Health Sector PERs: Ukraine Case Study

Health Sector PERs: Ukraine Case Study Fiscal, Efficiency, and Equity Issues in the Health Sector Adam Leive Human Development Network

Outline • Objectives of PER • Health outcomes and demographics • Description of health financing and delivery system • Background on intergovernmental fiscal transfers and Ministry of Health norms in Ukraine • Analysis of efficiency and equity of health sector • Conclusions and Recommendations

Core Objectives – Fiscal Issues and Health Sector • Examine the intergovernmental fiscal and administrative issues affecting efficiency and equity of public spending in the health sector. • Identify other social/economic trends affecting the efficiency of public spending in the sector • Link efficiency issues to quality of service delivered, performance, and equity. • To provide recommendations to improve the financing, efficiency, and equity of spending of the health sector.

Health Outcomes – Low Life Expectancy • Life expectancy has not rebounded as quickly as other ECA countries and was higher 40 years ago

Health Outcomes – Differences in Life Expectancy Between Males and Females • Life expectancy is 12 years higher for females than for males, on average

Demographics – Aging Population Structure Source: World Bank staff calculations

International Comparisons of Total and Public Health Spending

Public Health Expenditure by Economic and Functional Classification • Wages and other current expenditures constitute the majority of public health spending (usually over 90%) • Hospitals capture a large share of spending while polyclinics and preventive facilities receive less

Health Care Delivery • The Ukrainian health system has evolved from a centrally-planned Soviet-style model to one where responsibilities have been delegated to lower tiers of government • Most facilities are owned and managed at the regional (oblast) and sub-regional (rayon) levels • There is negligible participation by the private sector in service delivery • Ukraine has slightly fewer physicians per 100,000 (301) than in EU (348) and CIS (378) • However, it has about the same hospital beds per 100,000 (872) as CIS (866) and more than EU (591) • Ukraine also has a similar level of hospitals per 100,000 (5.6) as CIS (5.9) but more than EU (3.1)

Outline • Objectives of PER • Health outcomes and demographics • Description of health financing and delivery system • Background on intergovernmental fiscal transfers and Ministry of Health norms in Ukraine • Analysis of efficiency and equity of health sector • Discussion of Policy Reforms and Recommendations

Intergovernmental Fiscal Issues • The budget reform in 2001 radically changed the intergovernmental finances in the country • Funding from the central government for health is part of a horizontal equalization formula • In Ukraine the most basic shape of the equalization formula for the allocation to a local government is the following: where Ti is the transfer to local government i; Viare the estimated expenditure needs for local government i and Di its estimated revenue capacity • The estimated expenditure needs for health are calculated based on population size

Ministry of Health (MoH) “Norms” • Despite decentralization in service delivery there remains extensive regulation by the center • The MoH norms (mandatory guidelines) dictate to all health facilities across the country how they should allocate resources (particularly staffing, but also others) based mostly on bed size or another indicator • Facilities prepare budgets fulfilling the “norms”, which leads to current spending over 90% of total spending in most cases and little autonomy is left to local governments Examples of Norms for the functioning of health facilities 1 Infection disease doctor per 25 beds in outpatient aid in Rayon hospitals; 1 Surgeon per 20 beds (adults) and 15 beds (children) in Rayon hospitals 1 Nurse (gynecology) per 25 beds in Rayon hospitals; 1 Nurse per 20 beds in Children’s hospitals 1 Dietarian nurse per 500 portions served a day 1 Obstetrician-gynecologist per 20 beds in Rayon hospitals; 1 post per 15 beds in District hospitals; 0.5 Statisticians per 20 posts of doctors in polyclinics 1 Cook per 30 beds in a health facility 1 Cleaner per 500 square meters (0.5 per each 250 square meters) Source: Extracted from Order No. 33 Ministry of Health

The Inefficient Process of Budgeting by Input Norms Dictated from the Fiscal Framework Ministry of Finance Ministry of Health Budget Allocation Through: (1) Shared Revenues (PIT, Land) (2) Equalization Transfer (population- based for health) (3) Local Taxes Input Norms to form health facility budgets, which constrain budget flexibility (Order No 33) Mismatch Local Budget Submission complying with Norms Rayon Health Facility Budget Formation: Prepare budgets fulfilling the “norms”, which lead to extremely high current spending “Norms” fulfillment + Large network of facilities = non-flexible local budgetscrowded by high recurrent spending little spending autonomy is left to local governments.

