title: Age Targeting of Voluntary Medical Male Circumcision Programs Using the Decision Makers' Program Planning Toolkit (DMPPT) 2.0.
abstract: BACKGROUND
  Despite considerable efforts to scale up voluntary medical male circumcision (VMMC) for HIV prevention in priority countries over the last five years, implementation has faced important challenges. Seeking to enhance the effect of VMMC programs for greatest and most immediate impact, the U. S. President's Plan for AIDS Relief (PEPFAR) supported the development and application of a model to inform national planning in five countries from 2013-2014.
  
  
  RESULTS
  The Decision Makers' Program Planning Toolkit (DMPPT) 2.0 is a simple compartmental model designed to analyze the effects of client age and geography on program impact and cost. The DMPPT 2.0 model was applied in Malawi, South Africa, Swaziland, Tanzania, and Uganda to assess the impact and cost of scaling up age-targeted VMMC coverage. The lowest number of VMMCs per HIV infection averted would be produced by circumcising males ages 20-34 in Malawi, South Africa, Tanzania, and Uganda and males ages 15-34 in Swaziland. The most immediate impact on HIV incidence would be generated by circumcising males ages 20-34 in Malawi, South Africa, Tanzania, and Uganda and males ages 20-29 in Swaziland. The greatest reductions in HIV incidence over a 15-year period would be achieved by strategies focused on males ages 10-19 in Uganda, 15-24 in Malawi and South Africa, 10-24 in Tanzania, and 15-29 in Swaziland. In all countries, the lowest cost per HIV infection averted would be achieved by circumcising males ages 15-34, although in Uganda this cost is the same as that attained by circumcising 15- to 49-year-olds.
  
  
  CONCLUSIONS
  The efficiency, immediacy of impact, magnitude of impact, and cost-effectiveness of VMMC scale-up are not uniform; there is important variation by age group of the males circumcised and countries should plan accordingly.
authors.affiliation: Health Policy Project, Avenir Health, Washington, DC, United States of America.
authors.forename: Katharine
authors.initials: K
authors.lastname: Kripke
authors.affiliation: United Nations Joint Programme on HIV/AIDS (UNAIDS), Geneva, Switzerland.
authors.forename: Marjorie
authors.initials: M
authors.lastname: Opuni
authors.affiliation: Health Policy Project, Futures Group, Washington, DC, United States of America.
authors.forename: Melissa
authors.initials: M
authors.lastname: Schnure
authors.affiliation: Bill & Melinda Gates Foundation, Seattle, WA, United States of America.|Department of Global Health, University of Washington, Seattle, WA, United States of America.
authors.forename: Sema
authors.initials: S
authors.lastname: Sgaier
authors.affiliation: Office of the U.S. Global AIDS Coordinator and Health Diplomacy, Washington, DC, United States of America.
authors.forename: Delivette
authors.initials: D
authors.lastname: Castor
authors.affiliation: Office of the U.S. Global AIDS Coordinator and Health Diplomacy, Washington, DC, United States of America.
authors.forename: Jason
authors.initials: J
authors.lastname: Reed
authors.affiliation: USAID, Washington, DC, United States of America.
authors.forename: Emmanuel
authors.identifier: http://orcid.org/0000-0001-5370-4827
authors.initials: E
authors.lastname: Njeuhmeli
authors.affiliation: Health Policy Project, Avenir Health, Glastonbury, CT, United States of America.
authors.forename: John
authors.initials: J
authors.lastname: Stover
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D002648:Child; D002944:Circumcision, Male / Q000191:economics / Q000706:statistics & numerical data; D003362:Cost-Benefit Analysis; D003657:Decision Making; D013541:Eswatini / Q000453:epidemiology; D015658:HIV Infections / Q000453:epidemiology / Q000517:prevention & control*; D006291:Health Policy; D006801:Humans; D015994:Incidence; D008295:Malawi / Q000453:epidemiology; D008297:Male; D015233:Models, Statistical; D009313:National Health Programs / Q000191:economics / Q000706:statistics & numerical data; D012984:Software; D013019:South Africa / Q000453:epidemiology; D013636:Tanzania / Q000453:epidemiology; D014454:Uganda / Q000453:epidemiology; D035141:Voluntary Programs / Q000191:economics / Q000706:statistics & numerical data; D055815:Young Adult