Efficient Management of Health Centres Human Resources in Zambia

7 downloads 0 Views 242KB Size Report
Nov 3, 2006 - About 83% of the 40 health centres were technically inef- ficient; and 88% of them .... the remaining human resources are utilized optimally; and.
J Med Syst (2006) 30:473–481 DOI 10.1007/s10916-006-9032-1

ORIGINAL PAPER

Efficient Management of Health Centres Human Resources in Zambia Felix Masiye · Joses M. Kirigia · Ali Emrouznejad · Luis G. Sambo · Abdou Mounkaila · Davis Chimfwembe · David Okello

Received: 24 January 2006 / Accepted: 14 June 2006 / Published online: 3 November 2006 C Springer Science+Business Media, Inc. 2006 

Abstract This study uses Data Envelopment Analysis (DEA) to estimate the degree of technical, allocative and cost efficiency in individual public and private health centres in Zambia; and to identify the relative inefficiencies in the use of various inputs among individual health centers. About 83% of the 40 health centres were technically inefficient; and 88% of them were both allocatively and cost inefficient. The privately owned health centers were found to be more efficient than public facilities. Keywords Health centers mangement . Data envelopment analysis . Technical efficiency . Zambia health F. Masiye Department of Economics, University of Zambia, Zambia J. M. Kirigia · L. G. Sambo World Health Organization, Regional Office for Africa, Brazzaville, Congo A. Emrouznejad () Operations & Information Management, Aston Business School, Auston University, Birmingham B4 7ET, UK e-mail: [email protected] A. Mounkaila Epidemiologist, JSI/MEASURE/Evalution, Arlington, VA 22209, USA D. Chimfwembe Planning and Policy Development, Ministry of Health, Zambia D. Okello World Health Organization, Mbabane, Swaziland

Introduction The World Health Report (WHR) 2000 [1] and the Bulletin of the World Health Organization [2] were devoted to the development and application of a framework for assessing the performance of health systems. The framework includes measurement of three goals of health systems (health, responsiveness and fairness in financial contribution), and an exposition four functions of health systems (including financing, provision, stewardship and resource generation) [3]. Although the framework and the assessments were heavily criticized on methodological and data grounds [4–7], the WHR heated debate which propelled health systems higher on the priority list of various health development partners. Today there is a near universal acceptance that in the absence of a functional health system, all piece-meal attempts at addressing priority diseases (e.g. HIV/AIDS, malaria, TB, childhood diseases) are likely to have dismal and at most perennial success. In that assessment, Zambia ranked number 182 out of 191 Member States of the WHO [1]. Poor countries like Zambia have been asking: what is the point of comparing themselves with rich countries like France, USA, UK, Japan, etc? What is the point of comparing themselves against middle-income countries like South Africa, Botswana, Mexico, etc? Even if Zambia is performing, relatively worse-off compared to other low-income countries, so what? How can Zambia improve the performance of its health system? How can Zambia measure and improve the performance of its individual health facilities (including hospitals and health centres), which absorb the majority of recurrent and capital budget of the Ministry of Health? The specific objectives of this study are to: (i) to estimate the degree of technical, allocative and cost efficiency in Springer

474

individual public and private health centres; and (ii) to identify the relative inefficiencies in the use of various inputs among individual health centers.

Overview of Zambian macroeconomic and health-related situation Socio-economic situation Zambia is a land-locked, low-income and highly indebted country located in Southern Africa with a population of 10.4 million people (and growing at annual rate of 2.9%). The economy is largely based on copper mining and agriculture. The collapse of world copper prices in 1970s had devastating effects on the economy. About 86% of the Zambian population live below the national poverty line. 63.6% live below the income poverty line of US$1 (in 1993 purchasing power parity) per day. The adult illiteracy rate is 21.9%. 36% and 22% of the population do not have access to safe drinking water and adequate sanitation facilities respectively [8]. Health delivery system Zambia has about 655 doctors, 1365 clinical officers and 10378 nurses working in 84 hospitals and 1084 health centres [9]. The vision of government has been to provide all Zambians with equitable access to cost effective quality health care. To realize this vision, government embarked on health sector reform in 1992, whose main thrust was to decentralize the planning, management and decision-making of health services to the health boards and restructuring of health delivery systems. The process of restructuring culminated in the formation of 1 Central Board of Health (CBoH), 72 District Health Boards (DHBs) and 20 Hospital Management Boards (HMBs). The Central Ministry of Health is responsible for policy and strategic directions, while the CBoH is in charge of interpretation and implementation of health policies, and overall technical management of health services. The district health system and hospitals are run by the DHBs and HMBs. Inspite of the various forms of health sector reforms that have been introduced, approximately 36% of the population does not have access to essential medicines; 64% of infants do not use oral rehydration; 75% of the eligible population do not use contraceptives; 54% of births are not attended by skilled health attendants; and there a only 7 physicians per 100,000 people [8].