The Effect of Intergovernmental Fiscal Transfers on Public Health Expenditure • In 2001, public health spending varied across oblasts and was correlated with income • Despite a major change in the way transfers are allocated, differences in absolute public health spending per capita across oblasts remain

Labor vs. Capital Across Oblasts • Personnel highly correlated with public health exp. across oblasts while there is little correlation with public expenditure and the number of health facilities • This is most likely due to rigidities created by health norms which specify strict requirements for staffing levels and must be followed in order to receive budgetary allocations at the oblast and rayon levels

Variation in Average Length of Stay (ALOS) Across Oblasts • ALOS in Ukraine was about 15 days, higher than the CIS average of 13 days and EU average of 9. • ALOS also varies widely across oblasts (see Annex 2 for probit regression) • ALOS is higher in regions with lower inpatient utilization rates and may be compensating to maintain budgetary allocations

Budget Rigidities and Inefficiencies Translate into Inequities - OOP Expenditure Across Oblasts • Insufficient public financing translates into the need for households to purchase drugs outside the health facility and into high rates of informal payments at health facilities • Extent of OOP expenditure is large, although estimates vary (40% to 60% of total health expenditure depending on source) • As a share of total income, OOP expenditures range widely across oblasts • Drugs constitute the largest share of OOP spending and households finance 99.9% of total drug costs in Ukraine (National Health Accounts) Source: Authors’ calculations from World Bank Health and Education Survey 2004

High Levels of Catastrophic OOP Spending • Compared to other ECA countries, Ukraine has the highest proportion of households facing catastrophic OOP spending (OOP spending greater than 40% non-subsistence spending. See Annex 3 for methodology) • 10 % of households face catastrophic spending due to drug spending alone, suggesting little financial protection

Outpatient Facility Use by Type of Facility and Economic Status • The poorest quintile is significantly less likely to use outpatient services • Public polyclinics and rayon hospitals constitute the largest share of outpatient visits and these are used most by the three richest quintiles

Inpatient Facility Use by Type of Facility and Economic Status • The richest and poorest quintile use inpatient services the least and their utilization rates are statistically significantly less than the median and 2nd richest quintiles • Across income quintiles, the majority of inpatient visits occur at rayon hospitals

Conclusions and Policy Recommendations of PER • Reformulate the MoH norms to eliminate the rigidities preventing efficient resource use (necessary condition but it may not be sufficient) • Attempt to reduce underlying inefficiencies through: • Basing transfers on outputs rather than inputs • Gearing regulations more towards accreditation of facilities rather than towards the control of their production function • Purchase drugs, especially for the poor, if physician expenditures can be reduced by reforming the norms • Target expenditures on facilities that poor prefer to use (public polyclinics and rayon/town hospitals) • Provide incentives to shift a larger proportion of doctors into primary care • Regarding HIV/AIDS and TB, improve training of public health professionals and improve coordination between levels of government • In the medium to long term, introduce prospective payment into provider reimbursement at the level of the hospital or the individual provider

Annex 1 - Probit regression of a higher length of stay than average Variable Coefficient Z-statistic Economic Region Eastern 0.252 (1.94) Donetsk 0.241 (1.79) Prechornomorsk 0.291 (2.36)* Podilia 0.109 (0.87) Central 0.074 (0.55) Predniprovsk 0.235 (1.71) Carpathian 0.014 (0.11) 1,688 Observations * = significant at 5% Other control variables: age, sex, body mass index, marital status, income, education level, inpatient facility type, and geographical location of facility

Probit regression of a higher length of stay than average • Variable Coefficient Z-statistic • Economic Region • Eastern 0.252 (1.94) • Donetsk 0.241 (1.79) • Prechornomorsk 0.291 (2.36)* • Podilia 0.109 (0.87) • Central 0.074 (0.55) • Predniprovsk 0.235 (1.71) • Carpathian 0.014 (0.11) • Socioeconomic and demographic characteristics • 19 < age < 30 0.291 (1.59) • 30 < age < 40 0.217 (1.02) • 40 < age < 50 0.549 (2.60)** • 50 < age < 60 0.586 (2.73)** • 60 < age 0.571 (2.79)** • High education 0.107 (1.38) • No education -0.016 (0.09) • 2nd Income quintile -0.131 (1.13) • 3rd Income quintile -0.267 (2.23)* • 4th Income quintile -0.185 (1.54) • 5th Income quintile -0.289 (2.09)* • Male 0.141 (1.96) • Body mass index 155.31 (1.81) • Single 0.083 (0.57) • Divorced -0.103 (0.74) • Widow 0.074 (0.71) • Location and type of inpatient facility • Oblast hosp. 0.305 (1.61) • Rayon hosp. -0.027 (0.15) • Maternity hosp. -0.595 (2.11)* • Sanatorium 1.649 (6.29)** • Other hosp. 0.950 (3.99)** • Clinic 0.573 (2.64)** • City 0.056 (0.60) • Town 0.022 (0.25) • Constant -1.537 (4.70)** Observations 1,688 * significant at 5%; ** significant at 1% Ramsey’s RESET Test: p-value = 0.09 Reference individual is female, under 19, lives in a rural area of the Polisya economic region, married, lowest income quintile, has secondary education, and was hospitalized in a village hospital.

