2018 Capitation Rate in Ukraine

Transcription

1 2018 Capitation Rate in Ukraine

2 ACKNOWLEDGMENTS The USAID HIV Reform in Action Project conducted the «2018 Capitation Rate in Ukraine» study with technical expertise and contribution from various national and international experts. The USAID HIV Reform in Action Project greatly appreciates the financial support provided for this study by the United States Agency for International Development. This study was authored by Dr. Sanjit Puri (Senior Manager, Federal Human Capital, Deloitte Consulting LLP), Maksym Duda, PhD (Senior Financing Advisor and Health Finance Component Lead, USAID HIV Reform in Action Project, Deloitte Consulting LLP), Nataliia Kovalenko (Health Finance Advisor, USAID HIV Reform in Action Project, Deloitte Consulting LLP), Dr. Lijia Guo (Specialist Leader, Deloitte Consulting LLP), Palak Jhaveri (Consultant, Deloitte Advisory LLP). The authors of this study would like to thank Nataliia Shapoval, Vadym Bizyayev, and Olexandra Chmel (of the Kyiv School of Economics) for conducting the comprehensive data collection and consolidation steps that enabled the team to develop the primary care capitation rates for In addition, the authors would like to acknowledge the guidance and technical leadership of Dr. Nata Avaliani (USAID HIV Reform in Action Project, Deloitte Consulting LLP). We appreciate the guidance and support of the Deputy Minister of Health Pavlo Kovtonyuk. We are also very grateful for the support and assistance received from USAID/Ukraine. Finally, the team is thankful for the input of all who have contributed to the development of the primary health care capitation rates for calendar year Disclaimer This publication is made possible due to the funding provided by the U.S. President s Emergency Plan for AIDS Relief through the United States Agency for International Development (USAID) under the terms of the HIV Reform in Action Project; award number AID-121-A The content of this publication is the sole responsibility of Deloitte Consulting LLP and its implementing partners and does not necessarily reflect the opinion of USAID or the United States Government. Suggested format for citation: The USAID HIV Reform in Action Project. (2018) Capitation Rate in Ukraine. Kyiv, 2018.

3 Table of Contents 1. Introduction, Scope of Work, and Results Summary of Data...4 A. Regions of Ukraine Represented in the Study...5 B. Population Analysis...5 C. Data Quality...6 D. Form-20 Data Summary...6 Staff Data Analysis...7 Utilization Data Analysis...8 Trend Analysis...8 E. Plan and Actual Budget Summary Methodology for Capitation Rate Development...10 A. Phase 1 - Projection of PPPY utilization...11 Utilization Trends...11 Managed-Care Efficiency...11 Pent-up Demand...11 B. Phase 2 - Projection of cost per visit...12 Provider-Efficiency Adjustment (PEA)...12 Inflation Adjustment...13 C. Phase 3 - Calculation of capitation rates Limitations of the Study and Next Steps Sensitivity Analysis...19 A. Comparison of Estimated Capitation Rates to Private Sector...19 B. Comparison of Estimated Capitation Rates to Paid Services in the Public Sector Certification

