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Original Article

Evaluation of Technical Efficiency of County Referral Hospitals in and Its Determinants

Gilbert Koome Rithaa, George Kosimbei1, Andrew Yitambe2, Peter Kithuka2 Department of Health Records and Information Management, College of Health Sciences, Mount Kenya University, , Departments of 1Applied Economics and 2Health Management and Informatics, Kenyatta University, , Kenya

Abstract

Background: Kenya’s gross national income per capita of $ 2250 is significantly lower than the global average of $ 6977. In addition, Kenya Government’s health expenditure as a percent of the total government budget is approximately 7% which falls below the target of 15% recommended by the World Health Organization. It is, therefore, important that the country’s health‑care resources, specifically those allocated to the health sector, are optimally used. Methods: An one‑stage data envelopment analysis (DEA) method was used to estimate the technical efficiency of county referral hospitals. A total of 34 county referral hospitals were randomly sampled and studied. Data analysis was performed in two stages as follows: first, input and output data were entered into MS Excel sheet after which DEA version 2.1 was used to determine the technical efficiency scores for the hospitals. In the second stage, interval regression analysis using censored interval regression model was used to identify determinants of technical efficiency for the sampled hospitals. Results: Results indicated that the mean constant return to scale technically efficient score was 82.4%, the mean variable return to scale (VRS) technically efficient score was 94.1%, and the mean scale efficiency technical score was 87.4%. The mean level of VRS technical inefficiency was 17.2%. The total inputs slacks in the inefficient hospitals were 104 beds and 840 staff which represented an input slack of 4% for the beds and 28% for the staff. Conclusions: Inefficient hospitals could have attained efficient frontiers using fewer resources, specifically 4% beds and 28% staff. The technical inefficiencies in county referral hospitals are occasioned by the use of inappropriate production functions characterized by the existence of excess production inputs and suboptimal outputs.

Keywords: County referral hospitals, data envelopment analysis, scale efficiency, technical efficiency

Introduction resources but also their efficient use to improve access and quality of care.[7,8] However, the resources required to meet Kenya is located in the East Coast of Africa. In 2012, Kenya the costs of achieving the development goals are far beyond had a population of approximately 44 million with an average [9] [1] the reach of many. In Kenya and other sub‑Saharan African annual population growth of 2.7%. About 23% of the countries, hospitals absorb the greatest proportion of the total Kenyan population resides in urban areas compared to 51% health expenditure[10] which requires efficient use for maximum [1,2] th for the African region. Kenya is ranked 145 among 186 benefit to the population.[1] countries in the poverty index with almost half (21 million) of the population living below poverty line. In 2012, the country Achieving a high level of technical efficiency in the health had a gross domestic income per capita of $840. The average sector is an important task in the face of significant budgetary [11] total expenditure on health as a percentage of its gross domestic allocation in the public health‑care system. For instance, product (GDP) is approximately $4.7.[3,4] the Kenyan Government health expenditure as a percent of

Tightening budget and increasing pressures on the efficiency Address for correspondence: Mr. Gilbert Koome Rithaa, of public spending represent major challenges for the College of Health Sciences, Mount Kenya University, Thika, Kenya. Kenyan Government.[5,6] The achievement of the sustainable E‑mail: [email protected] development goals and related initiatives as reaffirmed by the UN state members requires not only availability of adequate This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to Access this article online remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. Quick Response Code: Website: For reprints contact: [email protected] www.ijamhrjournal.org How to cite this article: Rithaa GK, Kosimbei G, Yitambe A, Kithuka P. Evaluation of technical efficiency of county referral hospitals in Kenya and DOI: its determinants. Int J Adv Med Health Res 2019;6:24-31. 10.4103/IJAMR.IJAMR_80_18 Received: 10‑11‑2018, Accepted: 18‑04‑2019

