Anjuli D Wagner, Sarah Gimbel1, Kristjana H Ásbjörnsdóttir2, Peter Cherutich3, Joana Coutinho4, Jonny Crocker, Emilia Cruz4, Fatima Cuembelo5, Vasco Cumbe6,7, McKenna Eastment8, Jennifer Einberg9, Florencia Floriano4, Douglas Gaitho10, Brandon L Guthrie2, Grace John-Stewart2,8,11, Alex H Kral12, Barrot H Lambdin12,13, Shan Liu14, Martin Maina10, Nelia Manaca4, Mika Matsuzaki15, Loris Mattox16, Nancy Mburu10, R Scott McClelland2,8, Mark A Micek17, Ana Olga Mocumbi18, Alberto Muanido4, Ruth Nduati10,19, Irene N Njuguna2,20, Geoffrey Oluoch10, Laura B Oyiengo3, Keshet Ronen, Caroline Soi, Bradley H Wagenaar21, George Wanje22, Lynn D Wenger12, Kenneth Sherr2,14,21. 1. Child, Family, and Population Health Nursing, University of Washington, Seattle, WA. 2. Department of Epidemiology, University of Washington, Seattle, WA. 3. Ministry of Health, Nairobi, Kenya. 4. Health Alliance International, Beira, Mozambique. 5. Community Health Department, Eduardo Mondlane University, Maputo, Mozambique. 6. Department of Mental Health, Sofala Provincial Health Directorate, Ministry of Health, Beira, Mozambique. 7. Psychiatry Department, Paulista School of Medicine, Sao Paulo Federal University, UNIFESP. 8. Department of Medicine, University of Washington, Seattle, WA. 9. Iota Ink, Seattle, WA. 10. Network of AIDS Researchers of East and Southern Africa, Nairobi, Kenya. 11. Department of Pediatrics, University of Washington, Seattle, WA. 12. Community Health and Implementation Research Program, RTI International, San Francisco, CA. 13. Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA. 14. Department of Industrial and Systems Engineering, University of Washington, Seattle, WA. 15. Harvard T.H. Chan School of Public Health, Boston, MA. 16. HIV Education and Prevention Project of Alameda County, Oakland CA. 17. Department of Medicine, University of Wisconsin School of Medicine and Public Health. 18. Faculty of Medicine, Universidade Eduardo Mondlane, Division of Non Communicable Diseases, Instituto Nacional de Saúde, Maputo, Mozambique. 19. Department of Pediatrics, University of Nairobi, Nairobi, Kenya. 20. Research and Programs, Kenyatta National Hospital, Nairobi, Kenya. 21. Health Alliance International, Seattle, WA. 22. Department of Medical Microbiology, University of Nairobi, Nairobi, Kenya.
Abstract
BACKGROUND: Cascades have been used to characterize sequential steps within a complex health system and are used in diverse disease areas and across prevention, testing, and treatment. Routine data have great potential to inform prioritization within a system, but are often inaccessible to frontline health care workers (HCWs) who may have the greatest opportunity to innovate health system improvement. METHODS: The cascade analysis tool (CAT) is an Excel-based, simple simulation model with an optimization function. It identifies the step within a cascade that could most improve the system. The original CAT was developed for HIV treatment and the prevention of mother-to-child transmission of HIV. RESULTS: CAT has been adapted 7 times: to a mobile application for prevention of mother-to-child transmission; for hypertension screening and management and for mental health outpatient services in Mozambique; for pediatric and adolescent HIV testing and treatment, HIV testing in family planning, and cervical cancer screening and treatment in Kenya; and for naloxone distribution and opioid overdose reversal in the United States. The main domains of adaptation have been technical-estimating denominators and structuring steps to be binary sequential steps-as well as logistical-identifying acceptable approaches for data abstraction and aggregation, and not overburdening HCW. DISCUSSION: CAT allows for prompt feedback to HCWs, increases HCW autonomy, and allows managers to allocate resources and time in an equitable manner. CAT is an effective, feasible, and acceptable implementation strategy to prioritize areas most requiring improvement within complex health systems, although adaptations are being currently evaluated.
BACKGROUND: Cascades have been used to characterize sequential steps within a complex health system and are used in diverse disease areas and across prevention, testing, and treatment. Routine data have great potential to inform prioritization within a system, but are often inaccessible to frontline health care workers (HCWs) who may have the greatest opportunity to innovate health system improvement. METHODS: The cascade analysis tool (CAT) is an Excel-based, simple simulation model with an optimization function. It identifies the step within a cascade that could most improve the system. The original CAT was developed for HIV treatment and the prevention of mother-to-child transmission of HIV. RESULTS: CAT has been adapted 7 times: to a mobile application for prevention of mother-to-child transmission; for hypertension screening and management and for mental health outpatient services in Mozambique; for pediatric and adolescent HIV testing and treatment, HIV testing in family planning, and cervical cancer screening and treatment in Kenya; and for naloxone distribution and opioid overdose reversal in the United States. The main domains of adaptation have been technical-estimating denominators and structuring steps to be binary sequential steps-as well as logistical-identifying acceptable approaches for data abstraction and aggregation, and not overburdening HCW. DISCUSSION: CAT allows for prompt feedback to HCWs, increases HCW autonomy, and allows managers to allocate resources and time in an equitable manner. CAT is an effective, feasible, and acceptable implementation strategy to prioritize areas most requiring improvement within complex health systems, although adaptations are being currently evaluated.
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