4 INTRODUCTION, SCOPE OF WORK, AND RESULTS

5 1. INTRODUCTION, SCOPE OF WORK, AND RESULTS Health financing reform with the introduction of a national strategic purchaser and performance-based payment for services are pivotal components of the Concept for Health Financing System Reform (adopted on November 30, 2016). Following adoption of the concept, Ukraine then ratified the law «On Government Financial Guarantees of the Provision of Medical Care» (adopted on October 19, 2017). The Government of Ukraine is committed to launch the reforms, and the Ministry of Health (MoH) team requested technical assistance to implement these critical interventions successfully. The United States Agency for International Development (USAID) through HIV Reform in Action Project (HIVRiA), implemented by Deloitte Consulting LLP provides technical assistance to advance health financing system reforms. Ukraine will transition to a capitation payment system for primary health care (PHC) services from July 1, In March 2017, the MoH requested support from USAID in calculating the capitation rate, using internationally accepted actuarial methods, and utilization and cost data for PHC services delivered in Ukraine. As part of the technical assistance, the USAID HIVRiA developed per patient per year (PPPY) capitation rates for PHC services to be delivered in calendar year (CY) 2018 following the best actuarial practice guidelines of the Society of Actuaries of the North America (SOA) 1 and the American Academy of Actuaries. Based on the results of the study, the recommended PHC capitation rate for CY 2018 is UAH 350. Table 1 shows the breakdown of the capitation rate of UAH 350 for two service categories (Outpatient Visit and Home Visit) and an Emergency Room Add-on rate for facilities maintaining an emergency room. The total rate shown in Table 1 below is an annual rate for an age rating factor of 1, which is the rating factor for the entire population of Ukraine. Based on the rating factors determined by MoH, the capitation rate should be adjusted for the appropriate age group. Table 1. CY 2018 Recommended Capitation Rate for Primary Care Services Service Amount Outpatient Visit UAH 289 Home Visit UAH 61 Total UAH 350 Emergency Room Add-on 2 UAH 4 After the launch in July million individuals in Ukraine will participate in the PHC capitation program, resulting in a total estimated spend of UAH 14 billion on PHC services. The following sections of this report document the data, assumptions, and methodologies used to develop the 2018 PHC capitation rate using healthcare data collected through a stratified sampling approach from 100 PHC facilities in Ukraine. 1 Web page of SOA: [With roots dating back to 1889, the Society of Actuaries (SOA) is the world s largest actuarial professional organization with more than 28,000 members worldwide.] 2 This add-on rate should be paid only to those PHC facilities that provide emergency services. This rate should not be adjusted for agerating factors. INTRODUCTION, SCOPE OF WORK, AND RESULTS 3

6 SUMMARY OF DATA

7 2. SUMMARY OF DATA A. Regions of Ukraine Represented in the Study To develop the capitation rates for 2018, data related to utilization and cost of services were collected from 100 PHC facilities across 10 regions of Ukraine. The Kyiv School of Economics collected the data over four months (June-September 2017) through a grant approved by USAID. Chart 1 shows the distribution of PHC facilities across Ukraine by catchment population, which provided the utilization and cost data used in the study. Chart 1. Distribution of PHC Facilities by Region B. Population Analysis The project team analyzed the general Ukrainian population to determine trends by age and gender. As illustrated in the Chart 2 below, Ukraine s population appears to be aging and declining from years 2015 through Additionally, as shown in Chart 3, females in Ukraine tend to live longer than males according to data from Chart 2. CY Population Distribution by Age SUMMARY OF DATA 5

8 Chart 3. CY 2017 Population Distribution by Gender C. Data Quality The data from 100 PHC facilities are statistically significant since they represent approximately 4.8 million patients, or approximately 11.4% of the total population of Ukraine. Analysis excluded data determined to be outliers (two standard deviations from the mean), which resulted in the removal of approximately 350 patient records from the original dataset. We excluded these data to minimize and control for bias in the study. The final scrubbed data used in the development of the capitation rates represent 10.6% of the total population of Ukraine and are statistically significant. D. Form-20 Data Summary Project team analysts used Form-20 as the basis to aggregate the utilization and staff data. The Kyiv School of Economics obtained Form-20s from 100 PHC facilities across 10 regions of Ukraine shown in Chart 1. Analysis aggregated and used the following data elements as documented in Form-20 in the development of the capitation rates: a. Staff Data (used to develop unit cost and provider efficiency adjustment) i. Total doctors from Row 1 of Table ii. Total nurses from Row 94 of Table iii. Total other nurses from Row 108 of Table iv. Total support staff from Row 109 of Table b. House Call Data (used to develop the number of Home Visits PPPY) i. Total house call utilization from Table split between ages 0-17 and 18+ ii. Total house call utilization from Table split between ages 0-17 and 18+ c. PHC Service Utilization Data (used to develop the indicator of PPPY Outpatient Visits). The following information at the sub-regional level (rayon or district) from Table was collected: i. Total visits ii. Rural visits iii. Number of home visits iv. Number of home visits to children 6 SUMMARY OF DATA

