JUST-NOT-IN-TIME VACCINES: AN INITIAL EVALUATION OF THE CDC S VACCINE MANAGEMENT BUSINESS IMPROVEMENT PROJECT (VMBIP)

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1 JUST-NOT-IN-TIME VACCINES: AN INITIAL EVALUATION OF THE CDC S VACCINE MANAGEMENT BUSINESS IMPROVEMENT PROJECT (VMBIP) JASON SHAFRIN University of California, San Diego Department of Economics 9500 Gilman Drive, #0534 La Jolla, CA Phone: (858) Fax: (858) jshafrin@ucsd.edu JOHN FONTANESI, PHD Director, Center for Management Science in Health University of California, San Diego Department of Pediatrics & Department of Family and Preventive Medicine 9500 Gilman Drive #0821, La Jolla, CA, Cell Phone: (858) Fax: (619) jfontanesi@ucsd.edu

2 ABSTRACT Objective: To estimate the impact of the CDC s Vaccine Management Business Improvement Project (VMBIP) on vaccine availability. Design: Before-and-after longitudinal study. Setting: Vaccine ordering, usage, inventory, and delivery data was collected from two large southern California providers. Main Exposure: In March 2007, California providers participating in the Vaccines for Children (VFC) program were compelled to begin placing vaccine orders under the new VMBIP logistical system. Main Outcome Measures: The change in the average vaccine delivery delay before and after VBMIP was the main outcome measure. Using a simulation methodology, three other metrics were also estimated: 1) the probability a provider will run out of vaccines before the new shipment arrives, 2) the number of days per year a provider will have zero vaccines in inventory, and 3) change in the up-to-date status of 12 month old children. Results: After VMBIP was implemented, delivery times increase from 1.6 to 12 business days (p<0.001). For 7 of the 11 vaccines investigated, there is a strong, statistically significant increase (p<0.01) in the number of days a provider will be left without any vaccine. The end result of the simulation was a decrease in the up-to-date status for 12 month old children. Conclusion: VMBIP was implemented in order to save costs by centralizing vaccine ordering and delivery logistics. This study finds that during VMBIP s initial implementation timeline, longer delivery times may jeopardize vaccine availability for the nation s youth.

3 A cornerstone of the American Health System is the routine prevention of infectious disease through a network of national, state and local immunization programs 1 which are collectively estimated to create a net societal savings of up to $43 billion. 2 Central to this system of immunization provision is the Vaccines for Children (VFC) Program which provides free vaccines for eligible children. Enacted in 1993 under Title XIX of the Social Security Act 3 and placed under the administrative control of the Centers for Disease Control and Prevention (CDC), the VFC program was allocated $2.1 billion in fiscal year While the legislation tightly constrains administrative spending under the entitlement to the direct cost of vaccine inventory and distribution it is silent on how the overhead costs for administering the program are to be covered. This issue is addressed under the Office of Management and Budget s Government Performance Results Act (GPRA) of As part of GPRA reporting, the CDC has targeted decreased overhead costs for the VFC program by implementing electronic order entry and creating the Vaccine Management Business Improvement Project (VMBIP), a centralized distribution center. The CDC believed that nationwide centralized vaccine distribution was crucial in addressing the escalating costs associated with individual state/city grantees individual vaccine distribution mechanisms and the need to reduce potential vaccine wasted. 5 Prior to VMBIP (implementation of nationwide centralized vaccine distribution), VFC providers would place their orders through their state/city Department of Health. The vaccines would then be distributed directly to the provider from a local vaccine depot, or via a contracted distributor. Health care organizations with multiple sites could have vaccine

4 distributed to a central location and redistribute vaccine internally on an as needed basis. Providers who unexpectedly ran out of VFC-provided vaccines could petition the local vaccine depot and receive emergency supplies in very short order. Under VMBIP, however, nationwide vaccine distribution is now contracted to a thirdparty, McKesson Specialty. Vaccines are shipped directly from McKesson Specialty to providers from one of two distribution centers. Redistribution of VFC-provided vaccines within a health care organization has been disallowed and each clinical site is responsible for ordering and receiving vaccine for their site solely. Providers now order vaccine on a tiered order frequency schedule known as Economic Order Quantity (EOQ). A provider s reported vaccine usage and the eligible patient population they serve determine how often and in what amounts a provider may order vaccine. What has not changed is the admonition; Order vaccine in accordance with actual vaccine need; avoid stockpiling or build-up of excess vaccine inventory 6 which generally means ordering with one month s lead time. This paper evaluates the centralized distribution system s ability to supply vaccines to providers in a timely manner. Specifically, VMBIP s distribution efficiency is measured by changes in: 1) vaccine delivery times, 2) the probability a provider will run out of vaccines before a new order arrives, 3) the number of days per year a provider will run out of any given vaccine, and 4) the up-to-date status of 12-month-old children specifically due to out-of-stock vaccine inventory.

