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

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Webinar presented on June 29, 2017, by MEASURE Evaluation’s Abby Cannon, Carolina Mejia, and Jessica Fehringer.

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

  1. 1. Demystifying Disaggregated Data: Factors that Affect Collection and Use of Sex- and Age-Disaggregated Data Jessica Fehringer, PhD, MPH MEASURE Evaluation University of North Carolina June 29, 2017
  2. 2. Outline 1. Introduction/Background 2. Barriers and Facilitators to Availability and Use of Sex- and Age-Disaggregated Data (Abby Cannon) 3. Gender-Related Data Quality Assessment: Lessons from a pilot (Carolina Mejia)
  3. 3. Definitions Sex Gender Biological classification of males and females Determined at birth, based on biological characteristics Hard to change Socially constructed roles, duties, rights, responsibilities, and accepted behaviors associated with being male and female Varies across cultures and over time
  4. 4. Gender and Health Adherence to rigid gender roles can create a gender gap • Unequal options, opportunities, and realities that women and men experience Women Men Source: World Health Organization, “Why Gender and Health?”
  5. 5. Gender Matters in Health Gender inequality is associated with poor outcomes • Higher child mortality, stunting, and wasting • Lower maternal healthcare use; higher maternal mortality • Higher fertility; lower family planning use • Gender-based violence (GBV) Gender inequality is a major driver of the HIV epidemic • Leads to less awareness and knowledge of HIV • GBV fuels the HIV epidemic • More risky sex; less HIV testing and treatment
  6. 6. Why Age Matters Gender norms may differ between age and sex groups; they affect health risk factors and use of health services. Example: The “sugar daddy” or “blesser” phenomenon. Girls and young women in sub-Saharan Africa have a higher risk of acquiring HIV than older women and than male peers.
  7. 7. Gender and Health Systems How can health systems address gender inequality? An integrative approach considers the impact of gender on the people, the health program, and the results. Health problem Gender- specific needs and barriers Desired health outcomes
  8. 8. Health Information Systems Data Why gender? Availability and use of disaggregated data allow program managers and decision makers to: • Examine service delivery, treatment, and health outcome data in depth • Reveal differences between sexes, age groups, or key populations • Make informed decisions
  9. 9. Barriers and Facilitators Availability and Use of Sex- and Age- Disaggregated Data Abby Cannon, MPH, MSW MEASURE Evaluation University of North Carolina June 29, 2017
  10. 10. 1) Determine the availability of sex- and age- disaggregation of HIV and HIV-related health indicators in two countries 2) Explore if and how sex- and age- disaggregated data are used for decision making 3) Investigate the facilitators and barriers to collecting and using sex-disaggregated data across indicators Objectives
  11. 11. Methods 1) Desk review • Understand the current levels of sex and age disaggregation in national documents, including discussion of gender issues. 2) Primary data collection • Key informant interviews (n=28) • Explore barriers to, and facilitators of collecting, reporting, and using sex- and age-disaggregated data • Interviews were coded to identify relevant themes
  12. 12. Results: State of Disaggregation HIV data are almost always disaggregated by sex and age • Variation in age bands used • HIV retention data are captured by sex when electronic data collection is available but not when data are collected on paper
  13. 13. Results: State of Disaggregation • Registers at the facility are disaggregated by sex • When aggregated into summary tools, the male and female fields are often combined into total number of people • More common for non-HIV technical area: immunization, nutrition, tuberculosis, malaria • Age bands vary more in non-HIV area Collected by sex & age Aggregated Aggregated
  14. 14. Results: Use of Data • Most key informants (KIs) could not offer examples of using disaggregated data for decision making. • Most KIs expressed interest in seeing examples of how disaggregated data have been used for program and policy decisions. • Opinions varied about who was responsible for ensuring disaggregation and providing technical support.