9 In addition to national census data by age group, the Kyiv School of Economics collected data on the number of individuals in catchment areas served by primary care centers (including individuals who did not use PHC services as well as those who did) (Rayon at the Oblast level and District at the City level). The utilization and staff data collected through aggregation of Form-20 was analyzed for the distribution of staff members across the facilities (i.e. doctors, nurses, and support staff), the average number of hours spent PPPY by type of staff, the utilization of services by patient, and the trend in the utilization of services from 2014 to Staff Data Analysis The analysis of the data shows considerable variation in the distribution of staff members by type across 100 PHC facilities. As seen in Chart 4, doctors represent approximately 22% of the total staff employed by a PHC, ranging from a low of 10% to a high of 42%. On average, the data indicate that for every doctor employed in a PHC facility, there are approximately two nurses and one support staff employed. Chart 4. CY 2016 Staff Distribution by PHC Facility The analysis of hours spent by the staff shows that on average, staff spent 5.6 hours per person per year (PPPY) in CY 2016 in providing care to patients residing in PHC catchment areas. Of these total hours spent, nurses provided a majority of the care with 2.9 hours spent PPPY. The data also show that on average, in CY 2016, each PHC doctor provided care to approximately patients per year. Chart 5 3 presents a comparison of the patient-to-phc-doctor ratio in Ukraine to those of other countries in the region. As can be seen in Chart 5, Ukraine is slightly below average when compared against other Eastern European countries with respect to the number of patients per PHC doctor. The blue bars in Chart 5 indicate Eastern European countries that have healthcare delivery system similar to Ukraine, whereas black bars in Chart 5 indicate Western European countries having a healthcare delivery system whereby the PHC doctor acts as a gatekeeper in providing and coordinating patients care at all levels (primary, secondary, and tertiary). 3 Source: Eurostat Statistics Explained - Physicians, by Specialty, SUMMARY OF DATA 7

10 Chart 5. CY 2015 Number of Patients per Primary Care Physician by Country Utilization Data Analysis Data collected from the 100 PHC facilities shows considerable variation in the number of visits by adult and child. On average, the child age group (0-17 years of age) visits a PHC approximately 2.6 times more frequently than the adult age group (18 years and over). The children-to-adult ratio of home visit utilization is practically identical, almost three times that of adults. Chart 6 summarizes the visits PPPY by child vs. adult and by type of visit for CY Chart 6. CY 2016 Utilization by Type of Service Trend Analysis Analysis of the utilization from 2014 through 2016 shows no consistent pattern in the utilization trend, as illustrated in Chart 7. Children showed an increase in the number of visits in 2015 and a decline in the number of visits in 2016 compared to SUMMARY OF DATA

11 Chart 7. CY 2014 to CY 2016 Utilization Trend To project the number of visits per patient in CY 2016, analysis examined the tendency of utilization using the annual trend shown in Table 2 below. Analysts selected these trends based on the historical information on number of visits as previously shown in Chart 7. Table 2. CY 2018 Utilization Trend Child Adult Trend (Outpatient Visit) 3% 0% Trend (Home Visit) 4% 3% E. Plan and Actual Budget Summary To develop the cost for each visit, the Kyiv School of Economics collected plan and actual 4 budget (money spent during the year) data from 73 PHC facilities for CY These data applied to outpatient and home visits combined and allowed us to develop a per-visit cost for the PHC facility for CY The analysis shows considerable variation in the allocated cost per visit across the 73 facilities. Since budgetary allocations across facilities do not distinguish home visits from outpatient visits, information unavailable to develop disaggregated costs for the two types of visits. For the purpose of the study, analysis assumed equivalence in the cost for home and outpatient visits. The analysis shows that the allocated cost per visit for CY 2016 ranges from a low of UAH 31 to a high of UAH 133 with an average cost of UAH Project analysis used the CY 2016 average cost per visit of UAH across the 73 PHC facilities as the basis for modeling the CY 2018 cost. We projected the CY 2018 cost per visit to be UAH Section 3 below describes projection assumptions and methodology. 4 Plan budget the budget approved at the beginning of the year. Actual Budget the budget reported at the end of the year. SUMMARY OF DATA 9