5 METHODS Data Source The data for this study were collected from a convenience sample of the vaccine ordering behavior of two large southern California providers in San Diego and Orange Counties. Variables which were collected include quarterly vaccine usage, vaccine ordering dates and quantities, the dates the vaccine shipments were received and the vaccine inventory levels at the times the orders were placed. This data were collected for 11 vaccines, 1 between January 2005 and June Although VMBIP completed its nationwide rollout in June 2008, California was one of four pilot states that began rollout in February California s actual go-live date was in March Because of the early enactment date, we have 27 months of data prior to the VMBIP start date and 15 months of data after VMBIP was implemented. Statistical Analysis Analysis began with determining the average duration between the date when a vaccine order was placed and when it was received under the old and new distribution systems. The standard t-test was used to test for equality of means between the two samples. To further understand the effect of changing delivery times, a simulation methodology is employed using the additional vaccine data collected. The first two statistics calculated 1 The 11 vaccines were: Diphtheria, tetanus, and pertussis (DTaP); DTap/Hep B/IPV; Haemophilus influenzae (Hib); Hepatitius A; Hepatitius B; Measles, Mumps and Rubella (MMR); Meningococcal Vaccines (MCV); Pneumococcal conjugate vaccine (PCV); Polio (IPV); Tetanus and diphtheria (Td); Tetanus, Diphtheria and Pertussis (Tdap). Data for Varicella vaccines was collected but not included due to missing data.

6 with the simulation are 1) the probability a provider will run out of vaccines before a new order arrives and 2) the number of days per year a provider will have zero vaccine inventory on hand. A provider will run out of vaccines if number of vaccines used between the vaccine order and delivery dates is higher than the vaccine inventory levels at the time the order is placed. The inventory levels at the time of each order is known from the data collected, however, vaccine usage data was only available on a quarterly basis. In order to estimate daily vaccine usage, Matlab was utilized. The simulation assumed daily vaccine usage was distributed according to a negative binomial function 2 and the parameters of this distribution were set so that the simulated data s expected mean quarterly usage equaled that of the actual data collected. 3 Using the existing ordering schedule, one can estimate both the probability a provider would run out of vaccine and the number of days the provider would be left without vaccine assuming typical order delay times under both the new and old distribution system. After the number of days a provider will be without a vaccine is estimated, one can also estimate how a facility s cohort of children s up-to-date vaccination status is affected. If the provider is out of stock of a given vaccine, then it is assume that the child is not vaccinated. If the child was not vaccinated, it is assumed that they may be vaccinated at their next well-child visit. It is also assumed that children visit the doctor for well-child visit according to the CDC s standard well-child visit schedule; 9 that parents are unlikely 2 The only exception is that for the Dtap/Hep B/ IPV, we assumed a normal distribution since it fit the data better. 3 The simulated daily data was also calibrated so that the expected simulated quarterly standard deviation matched that of the actual data.

7 to bring the child to the provider for vaccinations before the next scheduled visit; and that children do not miss any well-child visits. 4 By standardizing patient-provider visit rates, one can isolate what portion of decrease up-to-date status is specifically due to changes in the probability that a provider has zero vaccine inventory. RESULTS Average Delay Time Figure 1 plots the vaccine delivery duration over time. The vertical black line demarcates the date VMBIP was implemented in California. Delivery times, under VMBIP are significantly longer than was previously the case. In 2008, this effect seems to have attenuated somewhat, but delay levels at the end of the sample are still far above those at the beginning of the sample. Table 1 displays summary statistics for vaccine delivery times. After VMBIP was implemented in California, the average vaccine delivery time increased from 1.6 to 12 days. The probability that a vaccine delivery arrived more than one week after an order was placed jumped from 7% to 89%. A t-test for equality of means is strongly rejected (p<0.001). Thus, the evidence shows that during the initial phases of implementation, VMBIP did increase vaccine delivery times. 4 This is a reasonable assumption since in a child s first year, they are supposed to visit a physician at 1, 2, 4, 6 and 12 months.