J Med Syst (2006) 30:473–481

Mortality Ratio (MMR) was recorded at 650 per 100,000 live births [11]. Under 5 mortality rate was 202 per 1000 [11]. 25% of the children under 5 years of age are underweight [9]. Infant mortality rate was 112 per 1000 live births [10]. The probability at birth of surviving to age 40 was 53.6% in 2001 [8]. Those dismal health indicators have largely been attributed to widespread poverty, which has been widened and deepened by the growing incidence of HIV. Faced with such wide-spread poverty, the only sustainable option the country has is to improve quality and coverage of health services through enhanced efficiency in the use of health sector resources at all levels of the national health system.

Rationale for focusing on health centres This study focuses on health centres for a number of reasons: (i) they are a critical part of primary health care system; (ii) they are a vital part of the so-called “close-to-client” (CTC) health service delivery system (or district health system), and thus, important to efforts of scaling-up of pro-poor package of cost-effective interventions to meet the Millennium International Development [12], Health-for-All in the 21st Century [13], the Commission on Macroeconomics and Health [14], and the New Partnership for Africas Development [15] health goals; (iii) they serve majority of the rural people (especially the poor) who constitute about 80% of the total population in Zambia (and indeed, in majority of other countries in the Region); (iv) inefficiency among health centres (and all other facilities for that matter) is unethical and immoral [16, 17] because it implies lost opportunities of improving extra persons health status at no additional cost; (v) CBoH and DHMTs can evaluate the effect of health sector reforms by scrutinizing periodic changes in efficiency scores of both public and private health centres; (vi) the Zambian hospital efficiency study [9] identified the need for replicating the study among health centres; (vii) the growing problem of brain-drain in the country (and indeed in the African Region), calls for urgent measures of ensuring that the remaining human resources are utilized optimally; and (viii) monitoring of efficiency of health centres is part-andparcel of the broader stewardship role of the State (through Ministry of Health) [18], especially ensuring that the benefits from health sector investments (by Governments and partners) are optimized.

Previous research/studies Health profile Zambian health indicators are generally very poor. Average life expectancy at birth is 41.4 years and the Disability Adjusted Life Expectancy (DALE) is 30.3 years [10]. Maternal Springer

This section does not aim at providing a comprehensive review of the health-related DEA literature. Instead, it attempts to review just a few recent health-related DEA applications to underscore the growing and fruitful use of DEA approaches

J Med Syst (2006) 30:473–481

in shedding light on efficiency of various aspects of health systems. USA Chattopadhy and Ray [19] applied DEA to examine the levels of technical, scale, and size efficiency of 140 nursing homes providing health care to the elderly in Connecticut, USA. Shroff [20] utilized DEA to estimate the relative siting efficiency of 26 potential sites for a long-term health care facility in the Northern Virginia region of USA. UK Hollingsworth and Parkin [21] used DEA to analyse data of neonatal services for a sample of 49 units in the United Kingdom, to determine technical efficiency, economies of scale and potential cost savings if the units were to operate efficiently. Jacobs [22] uses 232 UK Department NHS hospitals (Trusts) dataset to compare the efficiency rankings of three cost indices (the CCI, 2CCI and 3CCI) with those obtained using Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). Turkey Ersoy [23] used DEA to find out the portion of a sample of 573 acute general hospitals that were operating efficiently and the inputs (outputs) that contribute most to inefficiency. Finland Linna, Nordblad and Koivu [24] used a two stage procedure to estimate and explain technical efficiency (TE) and cost efficiency (CE) of oral care provision in Finnish health centres. Taiwan Chang [25] used DEA to evaluate relative technical efficiency of six central government-owned hospitals in Taiwan utilizing a panel data for five years. Wan et al. [26] explored the technical efficiency of nursing productivity and patient care costs among 57 nursing units, in a tertiary care medical centre in Taiwan (Republic of China), using a variable return to scale DEA model. South Africa Kirigia, Sambo and Lambo [27] employed DEA methodology to identify and measure technical and scale efficiencies among 55 public hospitals in South Africa. Kirigia, Sambo and Scheel [28] utilized DEA to investigate the tech-