12 METHODOLOGY FOR CAPITATION RATE DEVELOPMENT

13 3. METHODOLOGY FOR CAPITATION RATE DEVELOPMENT The product of expected utilization (visits), on a PPPY basis, and the expected cost for each visit determined the capitation rates. Analysis further refined rates to align with budget and age-based premiums. As such, the methodology implemented comprised the following three phases: z Phase 1: Projection of PPPY utilization z Phase 2: Projection of cost per visit z Phase 3: Calculation of capitation rates А. Phase 1 - Projection of PPPY utilization Managed-Care Efficiency As part of this phase, the Kyiv School of Economics collected healthcare utilization and cost data, using a stratified sampling approach that involved 100 PHCs from 10 regions of Ukraine as described in Section 2. Upon initial demographic analysis of the data, it was determined that utilization projections would be performed separately for the children and adult populations for the two types of services, outpatient and home visit, given that the populations exhibited substantially significant differences in utilization over the three-year period as shown in Chart 7 of Section 2. To project CY 2018 utilization, analysis of CY 2016 utilization adjusted for three considerations, each described in detail in the following sections: Utilization Trends As the most recent year of data available, CY 2016 set the base year for identified trends would be applied. A trend analysis for the two populations, by type of service, generated four distinct utilization trend rates. Section 2 of the report documents the analysis of the historical trend and the selection of the CY 2018 trend as shown previously in Table 2. Analysis applied a managed-care efficiency adjustment to account for a decrease in utilization, as is generally observed with an initial implementation of capitation rates. A positive managed-care efficiency factor represents a decrease in utilization. A factor of 10% was selected based on actuarial judgement for the expected utilization and mix of services for the population of Ukraine. Pent-up Demand With healthcare reform comes pent-up demand, bringing more enrollees, previously unable to access healthcare services, into the capitation system, many of whom were likely unhealthy prior to the healthcare reform. This will increase costs to the entire healthcare system. Since no actual data are available related to pent-up demand, capitation analysis based the assumed demand on actuarial assessment of increased utilization seen in plans implemented as part of the ACA in the United States. Table 3 summarizes the utilization projection assumptions applied to CY 2016 base-year data to develop CY 2018 utilization projections. METHODOLOGY FOR CAPITATION RATE DEVELOPMENT 11

14 Table 3. Utilization Projection Assumptions Assumption Child Adult Trend (Outpatient Visit) 3% 0% Trend (Home Visit) 4% 3% Managed Care Efficiency 4% 4% Pent-up Demand 10% 10% Applying the projection assumptions shown in Table 3 above to the CY 2016 utilization shown previously in Chart 6 results in the projected CY 2018 utilization seen in Chart 8 below. The aggregate utilization amongst both the population types and the service type results in projected CY 2018 utilization of 3.51 visits PPPY. Chart 8. CY 2018 Utilization by Type of Service B. Phase 2 - Projection of cost per visit As part of this phase, the average cost per visit was calculated. The healthcare cost data collected during Phase 1 provided CY 2016 actual budget information for 73 of the 100 PHC facilities under study. Analysis classified the actual budget for each facility into three major categories: equipment, salaries, and other costs and allocated it across the 2016 utilization to develop a per-visit unit cost of UAH Analysis this unit cost for provider efficiency and inflation prior to projecting CY 2018 unit cost. Provider-Efficiency Adjustment (PEA) Provider efficiency was applied to adjust for salaries, resulting in an adjusted cost per visit of UAH for CY The provider efficiency accounted for the implementation of the capitation rate resulting in less time spent on providing the same level of care as that provided prior to the implementation of capitation rates. PEA analysis examined the three staff types (doctors, nurses, and support staff) separately. In 2016, in Ukraine each PHC doctor on average provided care to patients. In developing the capitation rates after taking into account the provider efficiency adjustment, each PHC doctor in Ukraine is expected to provide care to patients in a year. To develop the provider efficiency adjustment, project analysis selected patient panel size estimation assumptions 5 from the analysis performed by Duke University of the amount of time a doctor spent on a patient per visit. The Duke University analysis provided four panel sizes based on the amount of 5 Table 1 from the study Estimating a Reasonable Patient Panel Size for Primary Care Physicians With Team-Based Task Delegation. Source: 12 METHODOLOGY FOR CAPITATION RATE DEVELOPMENT