8 Simulation Results Clinic data clearly demonstrates that delay times increased significantly under VMBIP. The consequence of these can be seen in the results of the simulation for the probability a provider will run out of vaccines. Figure 2 displays the simulation results for the MMR and Hib vaccines. The graph shows the simulated probability that the provider will run out of vaccine before the new shipment arrives for any inventory level at the time of ordering. For both MMR and Hib as well as for most all of the vaccines under consideration the longer vaccine delivery time resulted in a significantly increased risk of running out of vaccines before the order arrives. 5 For instance, if a clinic had 100 doses of MMR vaccine and 100 doses of Hib vaccine in inventory when placing a new order, under the old system there was less than a 1% chance the clinic would run out of MMR and a 7% chance they would run out of Hib. Under VMBIP, these probabilities increase to 41% and 76% respectively. Figure 2 shows that for most vaccine inventory levels, VMBIP s longer delays significantly increase the probability of running out of vaccines before a new shipment arrives. A more important question, however, is how vaccine delivery times will affect vaccine availability throughout the year. Table 2 shows the results of the second simulation. There is a very large increase in the number of days a provider would be without vaccine in the case of Hep A, Hib, PCV, and Td. In each of these cases, provider would be without the vaccine for more than two months out of the year. Vaccine availability is much higher under the old system and these results are statistically significant at the 1% 5 Results for the other nine vaccines are qualitatively similar and thus, not displayed in the figure for brevity.

9 level. For Dtap/Hep B/IPV, IPV, MMR, the decrease in vaccine availability under VMBIP is also statistically significant at conventional levels. On the other hand, for Dtap, Hep B, MCV, and Tdap, VMBIP s longer delays seem to have little impact on vaccine availability. The effect of longer delivery times on up-to-date status of children on their first birthdays is shown in Table 3. The simulation results show dramatic decreases in the number of children who are up-to-date on Dtap/Hep B/IPV, Hib, Hep A, and PCV vaccines, and a fairly large decrease in the number of children who are up-to-date on MMR. On the other hand, for the Dtap, Hep B, IPV, there is a minimal decrease in the number of children who are up-to-date. A larger effect on up-to-date status was found for vaccines with higher daily usage rates and for vaccines with more doses required in each series. COMMENT This analysis clearly indicates that at least during the period of operation included in this study VMBIP increased delays in vaccine delivery resulting in an increased number of days where vaccines are unavailable for patients. The effect is particularly pronounced when vaccine usage is high or when inventory levels are low at the time the order is placed. Coincident with and compounding the increase in the mean vaccine delivery times was a dramatic increase in the variability for expected arrival time for vaccine delivery. As shown in Table 1, the standard deviation of the delivery delay increases from 2.4 days

10 before VMBIP to 6.3 days after VMBIP was implemented. This implies that under VMBIP, a very long delay was more likely, but it also implies that an unexpectedly quick delivery relative to a larger mean was also more likely. Anecdotal evidence collected from discussions with various public health officials provided numerous examples of providers who ordered vaccines expecting the vaccines to arrive in the standard 2-3 weeks but the vaccines arrived early and the provider did not have space available in their refrigerator. Some of these vaccines had to be discarded. Providers who elect to participate in VFC do so with the understanding they must abide by VFC program requirements that include when and how to order. The federal cost saving created from a centralized distribution may not be worth the cost of decreased ordering flexibility and increased number of children who miss a necessary vaccination because of inventory shortage. Besides the impact on routine childhood vaccinations, the data also brings into question the centralized distributions present capacity to meet unexpected demand under either pandemic or bio-terrorists scenarios. Even though the idea of centralized distribution is relatively new to the Vaccines for Children program, benchmarks and standards for warehouse and distribution centers are not. And there is much that can be done to achieve the original goals and objectives of the program. Distribution centers for biological products typically have an order fill time of 24 hours with an additional dock to stock time of 8-24 hours with no more than 0.5% pick/pack error rate. 10,11 This study s data suggests that the present centralized vaccine distribution system was not meeting industry benchmark.

11 There are several steps commonly taken by warehouse and distribution centers to increase efficiency and accuracy. The first is to review existing order and fill policy to eliminate unnecessary steps and restrictive requirements. One example of potential redundancy is the chain of order fulfillment. The just-in-time, centralized VMBIP distribution system is predicated on receiving timely inventory information from providers. However, information collection is far from centralized. Physicians do not update McKesson Specialty with respect to their inventory stocks frequently. Thus the centralized system lacks important information by which they can plan the needs of providers for the upcoming months. An example of a potentially restrictive policy is the emphasis on only ordering a certain amount of vaccine supply at a time and essentially for the same amount as the previous year. Though one of the rationales for the centralized distribution system was to prevent waste that results from medical offices suboptimal handing of vaccine storage, 4 it might be better to help provider offices improve handling and storage competencies rather then restrict ordering. Finally there is the simple matter of infrastructure: whether the central warehouse has enough space and enough properly trained personnel to achieve programmatic goals. Though it is quite possible to spend several hundreds of thousands of dollars on consultants, a novel approach presently used by many industry leaders is a self-assessment tool available online from the Georgia Institute of Technology, School of Industrial & Systems Engineering. 12 This online product uses Data Envelopment Analysis to determine how a user compares to their industry segment leaders in a way that highlights potential areas of improvement.