475

nical inefficiencies among 155 primary health care clinics in Kwazulu-Natal Province of South Africa. Zere, McIntryre and Addison [29] used DEA to examine technical and scale efficiency of a sample of 86 non-academic acute hospitals in the Eastern, Northern and Western Cape Provinces of South Africa. Kenya Kirigia, Emrouznejad and Sambo [30] measured relative efficiency of 54 public hospitals in Kenya using input oriented DEA technique. 26% of the hospitals analyzed were had some technical inefficiencies. The authors found that on average, the inefficient hospitals could reduced their utilization of all inputs by about 16% without reducing outputs. About 30% of the hospitals were scale inefficient. The average scale efficiency score in the whole sample was 90% implying that total output could be increased by 10% at the current input levels. The study also estimated the input reductions and/or output increases needed to make the individual inefficient public hospitals efficient. Zambia Masiye et al. [9] used DEA to measure the degree of technical and allocative efficiency of 20 hospitals (including 10 mission, 9 government and private hospital) in Zambia. Two models were estimated. The first model used one input (namely total expenditure) and five outputs comprising of outpatient department (OPD) visits for children aged under five years, OPD visits for children aged over five years, number of bed days for under fives, bed days for over fives and number of deliveries. In model 2, the authors used three inputs consisting of non labor expenditure, number of doctors (including clinical officers) and number of other personnel, and three outputs, namely: total OPD visits (under fives plus over fives), total number of bed days and number of deliveries. In model 2 they included price variables to enable them to compute allocative efficiency scores. underseven inputs (i.e., outpatient casual visits, special care visits, MCH-FP visits, dental care visits, inpatient department general admissions, paediatric ward admissions and maternity ward admissions). In model 1, 75% of the hospitals were technically inefficient, with an average score of 0.441. In model 2, 50% of the hospitals were found to be technically inefficient, with an average score of 0.543. Only 85% of the hospitals were both allocatively and economically (in overall terms) inefficient, with average scores of 0.798 and 0.575 respectively. The latter finding implies that the 17 economically inefficient hospitals could produce their current levels of output with 42.5% less cost. In order to reduce the ineffiencies in hospitals, it will be necessary to reallocate the excess inputs to the primary health care (PHC) facilities, but before such an action it taken Springer

476

there is need to establish the efficiency of individual PHC facilities. This study did not estimate the input reductions and/or output increases needed to make the individual inefficient hospitals efficient. Such information would have been very useful to the policy-makers in designing interventions geared at reducing the inefficient use of individual inputs.

DEA conceptual framework In the production process, health centres turn inputs (factors of production), into outputs (e.g. curative visits, out reach health education visits, MCH visits). Inputs can be divided into the broad categories of labour, materials, and capital, each of which might include more narrow subdivisions. Labour inputs include skilled workers (doctors, clinical officers, nurses, pharmacists, laboratory technologists/technicians, etc) and unskilled workers (cleaners, lawnmowers, mesengers, etc), as well as the entrepreneurial efforts of the health centre managers and district health management teams. Material include pharmaceuticals, non-pharmaceutical supplies, electricity, water and any other goods that the health centres acquires and transforms into a final services. Capital includes buildings, equipment, beds, etc. The relationship between the quantities of inputs and the resulting quantities of outputs is described by a production function (PF). PF describes the maximum output feasible for a given set of inputs and a given level of technology (i.e., a given state of knowledge about the various methods that might be used to transform inputs into outputs). Suppose, for example, the inputs are full time nursing time and full time clinic officers time per year, and that they are used to produce outpatient care, proxied by outpatient visits. That production function can be depicted graphically (see Fig. 1) using an isoquant (IS), i.e. a curve that shows all the possible combinations of inputs that yield the same output. AB is the isocost, i.e. the minimum cost line.

J Med Syst (2006) 30:473–481

Technical efficiency (TE) is about ensuring no resources are wasted, i.e. the maximum amount of output is obtained from the available inputs [31]. Health centers I, Q and S are technically efficient because they are operating on the production function or isoquant or efficiency frontier. Their efficiency score is one (or 100%). Health centers P and T are inefficient because they are using more nurses and clinic officers time to produce the same level of output as health centres I, Q and S. The extent of technical inefficiency of health center ‘P’ can be expressed as: [1-(OQ/OP)] [32], which is the amount by which all inputs could be proportionately reduced without a reduction in output. Allocative efficiency (AE) is about using resources to produce outputs with the highest possible value. AE implies the isoquant (IS) and isocost (AB) lines are tangential. Even though health centers I and Q are technically efficient, they are allocatively inefficient. Health center S is both technically and allocatively efficient. Allocative efficiency of facility P = OR/OQ. Economic efficiency (or cost efficiency) (CE) combines both productive efficiency (producing without waste, on the production possibilities frontier) with allocative efficiency (allocating resources to their most highly valued uses) (Skaggs and Carlson, 1996). Cost efficiency of facility P = OR/OP = (OQ/OP) × (OR/OQ) = TE × AE. The input-orientated frontier production function technical efficiency measurement approach used in this study is that proposed in Farrell [33] and generalized through the use of mathematical programming [34]. For other nonparametric DEA models see Emrouznejad A. (2003) and Emrouznejad A and E. Thanassoulis. (2004). See also Emrouznejad A. (2004) for method of calculation. Technical efficiency DEA measures the technical efficiency of health center k compared with n peer group of health centers as follows: Objective Function: s 

Max E k =

r =1 m 

ur yr k vi xik

i=1

Fig. 1 Health centres technical and allocative efficiencies

Springer

where: Ek = efficiency score of kth health centre, yrj = observed amount of rth output produced by the jth health centre, xij = quantity of ith input used by the jth health centre, ur = the weight given to output r by the DEA programme, vi = the weight given to ith input by the DEA programme, n = the number of health centers, m = the number of inputs used by health center k, s = the number of outputs produced by health centre k and k is the health center being assessed in the set of j = 1, . . . , n health centres.