15 time a doctor delegated to lower staff. Table 4 shows the time delegated by the doctor and the estimated number of patients to whom a doctor can provide care. From Table 4 below, analysis selected Delegation Model 3, with an annual patient size of 1 387, to guide provision of care to children, and Delegation Model 2 with an annual patient size of 1 523, to do the same for adults. Blending the two models resulted in an annual estimate of patients per year per doctor. Similar adjustments applied to the nurses and the support staff to develop provider efficiency adjustments. Table 4. Patient Size per Delegated Model Estimated by Duke University Type of Care Non-Delegated Model % Doctor Time Delegated Delegated Model 1 Delegated Model 2 Delegated Model 3 % Doctor Time Delegated % Doctor Time Delegated % Doctor Time Delegated Preventive 0 77% 60% 50% Chronic 0 47% 30% 25% Acute 0 0% 0% 0% Total number of Patients We applied the provider efficiency adjustment, developed through the blending of Model 2 and Model 3, to the CY 2016 salaries collected from the budgets for the 73 PHC facilities to revise the total budget for the improved staff performance. This revised budget was then allocated across the total CY 2016 utilization of services to develop the per visit cost of UAH Inflation Adjustment To account for the rising cost of providing care and the increase in the minimal wage between CY 2016 and 2018, analysis applied inflation adjustments and wageincrease adjustments to the 2016 total budget adjusted for staff performance in the step above. The inflation adjustment utilized the costs allocated to the equipment and other cost categories. Salary assessments for doctors and nurses determined compensation to be above the minimal wage requirements for CY 2018, and thus increased by the inflation adjustment. The minimal wage change from 2016 through 2018 was applied to the salaries allocated to the support staff. Table 5 shows the assumptions used in projecting the costs for this period. Estimates further raised the costs allocated to equipment by 10% to account for additional funds for PHCs to modernize health facility equipment. Research grounded this assumption in actuarial judgment and discussions with various PHCs. Table 5. Summary of adjustments for CY 2018 Year Inflation Minimum Wage Equipment Modernization % UAH % UAH % UAH Applying these three adjustments led to a final calculated cost per visit of UAH for CY METHODOLOGY FOR CAPITATION RATE DEVELOPMENT 13

16 C. Phase 3 - Calculation of capitation rates Since data were unavailable for developing the cost per visit for children and adults, the utilization estimated in Phase 1 for children and adults informed development of the weighted average utilization estimate of 3.51 visits for CY 2018 for all outpatient and home-visit services. This utilization was converted into an estimated PPPY capitation rate of UAH 350 using the cost per visit of UAH estimated in Phase 2. The analytical team recommends a composite capitation Rate of UAH 350 for CY The composite capitation rate of UAH 350 was aligned to budget and age-based premiums. First, Ukrainian census data collection by age group over the determined a two-year population trend. The United States SOA-published data on aging factors, 6 applied to the Ukrainian population, then underwent adjustment to incorporate the following differences to normalize the aging factor: 1. differences in child and adult utilization 2. differences in home visits in the U.S. vs. in Ukraine 3. observed aging of the population of Ukraine 4. budgetary neutrality Based on the adjusted SOA factor, Table 6 below represents the age-based rates for the population of Ukraine for CY Table 6. Age-based Premium Age Group Aging Factor Capitation Rate Composite Capitation Rate UAH 350 Emergency Room Add-on Rate UAH 4 6 Health Care Costs From Birth to Death Report, published by Society of Actuaries. Source: 14 METHODOLOGY FOR CAPITATION RATE DEVELOPMENT