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Rithaa, et al.: Evaluation of technical efficiency of count referral hospitals in Kenya and its determinants

the total government budget was 6.5% for the financial year the optimal combination of inputs to achieve a given level of 2010/2011[3,4] which falls below the target of 15% recommended outputs (an input‑oriented model). The input‑oriented model by the Abuja Declaration.[12] Despite Kenyan health sector of technical efficiency measures how many fewer resources budget increasing overtime, there has not been a corresponding a hospital could employ and still produce the same level of significant change in her key health indicators.[12,13,14,7] For output.[26,22] In this context, efficiency is interpreted as a hospital’s instance, comparison from the UNICEF[15] has pointed out resource intensity relative to best practice. This is a better that weak health‑care management systems and poor quality approach for estimating technical efficiency in public hospitals of services are derailing health sector reform gains as they are which have less flexibility to change their output but can change associated with increasing inefficiency and resource wastages. their use of inputs since they operate using a capped/fixed budget. Kenya through a number of economic development programs In this analysis, hospital efficiency is assessed using an such as Vision 2030,[16] National Health Sector Strategic Plan III input‑oriented model. A two‑stage data envelopment 2013–2018,[17] and the Health Sector Policy Framework 2012– analysis (DEA) was used in estimating technical efficiency 2017 places emphasis on improved efficiency in health‑care scores of the County Referral Hospitals in Kenya and its delivery at all levels. This is important for the health sector determinants. DEA which is defined as a nonparametric since hospitals in Kenya are reported to account for over 75% mathematical programming approach to frontier estimation of the health sector budget and employ over 80% of these key which uses linear programming to sketch a boundary health professionals.[18] Despite this increasing advocacy for function (efficient frontier) to observed data for relatively efficiency, there has been limited effort in assessing the status homogeneous firms.[5] DEA and econometric methods of technical efficiency in health sector in Kenya. At present, have been greatly used in analyzing technical efficiency studies on technical efficiency in county referral hospitals in of health‑care sector in both developed and developing Kenya are limited. The main objective of this study was to countries.[24] In this study, DEA is used to estimate the evaluate technical efficiency of county referral hospitals in hospital‑level efficiency scores and the magnitude of technical Kenya and its determinants. inefficiencies in county referral hospitals. Data and data management Methods In Kenya Health System, the hospital medical record officers Efficiency refers to the use of available economic resources in a collate daily summaries of the numbers of visits, discharges, manner that results in maximum possible output. For a hospital and operations. Every month, hospitals upload a summary of to be economically efficient, it has to be also technically this data on selected output and input indicators to the district efficient. This requires the firm to use the right mix of inputs health information system (DHIS) housed at the Ministry of in light of the relative price of each input (input allocative Health headquarters. However, the staff data and capital data such efficiency) and produce the right mix of outputs given the as bed capacity are updated annually in the DHIS. Therefore, set of prices (output allocative efficiency) which results into the input, output, and other capital data used in the study were [19] economic efficiency. Therefore, economic efficiency refers obtained from the DHIS report 2012/2013 financial year. To to the process of obtaining maximum benefits from a given cost complement this data, personal visits to the county health records [20] or minimizing the cost of obtaining a given benefit. Technical and information offices were done to facilitate records review efficiency of a hospital can be categorized into pure technical and validation of collected data. In this study, two main county efficiency and scale efficiency. Pure technical efficiency refers referral hospitals were selected from each of the 47 counties in to technical efficiency score which cannot be attributed to Kenya to form a sampling frame of 84 county referral hospitals. changes in economics of scale in the production process. It An excel sheet was used to enlist the hospitals using unique assumes no deviations from the optimal scale. It is depicted by numerical codes for each of the sampled hospitals. The choice of regions in which there exists a constant return to scale (CRS) the two main county referral hospitals selected was based on the [21,22] relationship between the outputs and inputs. On the other scale of operations to enhance the comparability of results. In the hand, scale efficiency refers to extent to which health‑care second stage, random sampling was used to randomly sample 34 outputs change due to changes in health‑care inputs. In other county referral hospitals for analysis using the excel sheet random [23] words, according to Salvatore, scale efficiency assumes selection function. This sampling technique was justified by its economics of scale in the production process which is mainly ability to ensure representation and control for selection bias. depicted in regions with variable returns to scale regions. Scale In this study, data analysis was performed in two stages: first, efficiency is equal to CRS technical score divided by variable input and output data were entered into Excel sheet after which returns to scale technical score.[24] DEA version 2.1 was used to determine the technical efficiency Technical efficiency is evaluated relative to other decision‑making scores for the hospitals. In the second stage, Stata, an analytic units (DMUs). A DMU is said to be fully efficient (100%) when software,[27] was used to perform interval regression analysis the performance of other DMUs indicates that altering the mix using censored interval regression model.[28] This model was of its inputs and outputs could worsen the production mix.[25] used to identify determinants of technical efficiency for the Therefore, technical efficiency can be measured in terms of sampled hospitals.[29]