J Med Syst (2006) 30:473–481

477

where: m = the number of inputs used health centre k. The above constraint implies that TE of health centre k is maximized subject to efficiency of all health centres being less than or equal to one. In other words, the relative efficiency of all health centres is constrained between 1 (relatively efficient) and less than 1 (relatively inefficient). The u and v are variables of the problem and are constrained to be greater than or equal to some small positive quantity 0 in order to avoid any input or output being totally ignored in determining the efficiency [35].

by the j0 th health center; (3) ensures that the frontier health center enjoys no more of a favorable situation than does the j0 th health center; (4) assumes constant or increasing returns to scale prevail; (5) the objective function and (2) determine the most cost effective use of each of the controllable resources so as to meet the specified output (Y1, j0 ,  Mvector  Pi j0 Z i∗ . Y2, j0 , . . . , YR, J0 ), at minimum total cost, i=1 The goal of the current study is to demonstrate to policymakers, using a sample of Zambian health centres, how data that is routinely collected by a national Health Information System (HIS) can fruitfully be analyzed using DEA method to shed light on the performance (efficiency) of individual health centres (HCs). This approach views HCs as productive units which use multiple inputs to produce one or more outputs. The approach yields measures of HCs relative efficiency by deriving an efficiency frontier (a production function) and measuring the distance of HCs to the frontier to get their efficiency scores. HCs on the frontier get an efficiency score of one (100%) and those below the frontier scores less than one (below 100%) depending on how far they are from the frontier.

Allocative efficiency

Results

The formulation for determining the degree of allocative efficiency for the j0 th health center is given by estimating the linear program formulation below [36]:

Due to budgetary constraints, the current study is based on a sample of 40 health centres, i.e. 3.7% of the total. 58% of the health facilities in the sample are government owned, while the remaining 42% are private-for-profit. Health centres provide three key services: outpatient visits, basic medical examinations, maternal health services and out-reach preventive care. Cases that require inpatient care are referred to the hospitals. Health centres have a laboratory, pharmacy, consultation room and a Maternal and Child Health department. Technical efficiency requires data on quantities of inputs and outputs of health centres. Ultimate health care outcome measures (e.g. number of lives saved or extended, quality adjusted life years gained, health adjusted life years gained, disability adjusted life years saved) are not routinely compiled by either the health centers or the National Health Information System. In fact, even multiple process count information (e.g. number of curative, preventive and promotive care visits, number health outreach visits) was not readily available in the records. Therefore, in the current study, the health centres were assumed to produce one output (i.e. number of outpatient health care visits) using three inputs, namely, numbers of clinical officers, nurses and support staff. The data were collected from 40 health centres (17 private and 23 public) in three provinces, namely: Lusaka, Central and Copper-belt provinces. The data available centrally at the Central Board of Health (CBOH) was found to be outdated. Thus, data had to be collected from health centres using

subject to constraints: Less-than-unity constraint: s  r =1 m 

ur yr j ≤ 1; j = 1, ... , n vi xik

i=1

ur ≥ 0; r = 1, ... , s, vi ≥ 0; i = 1, ... , m,

N 

λ j Yr j ≥ . . . (r = 1, 2, . . . , R)

(1)

  λ j X i j − Z i = 0 . . . i = 1, 2, . . . , M 

(2)

  λ j X i j ≤ X i j0 . . . i = M  + 1, . . . , M 

(3)

λj ≥ 1

(4)

j=1 N  j=1 N  j=1 N  j=1

Min

M 

Pi j0 Z i

(5)

i=1 

M  i=1



Pi j0 Z i∗

=

M 

Pi j0 X i j0

(6)

i=1

Constraint: (1) insures the composite frontier health center equals or exceeds the level of each output actually obtained

Springer

478

J Med Syst (2006) 30:473–481

Table 1 Descriptive statistics (mean and standard deviation) of efficiency scores Type of efficiency

Privately owned Standard Mean deviation

Government owned Standard Mean deviation

Technical efficiency Allocative efficiency Cost efficiency

0.70 0.84 0.59

0.56 0.57 0.33

0.25 0.15 0.27

0.21 0.25 0.25

Thirteen (77%) of the 17 privately owned health centres were allocatively inefficient; none of them had an AE score of less than 0.50. The allocative inefficiency ranged from 0.56 to 0.97. Contrastingly, 22 (96%) of the 23 government owned health centres were found to be allocatively inefficient; 59% of them had an AE score of less than 0.50. The allocative inefficiency varied from 0.27 to 0.98. Thirteen (77%) of the 17 privately owned health centres were cost inefficient; 62% of them had a CE score of less than 0.50. The cost inefficiency ranged from 0.11 to 0.75. On the contrary, 22 (96%) of the 23 government owned health centres were cost inefficient; 91% of them had a CE score of less than 0.50. The cost inefficiency varied from 0.12 to 0.89. The inefficient health centres should be able to produce their current level of services (outputs) with fewer inputs and, therefore, at lower cost.