17 LIMITATIONS OF THE STUDY AND NEXT STEPS

18 4. LIMITATIONS OF THE STUDY AND NEXT STEPS Capitation analysis encountered several limitations during the study. Table 7 describes the limitations, their impact, and the potential solution for mitigating the limitation for setting the capitation rate for CY Table Capitation Study Limitations Limitation of Study Utilization was not available by age groups used to reimburse the capitation rate to the PHC, therefore the aging factors could not be developed using existing utilization and cost data specific to the population of Ukraine. Salary data sources do not disaggregate by type of staff. Projecting the salary for CY 2018, analysis assumed that all salaries received either the general inflation adjustment or the minimum-wage-increase adjustment. Facilities did not provide information on type of service performed per visit. Therefore, analysis assumed the same cost for children and adults and for outpatient and home visits. Ideally, the cost of the visit should depend on the mix of services performed specific to a given age group and should differ by type of visit, in this case, outpatient or home visit. Data from PHC facilities in the western and southern regions of Ukraine were limited. This could result in the utilization of the sample not being fully representative of the utilization for the entire population of Ukraine. Analysis did not have access to data on time spent per home visit. Therefore, information was unavailable to distinguish the cost of home visits from that of outpatient visits. Because of this limitation, the study assumes equal home and outpatient visit costs and equal reimbursement rates for facilities regardless of where the services were performed. Additional Steps for Development of CY 2019 Capitation Rate Obtain a breakdown of utilization by five-year age groups to develop an aging curve specific to Ukraine s population, and reimburse PHC facilities based on the population expected to enroll in them. Obtain a breakdown of total salary by staff type to improve the prediction of cost per visit by applying the appropriate salary increase by staff type. Use medicals cards to obtain a distribution of services performed per visit to develop the mix of services specific to the age group being valued. Use this information to develop population-specific cost that is representative of the services used by patients based on their age group. Increase the number of facilities to obtain data from all regions of Ukraine to represent better the expected utilization for the entire population of Ukraine. Conduct costing analysis to assess the relative cost of home visits to outpatient visits to develop the capitation rates that reflect the expected utilization of services at the location they are performed. 16 LIMITATIONS OF THE STUDY AND NEXT STEPS

19 Limitation of Study The capitation rate did not incorporate informal payments. Ukraine has an inherent issue whereby PHC facilities require patients to pay for services that they should provide free of cost. Data were not available in the study to estimate the amount of informal payments to factor into the capitation rate. The purpose of including the informal payments in the capitation rates is to reduce the incidence and amount of informal payment expected from patients after the implementation of the capitation rates. Additional Steps for Development of CY 2019 Capitation Rate Conduct an informal payment study to assess the magnitude of the payment, and revise capitation rates to include an estimate of informal payments to reduce their incidence and amount. LIMITATIONS OF THE STUDY AND NEXT STEPS 17

20 SENSITIVITY ANALYSIS

21 5. SENSITIVITY ANALYSIS А. Comparison of Estimated Capitation Rates to Private Sector The study conducted a sensitivity analysis to analyze the relativity of the capitation rates developed as part of this study to the services provided in Ukraine s private sector. Twenty-one private clinics provided data from 13 regions of Ukraine to obtain an estimate of the cost for a single PHC visit. The analysis divided the data into urban and rural regions to account for the difference in cost of living between the regions. Table 8 shows the range (minimum, maximum, and average) of the cost of service and the expected capitation rate using the expected utilization of services. Table 8. Cost of Services in the Private Sector, UAH Minimum Maximum Average Rural Visit Procedures Total Capitation Rate Urban Visit Procedures Total Capitation Rate As can be seen from the table above, the capitation rate estimated using the cost of services being provided in the private sector healthcare ranges from a low of UAH 512 to a high of UAH 2 521, which is substantially higher than the capitation rate estimated using the cost data from the PHC facilities financed from the state budget. We estimate that to eliminate informal payments from PHC services in Ukraine, the capitation rate will need to be set at a level consistent with the cost of providing those services in the private sector. B. Comparison of Estimated Capitation Rates to Paid Services in the Public Sector We compared estimated capitation rates to obtain the cost of the services performed in the public facilities for a tariff. Data on service cost of services came from 14 PHC facilities in eight regions of Ukraine. We did not establish a one-to-one comparison of services indicated by the MoH as part of PHC benefits and PHC facility services performed for a tariff. As an approximation, we related services SENSITIVITY ANALYSIS 19

