title: The impact of an emergency hiring plan on the shortage and distribution of nurses in Kenya: the importance of information systems.
abstract: OBJECTIVE
  To analyse the effect of Kenya's Emergency Hiring Plan for nurses on their inequitable distribution in rural and underserved areas.
  
  
  METHODS
  We used data from the Kenya Health Workforce Informatics System on the nursing workforce to determine the effect of the Emergency Hiring Plan on nurse shortages and maldistribution. The total number of nurses, the number of nurses per 100,000 population and the opening of previously closed or new heath facilities were recorded.
  
  
  RESULTS
  Of the 18,181 nurses employed in Kenya's public sector in 2009, 1836 (10%) had been recruited since 2005 through the Emergency Hiring Plan. Nursing staff increased by 7% in hospitals, 13% in health centres and 15% in dispensaries. North Eastern province, which includes some of the most remote areas, benefited most: the number of nurses per 100,000 population increased by 37%. The next greatest increase was in Nyanza province, which has the highest prevalence of HIV infection in Kenya. Emergency Hiring Plan nurses enabled the number of functioning public health facilities to increase by 29%. By February 2010, 94% of the nurses hired under pre-recruitment absorption agreements had entered the civil service.
  
  
  CONCLUSIONS
  The Emergency Hiring Plan for nurses significantly increased health services in Kenya's rural and underserved areas over the short term. Preliminary indicators of sustainability are promising, as most nurses hired are now civil servants. However, continued monitoring will be necessary over the long term to evaluate future nurse retention. The accurate workforce data provided by the Kenya Health Workforce Informatics System were essential for evaluating the effect of the Emergency Hiring Plan.
authors.affiliation: Emory University School of Nursing, 325 Swanton Way, Decatur, GA 30030, United States of America.
authors.forename: J M
authors.initials: JM
authors.lastname: Gross
authors.forename: P L
authors.initials: PL
authors.lastname: Riley
authors.forename: R
authors.initials: R
authors.lastname: Kiriinya
authors.forename: C
authors.initials: C
authors.lastname: Rakuom
authors.forename: R
authors.initials: R
authors.lastname: Willy
authors.forename: A
authors.initials: A
authors.lastname: Kamenju
authors.forename: E
authors.initials: E
authors.lastname: Oywer
authors.forename: D
authors.initials: D
authors.lastname: Wambua
authors.forename: A
authors.initials: A
authors.lastname: Waudo
authors.forename: M F
authors.initials: MF
authors.lastname: Rogers
mesh_terms: D000328:Adult; D003906:Developing Countries; D005843:Geography; D006291:Health Policy; D006301:Health Services Needs and Demand / Q000639:trends; D054624:Health Status Disparities; D006801:Humans; D007256:Information Systems / Q000458:organization & administration*; D007630:Kenya; D008875:Middle Aged; D009726:Nurses / Q000600:supply & distribution*; D009729:Nursing; D010560:Personnel Selection / Q000379:methods / Q000706:statistics & numerical data; D011203:Poverty; D000078329:Workforce; D055815:Young Adult