questionnaires prepared for the purpose. The questionnaires were administered by two of the authors (FM and DC). It is important to note that the remunerations in the private sector are more than twice those of the public sector. We used number of clinical officers, number of nurses, number of other staff as input variables and number of visits as output. Outputs such as outreach services, immunization, and other forms of services have not been included seperately in the Discussions model for two reasons. First the number of visits is good proxy for all typoes of outputs, seconly disagreegatde data The current human resource endowment among the private is not avilable for all helath centers. health centers sub-sample is 43 clinical officers, 43 nurses The main limitations of the current study have to do with: and 37 other staff (mainly casual). They can produce their lack of quality-adjusted output measures; lack of outpatient current level of output (a total of 107,477 outpatient visvisits data broken down by disease to give an indication of its) with 17 clinical officers, 22 nurses and 23 other staff. case mix differences (if any); and lack of data on non-labor This implies that the private health centers could easily inputs Table 1. reduce the number of clinical officers by 60%, nurses by Health Centres technical efficiency (TE), allocative effi49% and other staff by 38% while maintaining the curciency (AE) and cost efficiency (CE) scores are summarized rent level of health care production. The private proprietors in Table 2. Efficiency scores for individual health facilities stand to save a total of 27.7 million Kwacha per month can be found in Appendix I. The average TE for the whole by laying off excess clinic officers, nurses and other staff. sample was 0.619, AE was 0.685 and CE was 0.445. Of course the net saving would be equal to the above estiThe TE, AE and CE scores are constrained between 1 mate minus the affected staff’s compensation for premature (totally efficient) and 0 (totally inefficient). All those health termination of their employment contract plus any legal centers with an efficiency rating of less than one are ineffifees. cient. What do the average efficiency scores in Table 2 mean? The private owners of health centers with excess staff have For example, the TE of privately owned is 0.70; this means three options: that those health centres (compared with their peers) on average should be able to produce their actual output level using (i) to do nothing (which would amount to continuing the 30% [(1.00–0.70) × 100] less of each input. current inefficiencies). As evidenced in Table 2, the privately owned health cen- (ii) To terminate contracts of the excess staff. ters are more technically, allocatively and cost efficient than (iii) To negotiate with owners of proprietors with human rethose owned by the government. source deficit to take over the excess staff’s contract. Table 3 presents the distribution of health centres across It is important to note that not all inefficient private health the various efficiency brackets. 12 (71%) of the 17 privately centers have got excess inputs. For example, the “Primary owned health centres were technically inefficient; 17% of Care Services” health center has a deficit of 6.8 nurses and them had a TE score of less than 0.50. There was wide a “Lime Company Clinic” 0.4 nurses. Bank of Zambia Clinic, variation in TE of the inefficient health centres, ranging from Mufulira Clinic 6, ZESCO Clinic and Ajali Clinic together 0.11 to 0.75. Comparatively, 22 (96%) of the 23 government have a deficit of 4 other staff. The proprietors of those two owned health centres were found to be technically inefficient; health centers have three options: 36% of them had a TE score of less than 0.50. There was wide variation in the TE scores among the inefficient health (i) to do nothing (which would amount to continuing the centres, ranging from 0.26 to 0.93. current inefficiencies). Springer

J Med Syst (2006) 30:473–481 Table 2

479

Health centers efficiency scores

Health facilities Bank of Zambia Clinic Mufulira Clinic 6 Mufulira Clinic 2 Chilanga Cement Clinic North Breweries Clinic Home Clinic ZESCO Clinic ZESCO Clinic 2 TDRC ZNPF Clinic Zam. Sugar Co. Clinic Primary Care Services Lever Brothers Lime Company Clinic Ajali Clinic ZAMSEED Staff Clinic Med. Health Centre Clinic Chibuluma Government Ngungu Kalulushi Main Mean

Technical efficiency

Allocative efficiency

Cost efficiency

Health facilities

0.536 0.7 0.525 0.75 1 1 0.618 0.437 0.111 0.6 0.5 1 0.757 0.572 0.75 1 1 0.5 0.5 0.375

0.899 0.944 0.563 0.587 1 1 0.658 0.774 0.971 0.877 0.894 0.748 0.784 0.797 0.715 1 1 0.37 0.33 0.367

0.482 0.661 0.296 0.44 1 1 0.406 0.339 0.107 0.526 0.447 0.748 0.594 0.456 0.537 1 1 0.185 0.165 0.138

Mandevu Clinic Kalulushi Government Ndola Clinic Limited Kansuswa Ndeke Buchi Main Kamuchanga Bwacha Chowa Mukobeko Nakoli Butondo Chibolya Chimwemwe Bulangililo Kawama Kwacha Luangwa Mindolo Ipusukilo

Technical efficiency

Allocative efficiency

Cost efficiency

0.477 0.26 0.928 0.632 0.333 0.338 0.6 0.429 0.844 1 0.75 0.429 0.5 0.378 0.5 0.503 0.545 0.6 0.5 1 0.619

0.904 0.753 0.91 0.467 0.679 0.955 0.27 0.283 0.556 0.893 0.373 0.373 0.447 0.978 0.379 0.496 0.663 0.411 0.319 1 0.685

0.431 0.196 0.844 0.295 0.226 0.323 0.162 0.121 0.469 0.893 0.28 0.16 0.224 0.37 0.19 0.25 0.362 0.247 0.16 1 0.443

Note: (1) Allocative efficiency = (cost efficiency)/(technical efficiency). (2) Scale assumption: Variable Returns to Scale.