22 for obtaining a driver s license to those indicated by the MoH under the PHC benefits package. Our analysis determined that the services performed by the PHC to assist the patient to obtain a driver s license functioned as a good proxy for the services indicated by the MoH as part of the PHC benefit package. Table 9 shows a comparison of these services. Table 9. Comparison of Primary Care and Driver s License Services Per MOH for Primary Care Driver s License Requirements Blood tests 1. Complete Blood Count (CBC) with differential 1. Complete Blood Count (CBC) with differential Biochemical and immunochemical blood serum tests 2. Blood glucose 2. Blood glucose 3. Blood cholesterol 3. Blood cholesterol Urine tests 4. Clinical urinalysis 4. Clinical urinalysis Instrumental examination methods 5. Electrocardiogram (ECG) at rest 5. Electrocardiogram (ECG) at rest 6. Microscopy of sputum 6. Acuity and field of vision Rapid tests 7. Rapid tests for HIV, viral hepatitis, and syphilis 7. Identification of blood type and Rh Factor In the next step, the tariffs collected from the 14 PHC facilities provided the cost of the services performed by the PHC facilities for obtaining the driver s license. We then applied this cost to the estimated CY 2018 utilization obtained in Phase 1 to obtain an alternate estimate of the capitation rate for services performed in the public sector in Ukraine. Table 10 shows the cost of the services and the estimated capitation rate using driver s license fees as a proxy of the cost of services. Table 10. Capitation Rates Using Driver License Fees as a Proxy Minimum Maximum Average Men Women Composite Capitation Rate In Table 10, the lowest tariff of services for the driver license test multiplied by the projected 2018 utilization developed in Phase 1 yielded the minimum capitation rate. Likewise, to develop the maximum capitation rate in Table 10, the 2018 utilization developed in Phase 1 was multiplied by the highest 20 SENSITIVITY ANALYSIS

23 tariff of services for the driver s license. Developing the average capitation rate involved multiplying the 2018 utilization developed in Phase 1 by the average of all the tariffs for obtaining the driver s license across all the facilities that provided data related to the cost of services. As can be seen in Table 10, the lower end of the capitation rate using driver s license fees is UAH 417. Capitation rates can be set using the cost structure described above to reduce the incidence and amount of the informal payments paid by patients receiving PHC services. SENSITIVITY ANALYSIS 21

24 CERTIFICATION

25 6. CERTIFICATION This report presents the 2018 calendar year capitation rate projections for primary health care services to be delivered in Ukraine under the capitation model. In our opinion, this report is complete and accurate and represents fairly the actuarial position of the plan for the purposes stated herein. Deloitte Consulting LLP has relied on the Kiev School of Economics for the data collected from Form-20 in developing its calculation of the capitation rate. We have analyzed the data and other information provided for reasonableness, but have not independently audited the data or other information provided. We have no reason to believe the data or other information provided are incomplete and inaccurate, and know of no further information essential to the preparation of the actuarial information contained herein. The assumptions listed in the report are individually and collectively reasonable, as of the date of this report. In our opinion, all factors and assumptions underlying these actuarial computations have been determined on the basis of actuarial assumptions and methods that are each reasonable (or consistent with authoritative guidance) for the purposes described herein, taking into account actual experiences and future expectations, and which, when combined, represent our best estimate of anticipated experiences. The undersigned with actuarial credentials collectively meet the qualification standards of the American Academy of Actuaries to render the actuarial opinions contained herein. Actuaries Non-Actuarial Participants Dr. Sanjit Puri ASA, MAAA, FCA Dr. Lijia Guo, ASA, MAAA Maksym Duda, PhD Nataliia Kovalenko Palak Jhaveri CERTIFICATION 23

26 NOTES

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