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Before data collection, a research permit to authorize the study (b) Variable Return to Scale and data collection was granted by the National Commission In terms of variable returns to scale (VRS), of the 34 hospitals for Science, Technology, and Innovation which is a research studied, 22 (64.7%) hospitals, were VRS technically efficient regulatory body mandated to regulate research activities in while the remaining 12 hospitals were VRS technically Kenya. Because the study used existing data with no human inefficient. The mean VRS technical efficiency score was subject involvement, ethical review exemption was granted. 94.1% with a standard deviation of 11.1%. District hospital had the lowest variable returns to scale technical Results efficiency score of 63.2%. Table 1 presents the descriptive statistics for inputs and outputs Scale Efficiency of randomly sampled Kenyan county referral hospitals in this Results indicated that 14 (42%) hospitals had an optimal study. In the financial year of April 1, 2012–March 31, 2013, production function for their input -output mix i.e. they had the 34 hospitals [Appendix 1] received 3,499,531 outpatient a scale efficiency score of 100%. The remaining 20 (59%) visits, discharged 241,436 inpatients, and performed 32,401 hospitals had scale efficiency scores of less than 100% and operations. These outputs were produced using a total of were thus deemed scale inefficient. The mean scale efficiency seven inputs which were 234 medical consultants, 284 general score was 87.4%, with a standard deviation of 19.8%. The practitioners (medical officers), 739 clinical officers (specialized average scale efficiency score varied from a minimum of and registered clinical officers), 4542 nurses, 435 therapy 26.2 % to a maximum of 100%. Increasing the quantity of all specialists, 434 laboratory technologists, and 6110 hospital hospitals inputs by a given proportion would result in: beds (including cots). The outputs and inputs varied widely across different hospitals. The means and standard deviations (i) Increasing returns to scale in 41.2% hospitals. of hospitals outputs varied widely across the hospitals. The (ii) Constant returns to scale in 41.2% hospitals. This means mean of outputs was as follows: 102,927 outpatient visits, that their health service outputs would increase in the same 7101 discharges, and 959 operations. On the other hand, the proportion. mean inputs were as follows: 180 beds, 7 medical consultants, 8 general practitioners, 22 clinical officers, 134 nurses, 10 (iii) Decreasing returns to scale in 17.6% hospitals. therapy specialists, and 13 laboratory technologists. Magnitude of inefficiencies using VRS Technical efficiency A total of 22 (64.7%) hospitals were VRS technically efficient Table 2 shows the scores for constant returns to scale technical while the remaining 12 (35.3%) hospitals were VRS inefficient. efficiency, variable returns to scale technical efficiency, scale Technical inefficiency varied from a minimum of 4.1% efficiency, types of scale efficiency and the efficiency reference (Kathiani) to a maximum of 36.8% (Kapenguria). Among set for each hospital sampled in this study. the technically inefficient hospitals, the mean level of VRS inefficiencies was 17.2% with a standard deviation of 10.4% (a) Constant Return to Scale (CRS) [Table 3]. We found that 14 (41%) hospitals were constant return to scale technically efficient, scoring 100% while the remaining 20 Production changes (input and output slacks) (59%) hospitals were constant return to scale inefficient. The 35.3% technically inefficient hospitals required to change their mean CRS technical efficiency was 82.4% with a standard input-output mix in different proportions as shown in Table 4 deviation of 21.1%. The average CRS technical efficiency to attain technically efficiency frontiers. Since the VRS model score varied from a minimum of 26.2% to a maximum of 100%. was an input oriented model, the inefficient hospitals were