  15. 15. Facilitators Value Reporting Requirements Usefulness “Epidemiologically, why would you not want to look at male/female unless the disease is only for females or only for males?” ─Kenyan stakeholder “The disaggregation brings in the human aspects of what you’re looking at. You stop seeing statistics, and start thinking of them as people.” ─ Zambian stakeholder
  16. 16. Barriers Value Reporting Requirements Usefulness Value Reporting Requirements Lack of Understanding Resource Burden Reporting Requirements Lack of Understanding Resource Burden Program Priorities Lack of Understanding “Under nutrition, disaggregating wouldn’t necessarily be that helpful. Children are children.” ─ Zambian stakeholder “Once I enter into the register, I now have to get another paper and start recording. And I don’t have time. And these then go to another worker. It’s time for an electronic system, so we don’t burden the health worker.” ─ Zambian stakeholder
  17. 17. Gender-Sensitive Indicators • Knowledge of gender-sensitive indicators varied • Few gender-sensitive indicators are routinely collected • Most key informants identified gender-based violence (GBV) as a gender-sensitive indicator that is collected • When GBV data are collected, some forms disaggregate by sex; others do not • Often sexual violence is recorded, as well as provision of post-exposure prophylaxis (PEP), but other incidents may be lost
  18. 18. Conclusions • Much progress has been made in gender integration and sex- and age- disaggregation in health information systems. • Gaps remain and increased efforts in data disaggregation should be approached in the following way: • As a collaborative endeavor, to avoid overburdening healthcare workers while balancing essential data needed to identify and address inequities
  19. 19. Recommendations (1) Advocacy and awareness: • Increase advocacy and training at all levels about importance of disaggregation in health information systems • Increase awareness of how disaggregation helps meet program goals • Include gender focal persons for advocacy and technical assistance
  20. 20. Recommendations (2) Data training and guidance: • Develop guidelines on how disaggregated data can and should be used • Improve capacity at the facility and national level to use disaggregated data for decision making
  21. 21. Recommendations (3) Improved systems: • Maintain disaggregation throughout the health information system • Expand electronic data when feasible • Conduct spot checks of records or data verification of disaggregation • Incorporate GBV in routine data
  22. 22. Resources Kenya: https://www.measureevaluation.org/resources/pu blications/tr-17-163 Zambia: https://www.measureevaluation.org/resources/pu blications/tr-17-160 Related publication from Tanzania: https://www.measureevaluation.org/resources/pu blications/tr-16-132 Brief: https://www.measureevaluation.org/resources/pu blications/fs-17-215 Detailed country results and recommendations
  23. 23. Questions?
  24. 24. Headline goes here Headline goes here Headline goes here Author Name and Degree Here MEASURE Evaluation Your organization here Date for presentation if necessary Name of meeting Gender-Integrated Routine Data Quality Assessment Lessons Learned from a Pilot Carolina Mejia, PhD, MPH University of North Carolina MEASURE Evaluation June 29, 2017
  25. 25. Objectives 1. Adapt and pilot a supplemental tool for assessing gender integration during routine data quality assessment (RDQA+G ). 2. Assess M&E strengths and weaknesses of Implementing partners (IPs) of gender- integrated data. 3. Strengthen the capacity of IPs to collect and use good-quality gender-related data, without external support.
  26. 26. Methods The RDQA Process Data Verification System Assessment Interpret the Output Develop Action Plans Disseminate Results Ongoing Monitoring & Follow-Up
  27. 27. Methods: System Assessment M&E structures, functions, capabilities Indicator definition & reporting guidelines Data collection, reporting, forms, and toolsData management processes Evidence informed decision making Links to the national reporting system GenderIntegration
  28. 28. Additional Gender Items II – Indicator Definitions and Reporting Guidelines If the M&E Unit has disseminated formal indicator definitions, is this documentation accessible to staff? Do indicator definitions include description of data disaggregation by sex? Do indicator definitions include description of data disaggregation by age groups? If the M&E Unit has disseminated reporting guidelines (what to report, the required template, to whom the report should be submitted, and the due date), is this guidance accessible by relevant staff? III – Data Collection and Reporting Forms and Tools If the M&E Unit has identified standard data collection and reporting forms/tools to be used at this reporting level, are these forms/tools consistently used? If not, why in the comments section. If the M&E Unit has provided instructions on how to complete the data collection reporting forms/tools, are these instructions followed/adhered to? If not, explain in the comments section. Do data collection and reporting tools allow disaggregation by sex? Is there a clear documented instruction on this? Do data collection and reporting tools allow disaggregation by age groups? Is clear documented instruction on this?
