effective use of limited resources. Every country should strive to develop the capability for basic equity analysis.[2] Nevertheless, large gaps remain in the collection and use of such data, obscuring inequities and barriers to reaching health goals.

To better understand what impedes the collection, analysis, and use of disaggregated data, MEASURE Evaluation conducted an exploration at the national level in two countries in sub-Saharan Africa. From a desk review and key informant interviews in Kenya and Zambia, we have produced two reports and one document brief that compile the evidence and illustrate trends and challenges around sex- and age-disaggregated HIS data and provide recommendations to advance the field of global health.

Barriers to and Facilitators of Sex- and Age-Disaggregated Data – Kenya

Barriers to and Facilitators of Sex- and Age-Disaggregated Data – Zambia

Factors Affecting Sex- and Age-Disaggregated Data in Health Information Systems – Lessons from the Field

Sex- and Age-Disaggregated Data 



[1] Croce-Galis, M., Gay, J., Harde& e, K. (2015). Gender considerations along the HIV treatment cascade: An evidence review with priority actions. Treatment brief for the USAID Evidence Project and What Works for Women and Girls. Washington, DC: PEPFAR. Retrieved from: http://www.whatworksforwomen.org/system/attachments/76/original/Gender_Considerations_Along_the_HIV_Treatment_Cascade.pdf?1442623311

[2] Nolen, L.B., Braveman, P., Dachs, J.N.W., Delgado, I., Gakidou, E., Moser, K., . . . Zarowsky, C. (2005). Strengthening health information systems to address health equity challenges. Bulletin of the World Health Organization, 83(8), 579–603. Retrieved from https://iths.pure.elsevier.com/en/publications/strengthening-health-information-systems-to-address-health-equity