S100
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`VA L U E I N H E A LT H 2 1 ( 2 0 1 8 ) S 1 – S 2 6 8
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`PHP79
`THE 2018 US PAYOR LAnDSCAPE: TREnDS AnD RESULTS fROM fORMULARY
`MAnAgEMEnT SURvEYS
`Brook RA1, Carlisle JA2, Smeeding JE3
`1The JeSTARx Group & TPG-NPRT, Newfoundland, NJ, USA, 2The TPG-NPRT, Glastonbury, CT,
`USA, 3The TPG-NPRT & JeSTARx, Glastonbury, CT, USA
`objeCtiveS: Understand the P&T decision-making process, formulary reviews/
`coverage and changes from prior surveys. MethodS: Online survey sent to 737 US
`medical and pharmacy officers on: respondent+plan information, formulary cover-
`age/restrictions. ReSultS: Survey completed by 77 respondents, 57% were MDs,
`43% were the senior officer,19% were payor specific, 9.9% regional, 1.3% therapeutic
`area specific. 40.5% worked for health plans, 11.4% PBMs, 8.9% IDNs, 3.8% PPOs/
`IPAs,1.3% Government. Plans were National= 39.2%,regional= 27.5%,or local= 33.3%
`and cover multiple members-types: commercial (68.8%= FFS,76.5%= HMO/PPO),
`Medicaid (Traditional= 36.4%,HMO/PPO= 67.9%), Medicare (71.2%,PDP-only= 50%),
`Employer/Self-funded= 77.1%,and IDN (47.7%,340B= 43.5%). Plans covered clinician-
`administered products under the medical-benefit (44.1% ↑ from 15.2%), 1.4% under
`the pharmacy-benefit, the remainder based on price+plan design, 72. 9% do not
`expect changes. Parity policies are in place for self-administered and clinician-
`administered agents for: no plans= 24.4% (↓ from 33.3%), select-plans= 28.9%, all-
`members= 20%(↓ from 25.6%), mandated-states= 8.9%, commercial plans= 8.9%,
`Medicaid plans= 8.9%. Mental health (MH) products were carved-out by 27.3% of
`plans (↓ from 35.9%), conditions with multiple MH-therapies required: generics-first
`(63.8% ↑ from 41.2%), step-therapy (68.1% ↑ from 41.2%) or Psychiatrist/specialist care
`(29.8% ↑ from 17.6%). MH parity policies were in place for: All= 54% (↓ from 62.5%),
`None= 2% (↓ from 10%), Mandated-states= 18%, Commercial-plans= 16%, Select-
`plans= 12%,Medicaid-plans= 14% (↑ from 7.5%), Never heard of= 10%. Respondents
`are involved in coverage decisions for: Rx-drugs (All= 65.6%,some= 32.8%) and
`Medical-devices (All= 34.5%,some= 58.2%). 88.2% of Rx and 81.6% of medical-device
`reviews included budget impact models and 42.3% of the models were solely devel-
`oped internally, the remaining in combination with the manufacturers. Biosimilar
`use is expected to provide some relief. Most respondents were happy with their
`medical-benefit, the most request change was moving all drugs to the pharmacy-
`benefit. Top concerns today and in the future included Oncology, Diabetes and
`Cardiovascular diseases. ConCluSionS: The P&T decision-making process con-
`tinues to change to better manage increasing costs and committee members, have
`distinct opinions as to how to alter the process to adapt to these influences.
`
`PHP80
`InCLUSIOn Of DRUgS In A fORMULARY: A CASE STUDY
`Jaraki J1, Levy X1, Palacios E2, Zapata L2, Aguilar JL3, Marquez M3
`1Florida Atlantic University, Boca Raton, FL, USA, 2Guia Mark, Mexico - D.F, Mexico, 3Vitamédica
`S.A. de C.V., Ciudad de México, Mexico
`objeCtiveS: To describe the process, evaluation criteria, and possible out-
`comes of decision-making to redesign a formulary in a private insurance com-
`pany (PIC). MethodS: The project will be conducted in three phases. This report
`describes the results of Phase I consisting in data analysis of the current prescrip-
`tion patterns in the PIC and determination of the potential medications more
`likely to be included in the new formulary. The files of medications prescribed in
`a one-year period were analyzed. Quantity of medications prescribed by group,
`and the association with their diagnoses codes was analyzed. Medications with
`higher impact on the total cost of medications per-year were selected. Diseases with
`higher prevalence were selected to conduct a literature review of clinical guidelines,
`meta-analyses, and experts reviews published to determine treatment algorithms.
