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

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Author(s): MEASURE Evaluation

Year: 2017

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

The availability of sex- and age-disaggregated data allows program managers and decision makers to examine service delivery, treatment, and health outcome data in depth. This helps them detect differences between the sexes, age groups, and key populations, which can lead to better understanding of the health needs of each of these groups and populations. Access to these data can also ensure that health systems do not perpetuate inequities associated with negative health outcomes. Despite the importance of examining gender and age differences, sex and age disaggregation are not always included or maintained in routine data collection practices and national HIS databases, such as the DHIS 2. Collection and use of gender-related data are increasing globally, but information gaps still prevent full understanding of the factors that facilitate or discourage helpful data disaggregation and use.

To enhance the availability and use of gender data, MEASURE Evaluation—funded by the United States Agency for International Development (USAID) and the United States President’s Emergency Plan for AIDS Relief (PEPFAR)—explored factors that contribute to collection and use of sex- and age-disaggregated data in Kenya. Our study used a two-pronged approach: (1) a desk review of key documents and literature, and (2) key informant interviews (KIIs) with national-level data producers and decision makers.

Many variables have an impact on when and how data are disaggregated, but we found common barriers and facilitators around the availability, production, and use of sex- and age-disaggregated data. The most common barriers to producing disaggregated data were low demand and the view that disaggregation was unnecessary. These barriers influenced data-collection tool design. We found that the availability of data by sex and age depended on the tool that was used and what type of data was collected. HIV data were generally disaggregated by sex and age, but there was variation in which age bands were used. Key informants (KIs) for this study said that registers at the facility were disaggregated by sex. This was confirmed by review of data collection tools. However, when aggregated into summary tools, the male and female fields were often aggregated into number of people, as programs did not require disaggregated summary reporting.

We also found that the production of disaggregated data was limited by the availability of resources and the added burden of reporting this type of data. Key informants strongly believed that data being collected should be used, or else it should not be collected. The KIs explored successes and challenges in analyzing and using disaggregated data. They were not sure who was responsible for ensuring disaggregation and providing technical support. Key informants working with PEPFAR data cited successes and supportive strategies more frequently than KIs in other health areas that also had implications for HIV (such as tuberculosis, malaria, and immunizations).

Our desk review revealed that a majority of Kenya’s HIV reports include sex and age disaggregation in their data presentations and discussions. Kenya’s progress in gender integration and sex and age disaggregation should be applauded. Kenya has shown substantial progress and has lessons to share with other countries, as it continues to strengthen data collection, analysis, and use of disaggregated data. Nevertheless, significant challenges remain that will require continued support to address.

At the end of this report, we offer recommendations for increased advocacy and awareness at all levels around the importance of data disaggregation by sex and age. We call for the development of guidelines, materials, and examples of how such data should be analyzed to reveal important findings. We also recommend support from gender-mainstreaming officers throughout program cycles to ensure production and use of sex- and age-disaggregated data.

Filed under: Kenya , Africa , Data