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Birds, Bats, or Humans … Gender Matters in Disease Preparedness and Response

Birds, Bats, or Humans … Gender Matters in Disease Preparedness and Response. Objectives. Understand and apply a cross-sectoral gender analysis framework to identify key gender disparities for zoonotic disease prevention, detection, and response.

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Birds, Bats, or Humans … Gender Matters in Disease Preparedness and Response

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  1. Birds, Bats, or Humans… Gender Matters in Disease Preparedness and Response

  2. Objectives • Understand and apply a cross-sectoral gender analysis framework to identify key gender disparities for zoonotic disease prevention, detection, and response. • Understand how One Health structures and interaction among different sectors can strengthen a gender-responsive approach to disease prevention, detection, and response.

  3. What is zoonotic disease? What do we mean by “One Health”…?

  4. A One Health approach to zoonotic disease… • Key factors: • Climate change • Globalization • Migration • Land use • Problemscreated: • Emergence of zoonoses • Food insecurity • Inequality • Poverty Gender Influences : Culture, economy, politics and policy, behavior

  5. Why gender matters… • Addressing the different risks and needs of all affected populations supports more effective disease response: better health outcomes… and economic security and well-being. • Ensure that the disease prevention, detection, and response efforts do not exacerbate existing inequalities.

  6. Women (and gender) in the Ebola outbreak “Women’s involvement in the evolution of the Ebola virus” (AfDB 2016)

  7. Illustrative considerations • health care decisions and use of formal and informal services • trade in food • wildlife conservation exposure to violence • household decision-making and control over resources • livestock management • participation in funeral ceremonies and burial practices caregiving for family members and friends who are ill access to information water and sanitation employment and access to markets • health workforce

  8. Review: domains of gender analysis Knowledge, beliefs and perceptions Legal status and entitlements Access to/control over assets & resources Power Practices, roles and participation Source: USAID Interagency Gender Working Group

  9. Scenario: Avian Influenza

  10. Putting gender analysis into practice – beyond human health…

  11. Debrief • What was one gender consideration that everyone agreed on/identified easily? • Were any boxes hard to fill? If so, what other information would have been helpful? • Did some factors apply to more than one box? • Did your group disagree on any points?

  12. The One Health solution: where does gender fit?

  13. Scenario: after the outbreak 19 human cases of avian influenza have been identified in the village. Sex and age-disaggregated data are not yet verified by the MoH, but a local NGO just published a report suggesting that of 12 reported fatalities in the village, all were women, including one community health worker. The commercial farm has been temporarily closed…

  14. Intervention design: what do you recommend? The country has a One Health coordination structure that is responsible for reviewing and updating disease prevention, detection, and response plans. Five priority areas have been identified for review. How would you design 1-2 associated interventions under each area to be more gender-responsive and to mitigate future similar outbreaks? What sectors need to be involved? What data/analyses/resources are needed? • Training for health clinics and community health workers • Public health awareness campaigns (long-term and emergency) • Animal vaccination, culling, and replacement programs • Improving centralized systems for data collection and analysis • Private sector coordination

  15. Debrief • What sectors need to be involved? Are there other institutional or individual actors? • What information do you have? • What information do you need? Are key data or analyses missing, or might that information be available from other sectors or sources?

  16. Thank You!

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