  29. 29. Methods • Pilot RDQA+G conducted with PEPFAR IPs: 2 in Kenya and 2 in Zambia at 10 sites between May and December 2016. 1. HTC_TST = number of individuals who received HIV testing and counseling (HTC) services for HIV and received their test results. 2. GEND_GBV = number of people who received post- GBV care. 3. OVC_SERV=number of active beneficiaries served by PEPFAR programs for children and families affected by HIV and AIDS. • Time frame: 6-month period (SAPR16)
  30. 30. Findings
  31. 31. Summary System Assessment HTC_TST (Overall and Gender) Site M&E Structure, Functions, and Capabilities Indicator Definitions and Reporting Guidelines Data Collection and Reporting Forms/Tools Data Manage- ment Processes Evidence- Informed Decision Making Links to the National Reporting System Kenya 3.3 2.9 3.7 2.7 2.3 3.4 Zambia 3.6 3.3 4.0 3.0 2.1 3.7 (Score range from 1 to 4) Site M&E Structure, Functions, and Capabilities Indicator Definitions and Reporting Guidelines Data- Collection and Reporting Forms/ Tools Data Manage- ment Processes Evidence- Informed Decision Making Links to the National Reporting System Kenya 1.6 4.0 4.0 1.0 0.8 3.3 Zambia 1.7 2.5 4.0 2.0 4.0 N/A
  32. 32. Gender Results
  33. 33. Areas of Strength: Gender 1. Sex- and age-disaggregated data are collected for all three indicators of interest. 2. Most data collection and reporting tools allow disaggregation by sex and age. 3. Some staff have received training on gender. 4. There is willingness to learn more about and integrate gender.
  34. 34. Areas for Improvement: Gender 1. Gaps exist around gender in monitoring and evaluation (M&E) structures, guidelines, and evidence-informed decision making. 2. Data entry for age and sex in registers is inconsistent over the three indicators. 3. The orphans and vulnerable children database needs a “sex” indicator in one country. (It is in the logs but not in the IP database.) 4. M&E staff lack training on gender, and facility-level staff lack training on use of gender data. 5. Regular supervision and data quality checks are lacking.
  35. 35. Recommendations (1) For IPs: 1. Monitor progress over time in reporting accuracy, timeliness, and completeness of gender data 2. Include the following: • Basic gender training for staff with minimal gender experience (a refresher for other staff) • Capacity building on gender analysis for data use and decision making
  36. 36. Recommendations (2) 3. Develop and/or disseminate a gender guidance and mainstreaming document, where appropriate. 4. Ensure that project-specific databases include data entry fields for sex and age.
  37. 37. Recommendations (3) For USAID/Mission 5. Clarify PEPFAR expectations around gender integration and analysis for IPs. 6. Facilitate IP gender training and data use and provide gender mainstreaming documents to IPs, where appropriate. 7. Consider using RDQA+G in place of RDQA.
  38. 38. Conclusion • RDQA+G was successfully completed. • The additional gender items can be a useful resource for IPs to focus on gender-related data. • The RDQA+G team noted many strengths in the performance of the M&E system at the M&E Unit and service delivery points. • There are areas that need to be improved at the SDP level, with guidance from the M&E Unit. • MEASURE Evaluation will conduct a capacity building workshop in 2017.
  39. 39. Resources Guidelines for Integrating Gender into an M&E Framework and System Assessment www.measureevaluation.org/resources/publications/tr-16-128-en DQA Auditing Tool Implementation Guidelines www.measureevaluation.org/resources/tools/health-information-systems/data- quality-assurance-tools/dqa-auditing-tool-implementation-guidelines/view RDQA with Gender Tool https://www.measureevaluation.org/our-work/gender/gender-integrated-routine- data-quality-assessment-rdqa-g-tool/gender-integrated-routine-data-quality- assessment-rdqa-g-tool Have feedback on the RDQA+G? We’d love to hear it! Email us at: cmejia@email.unc.edu
  40. 40. Questions?
  41. 41. This presentation was produced with the support of the United States Agency for International Development (USAID) under the terms of MEASURE Evaluation cooperative agreement AID-OAA-L-14-00004. MEASURE Evaluation is implemented by the Carolina Population Center, University of North Carolina at Chapel Hill in partnership with ICF International; John Snow, Inc.; Management Sciences for Health; Palladium; and Tulane University. Views expressed are not necessarily those of USAID or the United States government. www.measureevaluation.org

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