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Use of Routine Data for Economic Evaluations

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This Data for Impact webinar took place October 29, 2020. Learn more at https://www.data4impactproject.org/resources/webinars/use-of-routine-data-for-economic-evaluations/

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Use of Routine Data for Economic Evaluations

  1. 1. Use of Routine Data for Economic Evaluations Anna Krivelyova, Data for Impact Webinar October 29, 2020
  2. 2. Webinar goals • Why measure costs? • Types of economic evaluations • Design considerations • Sources of routine data/data quality • Factors affecting feasibility Theory Practice
  3. 3. Why invest in cost measurement? • Scaling and sustaining programs and interventions • Improving efficiency • Improving value for money Cost measurement is a process of collecting, processing, analyzing, and reporting on the costs of interventions.
  4. 4. Types of economic evaluations Cost analysis/“costing” effects: Not measured Need to measure: Costs Cost-effectiveness analysis effects: Natural units Cost-utility analysis effects: QALYs/DALYs Cost-benefit analysis effects: Monetary Need to measure: Costs and effects QALY: Quality Adjusted Life Year DALY: Disability Adjusted Life Year
  5. 5. Types of economic evaluations (cont.) • Cost-Utility and Cost-Benefit Analyses normally require longer-term follow-up period, modelling, and collection and analysis of secondary population-level epidemiological, demographic, and financial data • Level of detail and design for cost analysis depends on the type of economic evaluation
  6. 6. Cost analysis: Research questions • What is the total cost of the program/ intervention? • What is the cost of the program per client? • How do program costs differ for different client groups? • What are the major cost drivers? • How would the program cost per client change if we scale-up? • What is the allocation of costs across various program activities? Use of routine data only
  7. 7. Design considerations • Perspective • Population and focus • Unit of observation • Included and excluded costs • Time period • Retrospective/prospective (retrospective easier) • Routine data only or routine data plus some primary data collection
  8. 8. Analysis perspective Perspective Examples of costs Use of routine data only • Personnel • Supplies • Equipment Provider/program • User fees • Travel • Time Client/patient • Lost production/taxesSocietal
  9. 9. Challenges with measuring costs • Routine data exists but often in many different places and formats • Resources are used by one entity and paid for by another • Requires detailed understanding of implementation • Benchmarks rarely exist — very contextualized
  10. 10. HIV TX program: Cost centers Above site: central/subnational/technical assistance
  11. 11. HIV TX program: Examples of resources Other resources: Facility level: Utilities and contracted services Above site: Monitoring and evaluation (M&E) software costs, supervisory/ training visits Laboratory: Sample transport, testing Supply chain: Warehouse and transport costs for drugs and supplies Community level: Community health workers (CHWs) for retention and adherence
  12. 12. HIV TX program: Who Is paying? • Infrastructure: MOH (buildings), PEPFAR (improvements) • Utilities: MOH district office • Personnel: MOH • Equipment: MOH, PEPFAR • Supplies: MOH • Technical assistance/training: PEPFAR implementing partner • Drugs: Antiretrovirals (ARVs) (Global Fund), other drugs (MOH), supply chain (PEPFAR) • Laboratory: Reagents (PEPFAR), everything else (MOH) • Community-level services: Nongovernmental organizations (NGOs)
  13. 13. Data components needed Total cost = Quantity x Unit Cost 1) Quantities 2) Unit costs 3) Allocations Sources of routine data will often differ depending on the component. For each component there may be different sources of routine data.
  14. 14. Drugs: Sources of routine data Type Source Where Quantity Electronic health record (EHR)/ patient service record Facility Quantity Pharmacy dispensing Facility Quantity Stock registers/cards Facility Quantity/price Invoices Facility Quantity/price Orders/delivery logs Warehouse Price Country-level procurement price lists Central level/MOH Price Global/funder procurement price lists Global databases
  15. 15. Drugs: Examples
  16. 16. Personnel: Sources of routine data Type Source Where Quantity Personnel records/staff lists Facility Price Specific staff salaries/benefits Facility Price Generalized compensation schedules Central/MOH Price Top-up/performance-based payments Implementing partner
  17. 17. Personnel: Examples
  18. 18. Supplies, equipment, utilities, contracted services: Sources of routine data Type Source Where Quantity Stock registers/cards Facility Quantity/price Invoices Facility/district Quantity/price Orders/delivery logs Warehouse/IPs Price Country level procurement price lists Central level/ MOH Price IP accounting records/grant reporting IP Price Market prices Country level/ global
  19. 19. Supplies, equipment, utilities, contracted services: Examples
  20. 20. Merging prices and quantities • Item description: Abacavir 300mg/Abacavir 600mg Red top tube/grey top tube • Unit definition Box of 100/box of 1,000 Monthly bill/annual bill
  21. 21. Allocations Use of routine data only Stand-alone HIV TX clinic Stand-alone HIV TX and testing clinic HIV clinic within a larger facility PMTCT services: Multiple entry points within a facility Large hospital: HIV TX is completely integrated with primary care Every client/patient receives exact same services Few well-defined patient groups Many well-defined patient groups Many patient groups, not well defined Every patient receives completely different services
  22. 22. Allocations: Some options • Allocating based on some measure (e.g., number of patients, visits, tests, space, distance) • Normative assumptions (e.g., using TX guidelines) • Adding limited primary data collection (e.g., personnel questionnaire) • Excluding certain costs from the analysis
  23. 23. Key lessons • Understanding of implementation/service model • Complexity and heterogeneity of the service delivery • Understanding of resource types, cost centers, and payers • Hypothesis on major cost drivers (e.g., don’t spend a month counting cotton balls)
  24. 24. Key lessons (cont.) • Routine data in “analyzable” format • Reasonable allocation is feasible • Prospective data collection maybe easier (can we add a simple field to the existing data system?) • Routine data may need to be supplemented
  25. 25. Questions? Thank You!
  26. 26. 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