(ii) to negotiate with owners of health centers with excess nurses to have a transfer of staff (and their contracts), i.e. if the concerned staff are willing. This option would save all parties concerned some money. The transferring employer may avoid paying the compensation package for terminating contracts of concerned staff. The receiving company would save on recruitment and training costs. And the affected members of staff would retain their contracts, and thus, be saved from income loss and the attendant psychological costs. (iii) to recruit from the open market – which may not be feasible in the short-term. Even if there is enough resources, there is no unemployment of nurses in the country. In long-term one possiblity is to train more nurses in Zambia. On the other hand, the current human resource endowment among the public health centers sub-sample is 29 clinical officers, 282 nurses and 92 other staff. They can produce their current level of output (a total of 253,204 outpatient visits) with 23 clinical officers, 80 nurses and 54 other staff. This mean that the public health centers could easily reduce the number of clinical officers by 21%, nurses by 80% and other staff by 54% while maintaining the current level of health service provision. The Ministry of Health (MoH) could save a total of 51.6 million Kwacha per month by laying off excess clinic officers, nurses and other staff.

Regarding public health centers with excess human resources, the MoH has the following policy options: (i) to do nothing (which would amount to continuing the current inefficiencies). (ii) to transfer the excess clinical officers to public health centers (e.g. Mukobeko and Nakoli) and hospitals, and charitable mission hospitals and health centers which have a deficit. The MoH should opt for early retirement of technical staff only when staffing deficits among the public and charitable health facilities have been bridged. Any efficiency savings should be used to improve the remunerations of the remaining staff to reduce the rate of internal brain-drain to the private-for-profit sub-sector, and external brain-drain to the relatively affluent African countries and developed countries. Conclusions While DEA has been extensively used in the United States [19, 20, 37–45], Western Europe [21–24] and Asia [25, 26] to study efficiency of health facilities, very few frontier analyses have been conducted in Sub-Saharan Africa, yet we believe this is where they are needed most due to the severe budgetary constraints. Kirigia, Sambo and Lambo [27] employed DEA methodology to identify and measure technical and scale Springer

480

J Med Syst (2006) 30:473–481

Table 3 Frequency distribution of health centres efficiency by ownership Range

Private health centres: Frequency (%) TE AE CE

Government health centres: frequency (%) TE AE CE

1 0.90–0.99 0.80–0.89 0.70–0.79 0.60–0.69 0.50–0.59 0.40–0.49 0.30–0.39 0.20–0.29 0.10–0.19 Total

5 (29) 0 (0) 1 (6) 3 (18) 2 (12) 4 (24) 1 (6) 0 (0) 0 (0) 1 (6) 17 (100)

2 (9) 1 (4) 1 (4) 1 (4) 3 (13) 7 (30) 3 (13) 4 (17) 1 (4) 0 (0) 23 (100)

efficiencies among 55 public hospitals in South Africa. Forty per cent of hospitals were found to be technically inefficienct. Kirigia, Sambo and Scheel [28] utilized DEA to investigate the technical inefficiencies among 155 primary health care clinics in Kwazulu-Natal Province of South Africa. Seventy percent of the clinics were technically inefficient. Zere et al. [29] used DEA to examine technical and scale efficiency of a sample of 86 non-academic acute hospitals in the Eastern, Northern and Western Cape Provinces of South Africa. The average technical efficiency was 70%. It was estimated that if the relatively inefficient hospitals operate as efficiently as their peers, efficiency gains in terms of reduction in recurrent expenditure would be about US$47 million. Kirigia, Emrouznejad and Sambo (2002) measured relative efficiency of 54 public hospitals in Kenya using input oriented DEA technique. 26% of the hospitals analyzed were had some technical inefficiencies. About 30% of the hospitals were scale inefficient. Masiye et al. [9] used DEA to measure the degree of technical and allocative efficiency of 20 hospitals in Zambia. 85% of the hospitals were both allocatively and economically (in overall terms) inefficient. Masiye et al. recommended that in order to reduce the inefficiencies in hospitals, it will be necessary to reallocate the excess inputs to the primary health care (PHC) facilities, but before such an action it taken there is need to establish the efficiency of individual PHC facilities. The current study was partially motivated by the abovementioned recommendation. 71% of the 17 privately owned health centres were technically inefficient. 96% of the 23 government owned health centres were found to be technically inefficient. 77% of the 17 privately owned health centres were allocatively inefficient. 96% of the 23 government owned health centres were found to be allocatively inefficient. 77% of the 17 privately owned health centres were cost inefficient. 96% of the 23 government owned health centres were cost inefficient.