12 While the conclusions of this study seem robust according to the data collected, the study does have its limits. Data was collected from a convenience sample of southern California providers and thus may not be nationally representative. Further, the data for the post-vmbip period were collected during the start-up phase of VMBIP and prior to the initiation of the VTREKS ordering system. It is possible that VMBIP s long delays are caused by a poor initial implementation rather than long-term issues with the centralized distribution system. Finally, the change in the up-to-date status of 12-montholds only examines the effect of reduced vaccine availability in the short run, not over the life of the child. Regardless of start-up problems, the goals of providing safer vaccine more quickly to poor children at less cost to the tax-payer is one that continues to need to be pursued. It is hoped this study will be used to achieve those goals.

13 REFERNCES 1 Centers for Disease Control and Prevention. Ten great public health achievements United States, MMWR Morb Mortal Wkly Rep. 1999;48: Zhou F, Santoli J, Messonnier ML, Yusuf HR, Shefer A, Chu SY, Rodewald L, Harpaz R. Economic Evaluation of the 7-Vaccine Routine Childhood Immunization Schedule in the United States, Arch Pediatr Adolesc Med. 2005;159(12): U.S.C. 1396s. Program for distribution of pediatric vaccines (parallel cite Section 1928 of the Social Security Act. 4 Centers for Disease Control and Prevention. FY 2007 CDC/ATSDR President s Budget Request. Financial Management Office. February 10, Accessed August 1, 2008 at 5 Bell KN, Hogue CJR, Manning C, Kendal AP. Risk Factors for Improper Vaccine Storage and Handling in Private Provider Offices. Pediatrics. 2001;107(6):e Centers for Disease Control and Prevention. Vaccines for Children Operations Guide. August, 2007;M-6:10.

14 7 California Department of Health Services Immunization Branch, Internal Memo. Program in Brief: Vaccine Management Business Improvement Project. February, California Department of Health Services Immunization Branch, Internal Memo. VFC Prepares to Transition to National Vaccine Distribution System. March 15, %20Ditribution%20FAX.doc 9 Centers for Disease Control and Prevention. Recommended Immunization Schedule for Persons Aged 0-6 Years--United States, bw_pr.pdf 10 Frazelle EH, Hackman ST. The Warehouse Performance Index: A single Point Metric for Benchmarking Warehouse Performance. Material Handling Research Center Technical Report. TR-93-14, Georgia Institute of Technology, Atlanta, GA. 11 Hackman ST, Frazelle EH, Griffin PM, Griffin SO, Vlatsa DA. Benchmarking Warehousing and Distribution Operations: An Input-Output Approach. Journal of Productivity Analysis. 2001;16(1):

15 12 McGinnis LF, Chen WC, Griffin P, Sharp G, Govindaraj T, Bodner D. Benchmarking Warehouse Performance: Initial Results for Internet-based Data Envelopment Analysis for Warehousing. W. M. Keck Virtual Factory Lab. 2006; (ideas-w 1.0). 13 U.S. Census Bureau. U.S. Interim Projections by Age, Sex, Race, and Hispanic Origin. March 18,

16 Table 1: Vaccine Delivery Duration Statistics (In weekdays) Before VMBIP After VMBIP Average Delivery time Standard Deviation of delivery time Median Delivery time 1 13 Probability of delivery time greater than 1 week 7% 89% Table 2: Days/year with 0 vaccines Vaccine Post Pre p-value Dtap Dtap/Hep B/IPV Hep A Hep B Hib IPV MCV MMR PCV Td Tdap Table 3: Number of children not up-to-date on vaccine series due to out-of-stock inventory at 12 months (millions) Vaccine VMBIP Pre-VMBIP Dtap/Hep B/IPV Dtap, Hep B, IPV (separate vaccines) Hep A Hib MMR PCV Assumes a birth cohort of million (Census bureau estimate) 9

17 Figure 1: Vaccine Delivery Duration Number of weekdays Date San Diego Provider Orange County Provider Figure 2 MMR Hib Probability of running out of vaccines Probability of running out of vaccines Reserve quantity of vaccines when ordering Reserve quantity of vaccines when ordering pre_mmr post_mmr pre_hib post_hib

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