Table 1: Descriptive statistics for the hospitals Description Sum Mean Standard deviation Minimum Maximum Range Outputs Outpatient visits 3,499,531 102,927 82,582 2,181 377,566 375,385 Discharges 241,436 7,101 5,866 676 24,202 23,526 Total operations 32,401 959 1,327 14 8,030 8,016 Inputs Beds and cots 6,110 180 119 46 525 479 Medical consultants 234 7 6 1 25 24 General medical officers 284 8 6 1 29 28 Clinical officers (COs) 739 22 17 5 100 95 Nurses 4,542 134 102 27 429 402 Therapy specialists 335 10 8 2 32 30 Laboratory Technologist 434 13 10 1 44 43

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Table 2: Technical efficiency, and Lambda weights for the sampled hospitals No. Name of hospital CRS VRS SE Returns to scale* VRS Hospital reference (S) and lambda (peer) weight (s)/ (Hospital Benchmarks) 1 Coast PGH 0.823 1.000 0.823 DRS 2 Embu PGH 0.759 0.760 0.999 DRS 0.102 (11) 0.209 (22) 0.137 (4) 0.551 (12) 3 PGH 1.000 1.000 1.000 CRS 4 DH 1.000 1.000 1.000 CRS 1.000 (4) 5 DH 0.688 0.874 0.787 IRS 0.044 (22) 0.115 (4) 0.531 (33) 0.138 (21) 0.171 (29) 6 DH 0.636 0.782 0.813 IRS 0.739 (33) 0.229 (22) 0.032 (23) 7 Kapenguria DH 0.594 0.632 0.940 DRS 0.286 (22) 0.714 (29) 8 Kapkatet DH 0.863 0.931 0.927 DRS 0.969 (22) 0.031 (23) 9 Kathiani DH 0.414 0.959 0.432 IRS 0.855 (33) 0.141 (34) 0.004 (22) 10 DH 1.000 1.000 1.000 CRS 11 DH 1.000 1.000 1.000 CRS 1.000 (11) 12 DH 1.000 1.000 1.000 CRS 1 (12) 13 DH 0.878 0.890 0.986 IRS 0.392 (29) 0.371 (17) 0.165 (23) 0.072 (22) 14 DH 0.758 0.896 0.846 DRS 0.760 (22) 0.036 (23) 0.204 (29) 15 DH 0.749 0.802 0.933 IRS 0.353 (33) 0.222 (22) 0.340 (25) 0.085 (11) 16 DH 0.961 1.000 0.961 DRS 17 DH 1.000 1.000 1.000 CRS 18 DH 0.807 1.000 0.807 IRS 19 Moi DH 0.818 1.000 0.818 IRS 20 Msambweni DH 1.000 1.000 1.000 CRS 21 Murang’a DH 1.000 1.000 1.000 CRS 22 DH 1.000 1.000 1.000 CRS 23 PGH 1.000 1.000 1.000 CRS 24 Port Reitz DH 1.000 1.000 1.000 CRS 25 DH 1.000 1.000 1.000 CRS 26 Taveta DH 0.471 0.674 0.698 IRS 0.663 (29) 0.112 (31) 0.225 (17) 27 DH 1.000 1.000 1.000 CRS 28 Nandi Hills DH 0.803 0.803 0.922 IRS 0.742 (29) 0.016 (20) 0.242 (17) 29 DH 1.000 1.000 1.000 CRS 30 DH 0.684 1.000 0.684 IRS 0.304 (20) 0.212 (34) 0.484 (17) 31 DH 0.262 1.000 0.262 IRS 32 Eldama Ravine DH 0.765 0.935 0.818 IRS 0.400 (17) 0.600 (29) 33 Kanyakine DH 0.753 1.000 0.753 IRS 34 DH 0.522 1.000 0.522 IRS Minimum 0.262 0.632 0.262 Maximum 1.000 1.000 1.000 Mean 0.824 0.941 0.874 Standard deviation 0.211 0.110 0.198 Note: The numbers in the brackets indicate the name of the hospital peer (s) (as ordered in the no.column) used in determining hospital reference set in VRS. *There are three types of Return to Scale Efficiency Measures: (i) Constant Return to Scale (CRS) which implies increasing inputs would increase health service output in equal proportion, that is, there is optimal production scale; (ii) Increasing Return to Scale (IRS) which implies increasing input would increase health service output by greater proportion, that is, there is sub-optimal production scale and (iii) Decreasing Return to Scale (DRS) which implies increasing inputs would increase health service outputs by a smaller proportion, that is, there is larger than optimal production scale

