Analyzing Output Trends in Data Impact Meetings
Explore the impact of private sector adjustments on data outputs and compare statistics from service reports and surveys using the SS to EMU tool. Understand differences, exclusions, and trend alignment for informed decision-making.
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Presentation Transcript
SS to EMU: Output Review Name, Data for Impact Meeting or event Date
Objectives Use the SS to EMU tool-generated graphs to make visual comparisons. Describe the data trends per method. Identify differences between data service statistics. Compare the data from service statistics and surveys. Explain the differences in trend intervals. Understand the overall trend from the EMU compared with the trend from survey data.
Impact of the Private Sector Adjustment on Outputs Review the graphs. Compare the adjusted and unadjusted estimates of users. Assess the improvement of the estimates with the private sector adjustment. Review the adjusted or unadjusted values alignment with the UNPD FPET and method mix from surveys. Revisit the private sector adjustment if estimates of users are distorted. Make changes to the adjustment factors.
Compare Trends and Comparability of the Service Statistics to Survey Data Trend by methods Method mix data consistency between service statistics and surveys Difference between service statistics and survey data Overall trend from the EMU compared with trends from surveys (if available) or from the UNPD FPET Growth rate from EMU compared with surveys or modeled estimates?
Confirm Exclusion of Condom Data from EMU Calculation Ensure that the system automatically excludes service statistics data on condoms from the EMU calculation. Data are excluded because: oCondoms are distributed for other uses besides FP. Prevention of HIV or other sexually transmitted infections oMethod of collecting the number of condoms distributed varies from other methods. Unit distributed can be a box or individual condoms oCondom distribution may not always guarantee use.
Group Exercise Group Exercise Ask participants to display and review the output results.
This presentation was produced with the support of the United States Agency for International Development (USAID) under the terms of the Data for Impact (D4I) associate award 7200AA18LA00008, which is implemented by the Carolina Population Center at the University of North Carolina at Chapel Hill, in partnership with Palladium International, LLC; ICF Macro, Inc.; John Snow, Inc.; and Tulane University. The views expressed in this publication do not necessarily reflect the views of USAID or the United States government. www.data4impactproject.org www.data4impactproject.org
