title: The optimal number of routine vaccines to order at health clinics in low or middle income countries.
abstract: In a low or middle income country, determining the correct number of routine vaccines to order at a health clinic can be difficult, especially given the variability in the number of patients arriving, minimal vaccination days and resource (e.g., information technology and refrigerator space) constraints. We developed a spreadsheet model to determine the potential impact of different ordering policies, basing orders on the arrival rates seen in the previous 1, 3, 6, or 12 sessions, or on long-term historical averages (where these might be available) along with various buffer stock levels (range: 5-50%). Experiments varied patient arrival rates (mean range: 1-30 per session), arrival rate distributions (Poisson, Normal, and Uniform) and vaccine vial sizes (range: 1-dose to 10-dose vials). It was found that when the number of doses per vial is small and the expected number of patients is low, the ordering policy has a more significant impact on the ability to meet demand. Using data from more prior sessions to determine arrival rates generally equates to a better ability to meet demand, although the marginal benefit is relatively small after more than 6 sessions are averaged. As expected, the addition of more buffer is helpful in obtaining better performance; however, this advantage also has notable diminishing returns. In general, the long-term demand rate, the vial sizes of the vaccines used and the method of determining the patient arrival rate all have an effect on the ability of a clinic to maximize the demand that is met.
authors.affiliation: Department of Industrial Engineering, School of Engineering University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Jayant
authors.initials: J
authors.lastname: Rajgopal
authors.forename: Diana L
authors.initials: DL
authors.lastname: Connor
authors.forename: Tina-Marie
authors.initials: TM
authors.lastname: Assi
authors.forename: Bryan A
authors.initials: BA
authors.lastname: Norman
authors.forename: Sheng-I
authors.initials: SI
authors.lastname: Chen
authors.forename: Rachel R
authors.initials: RR
authors.lastname: Bailey
authors.forename: Adrienne R
authors.initials: AR
authors.lastname: Long
authors.forename: Angela R
authors.initials: AR
authors.lastname: Wateska
authors.forename: Kristina M
authors.initials: KM
authors.lastname: Bacon
authors.forename: Shawn T
authors.initials: ST
authors.lastname: Brown
authors.forename: Donald S
authors.initials: DS
authors.lastname: Burke
authors.forename: Bruce Y
authors.initials: BY
authors.lastname: Lee
mesh_terms: D003906:Developing Countries; D006801:Humans; D015233:Models, Statistical; D014611:Vaccination / Q000191:economics* / Q000706:statistics & numerical data*; D014612:Vaccines / Q000191:economics* / Q000600:supply & distribution*
chemical_list: D014612:Vaccines