required to reduce their inputs and augment any resulting gaps beds (including cots) and 840 staff in all the inefficient with their outputs in different proportions to achieve optimal hospitals. This was an equivalent of 4% reduction in total efficiency scores. In all the inefficient hospitals, the following beds and 28% reduction in total staff in all the inefficient total input reductions were required: 104 beds; 24 medical hospitals. Reductions in input levels are not sufficient specialists; 33 general practitioners; 126 clinical officers, 513 to achieve efficient levels in the inefficient hospitals. nurses and 91 laboratory technologists. This required inefficient hospitals to augment their input While beds slack varied from a minimum of 0 beds to reductions (slacks) with output additions (slacks). All a maximum of 41 beds (Kathiani), the total staff slacks the inefficient hospitals required a 23% increase in total ranged from a minimum of 4 staff (Kangundo) to maximum outpatient visits which translated to an additional 266,376 of 271 staff (Kapenguria). This translated to a total of 104 outpatient visits.

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Increase in outpatient visits varied between a minimum of 0 Discussion to a maximum of 89, 547 visits (Kisumu) with a mean of 22, The mean CRS technical efficiency for the hospitals was 198 outpatient visits. Additions in outpatient discharges varied 82.4%. About a third of the hospitals were technically from a minimum of 0 to a maximum of 2, 649 (Taveta) with a inefficient similar to findings of a study conducted in Kenya mean of 571 discharges in all the hospitals. This translated to by Kirigia et al.[10] which found that about 44% of the public 9% increase in total discharges. A mean of 385 operations was health centers were inefficient with an average efficiency score required for all the hospitals to reach efficient production frontier. of 84%. This means that the hospitals can attain efficiency However, the additional operations varied from a minimum of 0 levels in the efficiency frontiers by reducing their input mix to a maximum of 877 operations in Kapkatet. All the hospitals by an average of 17.6%. However, the high variation in output required to augment their operations capacity with a sum of 4,616 and input production mix among the different hospitals can operations which represented a 55% increase in total operations. be explained by variations in the sizes and operations of the hospitals sampled for the study.

Table 3: Magnitude of Technical Inefficiencies in The variable return to scale (VRS) model indicated that the Hospitals hospitals had a VRS efficiency mean of about 94.1% which meant that they could achieve efficiency levels by reducing their input Name of hospital Technical inefficiency levels by 5.9%. However, this differs from results by Kibe[30] Iten DH 0.126 who found that the average technical efficiency score for level Kangundo DH 0.218 four hospitals in Kenya in the period 2008–2011 was 97.72%. Kapenguria DH 0.368 However, the reduction in efficiency levels may point to increase Kapkatet DH 0.069 in inefficiency in public hospitals amid the many health‑care Kathiani DH 0.041 reforms targeting optimal resource utilization. This was further Kilifi DH 0.110 supported by the scale efficiency findings that about 13.6% of the Kisumu DH 0.104 total inefficiency scores can be attributed to scale inefficiency. Kitui DH 0.198 Optimal operational mix in their production functions is critical Taveta DH 0.326 for their optimal resource utilization. This finding supports the Nandi Hills DH 0.197 Eldama Ravine DH 0.065 available evidence that hospitals in developing countries operate [31] Minimum 0.041 at unacceptably high levels of technical inefficiency. Maximum 0.368 It has been found in this study that two‑fifths of the county Mean 0.166 referral hospitals have increasing return to scale production Standard deviation 0.107 functions. This means that an increase in their health service

