title: Using quality improvement to accelerate highly active antiretroviral treatment coverage in South Africa.
abstract: BACKGROUND
  The authors report on a health systems strengthening intervention using quality improvement (QI) methods at the subdistrict level to accelerate highly active antiretroviral treatment (HAART) initiation in South Africa.
  
  
  METHODS
  Using a phased scale-up design between August 2006 and November 2009, 14 primary healthcare clinics, one community health centre, one district hospital and one tertiary hospital in a subdistrict were recruited into a 'learning network' using QI methods to facilitate cross-facility learning/mentorship/support. Clinic teams consisting of nurses, counsellors, clerks and/or doctors set collective and individual performance targets, analysed their care systems using 'real-time' data feedback, and designed/implemented a set of simple changes to improve HIV testing and HAART initiation rates across the region.
  
  
  METHODS
  Primary clinic data were used to measure HAART initiation rates (primary outcome) and HIV testing (secondary outcome). We analysed data variation/trends using an interrupted time series design. Logistic regression analysis was applied to examine trends in HAART initiation during the intervention phases.
  
  
  RESULTS
  Clinics in the learning network increased HIV testing by 301.8% from 891/month (SD=94.2) to 3580/month (SD=327.7) (p<0.0001). Monthly HAART initiations increased by 185.5% from 179/month (SD=17.22) to 511/month (SD=44.93) (p<0.0001). During the pilot (phase I), the monthly rate of HAART initiations increased by 3.6 patients. In the prototype collaborative (phase II), there was no acceleration in the rate of increase (3.3/month, p=0.92). Significant acceleration was observed in the rate of increase during the QI scale up (phase III) (10.1/month, p<0.001). The proportion of estimated need for HAART met in the region increased from 35.8% to 72.4% at a time of rapid population growth.
  
  
  CONCLUSIONS
  A QI approach, using learning networks to teach simple data-driven methods for addressing system failures, with increased training and resource inputs, can assist districts to quickly reach universal coverage targets.
authors.affiliation: Institute for Healthcare Improvement, 20 University Road, 7th Floor, Cambridge, MA 02138, USA. pwebster@ihi.org
authors.forename: Patty D
authors.initials: PD
authors.lastname: Webster
authors.forename: Maria
authors.initials: M
authors.lastname: Sibanyoni
authors.forename: Dinah
authors.initials: D
authors.lastname: Malekutu
authors.forename: Kedar S
authors.initials: KS
authors.lastname: Mate
authors.forename: W D Francois
authors.initials: WD
authors.lastname: Venter
authors.forename: Pierre M
authors.initials: PM
authors.lastname: Barker
authors.forename: Winnie
authors.initials: W
authors.lastname: Moleko
mesh_terms: D000046:Academic Medical Centers; D023241:Antiretroviral Therapy, Highly Active; D003151:Community Health Centers; D015658:HIV Infections / Q000175:diagnosis / Q000188:drug therapy*; D006767:Hospitals, District; D006801:Humans; D016015:Logistic Models; D008611:Mentors; D015588:Observer Variation; D058996:Quality Improvement / Q000706:statistics & numerical data*; D013019:South Africa; D013192:Staff Development; D013996:Time and Motion Studies