`Cost-effectiveness analyses and budget impact evaluations will be conducted for
`the medications of higher utilization for the selected diagnoses. ReSultS: A total
`of 417,073 prescriptions were analyzed by type of medications and diagnosis code.
`Diabetes and HBP were analyzed in a previous study. Forty-six percent of the medi-
`cations were utilized for upper-respiratory tract infections. A significant percentage
`of these medications were prescribed for treatment of symptoms. Ten-percent of
`the medications were gastrointestinal diagnoses (acute gastritis, gastro esopha-
`geal reflux, duodenitis, colitis). Other significant diagnoses were osteoarthritis and
`low back pain. The proportion of impact in cost was consistent with percentages
`described for quantity of medications, except antiretroviral treatment for HIV and
`Hepatitis B representing a high-cost impact with low-prevalence of the disease
`(1%). ConCluSionS: Analyses based in real-world data provides a better under-
`standing of needs of specific populations. Based on the prescription profile of the
`PIC, cost-effectiveness analyses of the treatments for prevalent diseases will be
`conducted along with a budget impact analyses to provide scientific and health
`economics evidence to the decision-makers.
`
`PHP81
`ESTIMATIOn Of SUPPLY SIDE COST EffECTIvEnESS THRESHOLD In UkRAInE:
`PERSPECTIvE USE In HEALTH CARE DECISIOn-MAkIng
`Topachevskyi O1, Piniazhko O1, Lebega O2, Oleshchuk O1
`1National EML Committee, Kiev, Ukraine, 2Technical SAFEMED Advisor, Kiev, Ukraine
`objeCtiveS: Estimate supply side cost effectiveness threshold (CET) and
`develop recommendations to guide reimbursement decision-making in
`Ukraine. MethodS: Literature review was conducted to identify methods and
`data sources required to estimate the supply side CET in Ukraine. Methods iden-
`tified: empirical estimation of CET and evaluation of the effect of health care
`expenditure on mortality outcomes using cross country level data; analysis of
`the previous reimbursement decisions; estimates of CET for lower and middle
`income countries by Ochalek et al. (2015); revised WHO recommendation on setting
`the CET. The following data sources were used to estimate Ukraine specific CET:
`mortality rates provided by World Bank, empirical analysis of UK CET by Claxton
`et al. (2013), expected GDP per capita of USD 2,700 in year 2018. The estimates of
`Ukraine CET were benchmarked against CET estimates for Ukraine neighbouring
`
`countries and countries with similar level of economic development. ReSultS:
`The cost per QALY CET was estimated at 19%-66% of GDP or USD 513-1,782. The
`updated WHO recommendation on setting CET indicated that local data needs to
`be taken into account and that the 1-3 GDP per capita threshold should not be used
`as a rule of thumb, as this can potentially lead to a wrong reimbursement decision
`and forgone health. ConCluSionS: The local analysis of effects of health care
`expenditures on health gains can’t be performed due to lack of detailed country
`level data in Ukraine. As of 2018 the results reported by Ochalek et al. (2015) is a
`sole source of CET data for Ukraine. Multi-criteria decision analysis frameworks
`have also been suggested by WHO. Ukraine should consider establishing a context-
`specific process for decision-making that is supported by legislation, has stake-
`holder buy-in and is consistent, fair and transparent.