Table 4: Production slacks required to make inefficient hospitals efficient Name of Technical Input slacks Total Output slacks hospital inefficiency staff No. of beds Medical General COsNurses Therapy Laboratory Outpatient Discharges Operations and cots consultants practitioners specialists technologist visits Embu PGH 0.240 2 ‑ ‑ ‑ 28 15 15 59 25,835 ‑ 473 Iten DH 0.126 21 1 ‑ 6 ‑ ‑ 3 9 ‑ ‑ 42 Kangundo DH 0.218 22 ‑ 0 ‑ 1 2 1 4 4,838 1,297 ‑ Kapenguria DH 0.368 ‑ 3 4 51 181 10 22 271 55,272 ‑ 321 Kapkatet DH 0.069 ‑ 3 2 24 4 7 8 48 79,975 ‑ 877 Kathiani DH 0.041 41 3 2 ‑ 12 ‑ 3 20 ‑ 455 92 Kilifi DH 0.110 ‑ 0 4 2 63 8 7 84 ‑ ‑ ‑ Kisumu DH 0.104 ‑ ‑ 4 16 102 4 9 134 89,547 ‑ 801 Kitui DH 0.198 19 ‑ ‑ 1 10 ‑ 5 15 ‑ 1,283 570 Taveta DH 0.326 ‑ 1 2 4 2 3 ‑ 12 ‑ 2,649 294 Nandi Hills DH 0.197 ‑ ‑ 2 16 50 2 8 77 ‑ 1,173 579 Eldama 0.065 ‑ 14 13 7 60 3 10 107 10,910 ‑ 566 Ravine DH Minimum 0.041 Maximum 0.368 41 14 13 51 181 15 22 89,547 2,649 877 Sum 104 24 33 126 513 54 91 840 266,376 6,857 4,616 Mean 0.172 9 2 3 10 43 5 8 22,198 571 385 Standard 0.104 14 4 4 15 54 5 6 33,529 853 307 deviation