Springer

4 (24) 3 (18) 2 (12) 5 (29) 1 (6) 2 (12) 0 (0) 0 (0) 0 (0) 0 (0) 17 (100)

4 (24) 0 (0) 0 (0) 1 (6) 1 (6) 3 (18) 5 (29) 2 (12 0 (0) 1 (6) 17 (100)

1 (4) 4 (17) 1 (4) 1 (4) 2 (9) 1 (4) 4 (17) 7 (30) 2 (9) 0 (0) 23 (100)

1 (4) 0 (0) 2 (9) 0 (0) 0 (0) 0 (0) 2 (9) 3 (13) 7 (30) 8 (35) 23 (100)

This study has identified input reductions needed to make the individual inefficient health centres efficient. The study has also identified the policy options available to the policymakers as they ponder how to design interventions geared at reducing the inefficient use of individual inputs. We recommend that the current monitoring and evaluation activities going on in the Zambian Ministry of Health and the Central Board of Health should incorporate efficiency measurement and analysis. In addition, it is our hope that the DEA torch shall be taken to all the SSA countries with a view to ensuring that the available resources (and those that will be available in the future) are used to improve access to health care for the greatest number of people possible. Acknowledgments The author would like to express their thanks to anonymous reviewers for their carefully prepared report. All their points have been considered and the papers have been edited accordingly.

References 1. WHO, The World Health Report 2000: Improving Health Systems Peformance, WHO, Geneva, 2000a. 2. WHO, The Bulletin of the World Health Organization, WHO, Geneva, 2000b. 3. Murray, C. J. L., and Frenk, J., A framework for assessing the performance of health systems. Bull. WHO. 78(6):717–731, 2000. 4. Williams, A., Science or Marketing at WHO? A commentary on ‘World Health 2000’. Health Econ. 10(93):93–100, 2000. 5. Wagstaff, A., Measuring equity in health care financing: reflections on and alternatives to the World Health Organizations fairness of financing index. Development Research Group and Human Development Network, World Bank, 2001. 6. Shaw, R. P., World Health Report 2000 “Financial Fairness Indicator”. Useful compass or crystal ball? Int. J. Health Serv. 32(1):195– 203, 2002. 7. Pedersen, K. M., The World Health Report 2000: dialogue of the deaf. Health Econ. 11:93–101, 2002. 8. UNDP, The Human Development Report 2002, Oxford University Press, Oxford, 2002.

J Med Syst (2006) 30:473–481 9. Masiye, F., Ndulo, M., Roos, P., and Odegaard, K., A comparative analysis of hospitals in Zambia: a pilot study on efficiency measurement and monitoring. In: Seshamani, V., Mwikisa, C. N., and Odegaard, K. (eds.), Zambias Health Reforms Selected Papers 1995–2000 Chapter 7, Sweden, Lund, pp. 95–107, 2002. 10. WHO, World Health Report 2001, WHO, Geneva, 2001a. 11. UNDP, The Human Development Report 2001, Oxford University, Oxford, 2001. 12. United Nations, The millenium international development goals, United Nations, New York, 2000. 13. WHO, Regional Office for Africa, Health-for-all policy for the 21st century in the African Region: agenda 2020, WHO, Harare, 2000. 14. WHO, Macroeconomics and Health: Investing in Health for Economic Development, World Health Organization, Geneva, 2001b. 15. New Partnership for Africas Development, Human Development Programme: Health. Republic of South Africa, Pretoria, 2000. 16. Mooney, G. H., Economics, medicine and health care, Harvester Wheatsheaf, New York, 1986. 17. Culyer, A. J., The morality of efficiency in health care: some uncomfortable implications. Health Econ. 1(1):7–18, 1992. 18. Saltman, R. B., and Ferrousier-Davis, O., The concept of stewardship in health policy. Bulletin World Health Organ. 78(6):732–739, 2001. 19. Chattopadhy, S., and Ray, C. S., Technical, Scale, and Size efficiency in Nursing home care: a nonparametric analysis of Connecticut homes. Health Econ. 5:363–373, 1996. 20. Shroff, H. F. E., Gulledge, T. R., Haynes, K. E., and Oneill, M. K., Siting efficiency of long-term health care facilities. Socioecon. Plann. Sci. 32(1):25–43, 1998. 21. Hollingsworth, B., and Parkin, D., The efficiency of the delivery of neonatal care in the UK. J. Public Health Med. 23(1):47–50, 2001. 22. Jacobs, R., Alternative methods to examine hospital efficiency: data envelopment analysis and stochastic frontier analysis. Health Care Manag. Sci. 4:103–115, 2001. 23. Ersoy, K., Kavuncubasi, S., Ozcan, Y. A., and Harris, I. I. J. M., Technical efficiencies of Turkish hospitals: DEA Approach. J. Med. Syst. 21(2):67–74, 1997. 24. Linna, M., Nordblad, and Koivu, M., Technical and cost efficiency of oral health care provision in Finnish health centres. Soc. Sci. Med., 2002. 25. Chang, H., Determinants of hospital efficiency: the case of central government-owned hospitals in Taiwan. Omega Int. J. Manag. Sci. 26(2):307–317, 1998. 26. Wan, T. T. H., Hsu, N., Feng, R., Ma, A., Pan, S., and Chou, M., Technical efficiency of Nursing Units in a tertiary care hospital in Taiwan. J. Med. Sys. 26(1):21–27, 2002. 27. Kirigia, J. M., Lambo, E., and Sambo, L. G., Are public hospitals in Kwazulu-Natal Province of South Africa technically efficient? African J. Health Sci. 7(3–4):25–32, 2000. 28. Kirigia, J. M., Sambo, L. G., and Scheel, H., Technical efficiency of public clinics in Kwazulu-Natal province of South Africa. East African Med. J. 78(3):S1–S13, 2001. 29. Zere, E. A., Addison, T., and McIntyre, D., Hospital efficiency in Sub-Saharan Africa: Evidence from South Africa. South African J. Econ. 2000.