`
`HEALTH CARE USE & POLICY STUDIES - HEALTH CARE COSTS & MAnAgEMEnT
`
`PHP82
`UTILIzATIOn EvALUATIOn Of AnTIbIOTICS AT CRITICAL CARE SETTIng Of A
`TERTIARY CARE HOSPITAL
`Patel DS, Awhamefule UI, Joy JE, Pakalapati N, Madhan R
`Jagadguru Sri Shivarathreeswara University, Mysuru, India
`objeCtiveS: The study aimed to assess the nature and extent of antibiotics use,
`medication errors and the actual cost associated with the use of antibiotics at criti-
`cal care setting. MethodS: The study was conducted over a period of 6 months
`in a South Indian Tertiary Care Hospital. Patients of either gender aged > 18 years
`who were admitted into critical care unit for more than 24 hours and receiving at
`least one antibiotic were included in the study. All relevant details of each patient
`was collected from patient’s case notes, treatment charts, and laboratory/diagnostic
`test reports, billing reports and evaluated. All the diagnoses and antibiotics were
`coded according to the International Classification of Disease 10 and Anatomical
`Therapeutic Classification codes respectively. Medication errors were classified as
`per National Coordinating Council for Medication Error Reporting and Prevention
`(NCC MERP) guidelines. The actual cost associated with use of antibiotic was cal-
`culated. IBM SPSS software was used to analyse the data. ReSultS: Of the 744
`prescriptions reviewed, 500(67%) patients met the study criteria and were included
`in the study. The average number of drugs per patient was 14.16, while the aver-
`age number of antibiotics was 2.32. Beta-lactam +beta-lactamse inhibitors (n= 352,
`30.29%) and Cephalosporins [247(21.26%)] were the commonly prescribed antibiotic
`class. Ceftriaxone (n= 206, 25.75%) was the most commonly prescribed antibiotic
`while Piperacillin+tazobactum [n= 173(47.79%)] was the most commonly prescribed
`fixed drug combination. A total of 287(57%) patients were prescribed with antibiot-
`ics as an empirical therapy. A total of 47 medication errors were identified from 36
`(7.2%) patients. The average cost of antibiotics used per patient was 4,907.2 [Range:
`20.79-67,308.5] Indian Rupees (INR). ConCluSionS: Antibiotics were prescribed in
`67% of the patients and the average cost of antibiotics per patient was ₹4,907.2. The
`incidence of medication errors with the use of antibiotics was 4.04%.
`
`PHP83
`ARE wHOLE ExOME AnD wHOLE gEnOME SEqUEnCIng APPROACHES COST-
`EffECTIvE? A SYSTEMATIC REvIEw Of THE LITERATURE
`Schwarze K, Buchanan J, Taylor JC, Wordsworth S
`University of Oxford, Oxford, UK
`objeCtiveS: Evidence suggests that next generation sequencing technologies
`could improve the diagnosis and treatment of genetic diseases. However, demand
`is increasing for evidence on the costs and health outcomes associated with these
`technologies. Our objective was to conduct a systematic literature review to sum-
`marise the current health economic evidence for the use of whole exome sequenc-
`ing (WES) and whole genome sequencing (WGS) in clinical settings. MethodS:
`Relevant studies were identified in the EMBASE, MEDLINE, Cochrane Library,
`EconLit and University of York Centre for Reviews and Dissemination databases
`from January 2005 to July 2016. Publications were included in the review if they were
`economic evaluations, cost studies or outcome studies. Data were extracted from
`each publication on sample size, sequencing methods, study methods, outcomes,
`costs and main results. ReSultS: Thirty-six studies met our inclusion criteria
`(21 economic evaluations, 7 cost studies, 8 outcome studies). These publications
`investigated the use of WES and WGS in a variety of genetic conditions, the most
`common being neurological or neurodevelopmental disorders. Study sample size
`varied from a single child to 2,000 patients. Cost estimates for a single test ranged
`from $555-$5,169 for WES and from $1,906-$24,810 for WGS. There was no evidence
`that the cost of WES was falling over time, and only limited evidence that the cost of
`WGS was reducing. Few cost analyses presented data transparently or stated which
`components were included in cost estimates. In addition, few studies used outcome
`measures recommended for use in economic evaluations, such as survival or quality
`of life. ConCluSionS: The current health economic evidence base to support the
`more widespread use of WES and WGS in clinical practice is very limited. Studies
`that carefully evaluate the costs, health outcomes and cost-effectiveness of these
`tests are urgently needed to support their translation into clinical practice.
`
`PHP84
`ASSESSIng THE bURDEn Of DISEASE-ASSOCIATED MALnUTRITIOn AMOng
`HOSPITALIzED MALnOURISHED COLOMbIAn PATIEnTS wITH HEART AnD
`LUng DISEASE
`Ruiz A1, Misas JD2, Gomez G2, Sulo S3, Dennis RJ4, Buitrago G1, Rodriguez N1, Gomez C5,
`Alba M6, Chaves W7, Araque C8
`1Pontificia Universidad Javeriana Medical School, Bogota, Colombia, 2Abbott, Bogota, Colombia,
`3Abbott Nutrition, Abbott Park, IL, USA, 4Fundación Cardioinfantil, Bogotá, Colombia, 5Pontificia
`Universidad Javeriana, Bogota, Colombia, 6Fundación Universitaria de Ciencias de la Salud.,
`bogota, Colombia, 7Hospital Universitario de San José., Bogota, Colombia, 8Hospital Universitario
`Infantil de San José., Bogota, Colombia
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`Personalis EX2014.001
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