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inputs would increase their outputs by a greater proportion. produce more outputs with the same or even less inputs which These hospitals should increase their production sizes to achieve an indicator of resource wastages. optimal scale efficiency and reduce unit costs of production. For optimal resource production function to be attained, the This is consistent with observations from Eretria and Zambia excess staff needs to be reassigned to health facilities faced that the hospitals can perform better with the level of resources [29,32] with staff shortage. This will require clear policy guidelines that are allocated to the sector without scaling them up. for effective, rational, equitable, and evidence‑based Similarly, about 40% of the county referral hospitals exhibited staff deployment. This can be achieved by developing CRS in their production function. This meant that increasing demand‑based model as the basis for reassigning excess staff. their health service inputs would result in their health service Further, to augment for output slacks and reduce unit cost of outputs increasing in similar proportion; they are scale efficient. production, a pool of excess staffs needs to be created for However, the close to a fifth of the hospitals with decreasing providing essential, and demand‑based health services to the return to scale (DRS) meant that increasing their inputs in population through outreach programs within and outside the production mix would increase by a smaller proportion. These hospital’s catchment area. Further, the excess beds should be hospitals are not operating at their optimal scale sizes. This transferred to other public health facilities with bed shortages indicates the existence of excess inputs in these hospitals when to ensure that the extra supply of beds does not result in excess/ other hospitals are grappling with resource shortages. unnecessary admissions and longer stays, an effect known as [39] Technical inefficiency can be attributed to inappropriate Roemer’s Law, which has been shown to cause inefficiency production function.[33] Hollingsworth and Parkin[34] indicated in hospitals. that one cause of inefficiency is excess use of resources such Financial support and sponsorship as labor and supplies and inefficient organizational processes. We acknowledge National Commission for Science, This agrees with the study finding that excess inputs such as Technology and Innovation, Kenya, [NACOSTI] for partial overstaffing are causing inefficiencies. This has been shown financing of this research. by the existence of input and output slacks in the production functions. Previous research findings from developing countries Conflict of interest such as South Africa, Eritrea, and Namibia have recommended There is no conflict of interest. reallocation and rationalization of the excess inputs to achieve maximum production frontiers in other facilities.[35,11,36] References Inefficient hospitals with slacks in their production can make 1. United Nations Children’s Fund. Basic Statistics, United Nations International Children Emergency Fund. UNICEF Kenya; 2014. appropriate production changes which may require reducing Available from: http://www.unicef.org/infobycountry/kenya_statistics. the excess inputs to achieve optimal resource utilization. html. [Last accessed on 2019 Aug 17]. However, the input reduction may not be sufficient to make the 2. World Health Organization. World Health Statistics. Geneva: World hospitals inefficient hence the need for augmenting gaps with Health Organization; 2011. [37] 3. Health Sector Working Group Report. Medium Term Expenditure outputs additions. The World Health Organization (WHO) Framework (MTEF) 2012/13‑2014/15. Health Sector Working guidelines recommend proper staffing and resource resources Group Report; 2012. for equality, equity, and improved health outcomes. In 4. Ministry of Health. Health Sector Budgetary Allocation and Spending: addition, public hospitals can benchmark their facilities with Health Sector Analysis Report. Nairobi: Government Printers; 2011. 5. Coelli T. A guide to DEAP Version 2.1: A Data Envelopment Analysis best practices that entrench efficiency principles. However, Programme. CEPA Working Paper 96/8, Department of Econometrics. technical inefficiency has been associated with poor policy University of New England, UK; 1996. framework, governance, and leadership setbacks as supported 6. Constitution of Kenya. Constitution of the Republic of Kenya. [4,38] Government of the Republic of Kenya; 2010. by past studies done in Kenya and Botswana. This calls 7. World Health Organization. Health Statistics: Global Health for a facilitative and transformative leadership committed Observatory. World Health Organization, Kenya Office; 2012. Available to principles of efficiency at all levels of service delivery.[6] from: http://www.who.int/countries/ken/en/. [Last accessed on 2019 This is consistent with the WHO (2000) which advocated for Aug 20]. 8. World Health Organization. The World Health Report 2006: Working efficiency concerns in all functions of a health system. Together for Health. Geneva: World Health Organization; 2006. 9. Nancy C, Florian H. Estimated Government Spending 2009/2010: onclusions Kenyan Health Sector Budget Analysis. Mars Group; 2010. C 10. Kirigia JM, Lambo E, Sambo L. Are public hospitals in Kwazulu‑Natal The inefficient hospitals could achieve efficiency levels by province of South Africa technically efficient? Afr J Health Sci reducing their input levels by 17.6% under CRS and 5.9% 2000;7:25‑32. 11. Schmacker ER, McKay NL. Factors affecting productive efficiency in under variable returns to scale. A substantial number of primary care clinics. Health Serv Manage Res 2008;21:60‑70. county referral hospitals operated inefficiently. This is mainly 12. Kenya Demographic and Health Survey. Kenya Demographic and because of inappropriate production function which has led to Health Survey 2003‑04. Nairobi: Government Press; 2003. inefficient production frontiers in the allocation and utilization 13. Kenya Demographic and Health Survey. Kenya Demographic and Health Survey 2008‑09. Nairobi: Government Press; 2009. of the limited resources in county referral hospitals. The 14. Sealy S, Rosbach K. Kenya Health Sector Budget Analysis. Estimated production slacks mean that the inefficient hospitals could Government Spending. Gtz; 2010.

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Appendix 1: List of county refferal hospitals sampled Code Name of hospital County A Homa Bay District Hospital Homa Bay B Iten District Hospital Elgeyo Marakwet C Kangundo District Hospital Machakos D Kapenguria District Hospital West Pokot E Kapkatet District Hospital Kericho F Kathiani District Hospital Machakos G Kericho District Hospital Kericho H Kerugoya District Hospital Kirinyaga I Kiambu District Hospital Kiambu J Kilifi District Hospital Kilifi K Kisumu District Hospital Kisumu L Kitui District Hospital Kitui M Meru District Hospital Meru N Mandera District Hospital Mandera O Maralal District Hospital Samburu P Moi District Hospital Taita Taveta Q Msambweni District Hospital R Murang’a District Hospital Muranga S Naivasha District Hospital Nakuru T Port Reitz District Hospital U Siaya District Hospital Siaya V Taveta District Hospital Taita Taveta W Mwingi District Hospital Mwingi X Nandi Hills District Hospital Nandi Y Makindu District Hospital Makueni Z Marsabit District Hospital Marsabit AB Loitokitok District Hospital Kajiando AC Eldama Ravine District Hospital Baringo AD Kanyakine District Hospital Meru AE Lamu District Hospital Lamu

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