Annex 2 – Definition of Catastrophic Spending Catastrophic out-of-pocket (OOP) spending is defined for a household h if OOP spending exceeds 40% of the household’s capacity to pay. The definition of capacity to pay is constructed in the following way and closely follows Xu, K. (2005). "Distribution of health payments and catastrophic expenditures: Methodology." World Health Organization Health Systems Financing Discussion Paper, Number 2. First, the food expenditure share of total household expenditure is constructed by dividing the household's food expenditure by its total expenditure. FOODEXPh = The household equivalence scale is used instead of the actual household size to account for economies of scale in household consumption. The equivalence scale is defined as: EQSIZEh=HHSIZEhB Previous research from household surveys in 59 countries indicates that the B = 0.56 (Xu et al. 2003). The equivalised food expenditure share is generated by dividing food expenditure by the equivalent household size. EQFOODh = The poverty line is defined as the food expenditure of the household with food expenditure share of total household expenditure at the 50th percentile in the country. Average food expenditure of the households with food expenditure shares between the 45th and 55th percentiles of the total sample are used to minimize measurement error. The percentiles consider the household weighting variable of the survey. PL = The subsistence expenditure of each household is then calculated as the poverty line multiplied by the equivalent household size of each household. SEh = PLXEQSIZEh Capacity to pay is defined as a household's non-subsistence spending. Additionally, for those households reporting food expenditure lower than the level of subsistence spending, non-food expenditure is used as capacity to pay. CTPh = TEXPh - SEh if SEh ≤ FOODh CTPh = TEXPh - FOODh if SEh > FOODh

Annex 3 - Decomposition of the Redistributive Effect (RE) of OOP health payments • In addition to access, the effect of financing source on the income distribution is an important element of equity. The RE, which is the difference between pre- and post-payment Gini coefficients, is affected by: 1) degree of progressivity 2) OOP share of income 3) degree of horizontal inequity 4) reranking due to payment. Possible to think broadly of two components of RE: vertical (V) and horizontal (H+R), where V is a combination of 1) and 2) and (H+R) is the sum of 3) and 4). GXis the Gini coefficient before payment, GX-P is the Gini coefficient after payment,GF(x) is the Gini coefficient for post-payment income for households with pre-payment income x, αx are weights equal to the product of the population share squared and the post-tax income share of households with income x, GB is the Gini coefficient of the income distribution after payment that would exist if all members of each equal pre-payment income group paid the same amount, and CX-P is the concentration index after payment that is obtained by ranking households first by their income before payment and then within each group of pre-payment equals according to their income after payment References: Aronson, J.R. and P. Lambert. (1994). “Decomposing the Gini Coefficient to Reveal the Vertical, Horizontal, and Reranking Effects of Income Taxation.” National Tax Journal. 47(2): 273-94. van Doorslaer, E. et al. (1999). “The redistributive effect of health care finance in twelve OECD countries.” Journal of Health Economics, 18: 291-313.

Annex 3 - OOP Payments are Regressive in Ukraine • The redistributive effect, which is the difference between pre- and post-payment Gini coefficients, indicates the effect of a financing source on the income distribution • Compared to other ECA countries, OOP payments are most regressive (RE is lowest) in Ukraine • Increase in income inequality is also greater than of that of direct payments in 10 of 12 OECD countries estimated in van Doorslaer et al (1999).

Annex 3 - Decomposing the Redistributive Effect: Horizontal Effect Dominates Vertical Effect • RE is affected by: 1) degree of progressivity 2) OOP share of income 3) degree of horizontal inequity 4) reranking due to payment (See Annex 4) • Differential treatment of households of similar income levels is very large in Ukraine • While possibly due to random nature of illness, most likely greater indicator of regional differences in OOP payment rates and variation in informal payments across facilities, regions, and type of care

Health Sector PERs: Ukraine Case Study