481 30. Kirigia, J. M., Emrouznejad, A., and Sambo, L. G., Measurement of technical efficiency of public hospitals in Kenya: using Data Envelopment Analysis. J. Med. Sys. 26(1):39–45, 2002a. 31. Emrouznejad, A., An alternative DEA measure: A case of OCED countries. Appl. Econ. Lett. 10:779–782, 2003. 32. Emrouznejad, A., Measurement efficiency and productivity in SAS/OR. J. Comput. Oper. Res. 32(7) 1665–1683, July 2005, 2004. 33. Emrouznejad, A., and Thanassoulis, E., A mathematical model for dynamic efficiency using data envelopment analysis. J. Appl. Math. Comput. 160(2):363–378, 14 January, 2005, 2004. 34. Emrouznejad, Ali, and Victor, Podinovski, Data Envelopment Analysis and Performance management, Warwick Print, Coventry, UK, ISBN: 0 90268373 X, 2004. 35. Coelli, T. J., A guide to DEAP Version 2.1: A Data Envelopment Analysis (Computer) Program. Working Paper No. 8/96. Centre for Efficiency and Productivity Analysis (CEPA), Department of Econometrics, University of New England, Armidale, Australia, 1996. 36. Farrell, M. J., The measurement of productive efficiency. J. Royal Stat. Soc. Series A 120(III):253–281, 1957. 37. Charnes, A., Cooper, W. W., and Rhodes, E., Measuring efficiency of decision making units. Eur. J. Oper. Res. 2–6:429–444, 1978. 38. Emrouznejad, A., Ali Emrouznejad’s DEA HomePage, Warwick Business School, Coventry CV4 7AL, UK, 1995–2003. 39. Morey, R. C., Fine, D. J., and Loree, S. W., Comparing the allocative efficiencies of hospitals. OMEGA Int. J. Manag. Sci. 18(1):71– 83, 1990. 40. Ozcan, A., and Luke, R. D., A national study of the efficiency of hospitals in Urban Markets. Health Serv. Res. 27(6):719–739, 1993. 41. Lynch, J. R., and Ozcan, Y., Hospital closure: an efficiency analysis. Hospital Health Serv. Admin. 39(2):205–220, 1994. 42. Ozcan, Y., and Cotter, J. J., As assessment of efficiency of area agencies on aging in Virginia through data envelopment analysis. The Gerontologist 34(3):363–370, 1994. 43. Laura, H. T., Ozcan, Y. A., and Wogan, S. E., Mental health case management and technical efficiency. J. Med. Syst. 19(5):413–423, 1995. 44. Ozcan, Y., Efficiency of hospital service production in local markets: the balance sheet of U.S. medical armament. Socioecon Plann. Sci. 29(2):139–150, 1995. 45. White, K. R., Fache, R. N., and Ozcan, Y., Church ownership and hospital efficiency. Hospital Health Serv. Admin. 41(3):297–310, 1996. 46. Pai, C., Ozcan, Y. A., and Jiang, H. J., Regional variation in physician practice pattern: an examination of technical and cost efficiency for treating sinusitis. J. Med. Sys. 24(2):103–117, 2000. 47. Harris, II J., Ozgen, H., and Ozcan, Y., Do mergers enhance the performance of hospital efficiency? J. Oper. Res. Soc. 51:801–811, 2000. 48. Rollins, J., Lee, K., Xu, Y., and Ozcan, Y., Longitudinal study of health maintenance organization efficiency. Health Serv. Manage. Res. 14:249–262, 2001.

Springer