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REPUBLIC OF KENYA
Ministry of Health
Analysis of Performance, 2013/14
Transforming Health: Accelerating Attainment of Health Goals
Table of Contents List of tables and figures .............................................................................................................................. iv
Foreword ....................................................................................................................................................... v
Abbreviations ............................................................................................................................................... vi
CHAPTER 1: BACKGROUND ....................................................................................................................... 1
Introduction .............................................................................................................................................. 1
Data sources .............................................................................................................................................. 1
CHAPTER 2: OBSERVATIONS REGARDING SECTOR ACHIEVEMENTS ............................................................ 3
Financing of health .................................................................................................................................... 4
Investments in health ............................................................................................................................... 5
Health outputs .......................................................................................................................................... 8
Health outcomes ....................................................................................................................................... 8
Health impact .......................................................................................................................................... 10
CHAPTER 3: ANALYSIS OF SECTOR PERFORMANCE ................................................................................ 12
Efficiency in production of health outcomes .......................................................................................... 12
Equity in production of health outcomes ............................................................................................... 18
Equity in access to services ................................................................................................................. 18
Equity in utilization of services ........................................................................................................... 20
CHAPTER 4: CONCLUSIONS AND RECOMMENDATIONS ......................................................................... 27
List of tables and figures Table 1: Contribution of health to GDP ........................................................................................................ 4
Table 2: Annual production accounts for health, 2009 - 2013 ..................................................................... 5
Table 3: Trends in facility types, before and after the analysis period ......................................................... 5
Table 4: Variables used for county efficiency analysis................................................................................ 13
Table 5: Technical efficiency, by County ..................................................................................................... 15
Table 6: Scale efficiency, by County ............................................................................................................ 16
Figure 1: KHSSP Framework for Implementation ......................................................................................... 3
Figure 2: Contribution of public and private sectors to health GDP ............................................................. 4
Figure 3: Trends in facilities per 10,000 population per county at beginning, and end of the reporting
period ............................................................................................................................................................ 6
Figure 4: Trends in OPD per capita utilization by County, 2013/14.............................................................. 8
Figure 5: Trends in Skilled Birth Attendances, 2010 – 2014 ......................................................................... 9
Figure 6: Distribution of health outcomes, measured by an RMNCAH index ............................................ 10
Figure 7: Variations in deaths of 2013 versus 2012 .................................................................................... 11
Figure 8: Proportion of Counties at different levels of technical efficiency ............................................... 16
Figure 9: Beds per 1,000 vs poverty levels by county, 2014 ....................................................................... 18
Figure 10: Health workforce per 1,000 vs poverty levels by county, 2014 ................................................ 19
Figure 11: OPD per capita utilization vs poverty levels by county, 2014 .................................................... 20
Figure 12: RMNCAH index vs poverty levels by county, 2014 .................................................................... 21
Figure 13: RMNCAH index vs poverty levels by county, 2014 .................................................................... 22
Figure 14: Deliveries by skilled birth attendant’s vs poverty levels by county, 2014 ................................. 23
Figure 15: % children stunted vs poverty levels by county, 2014 ............................................................... 24
Foreword
The health sector in Kenya is increasingly focusing investments to the population in a manner that
ensures they receive services when needed, where needed and in a manner that addresses their needs.
Such targeting of investments calls for innovative ways to better understand how these investments are
made, and eventually used.
As part of the health sector performance monitoring, performance reports that give one a fair picture of
how the sector performed (based on our planned interventions) have been developed. Such
performance assessments are good accountability tools that can be used to ensure we are reporting
back to the citizens of the country on how we are utilizing their resources; and to make available for
them the health services they require.
This analytical report complements the annual performance review report (2013/2014). It is aimed at
health sector actors, to provide a more critical, in-depth analysis the performance based on our focus
and ideals that had been set out as a sector. As we provide services, we strive to ensure that these
services are made available to the population as they need it, and is done in the most efficient and
equitable manner. We attempt to provide through this report a more critical look at the kinds of
activities we are prioritising and the impact they are having, as well as analyse how well we are being
efficient and fair in distribution of our investments and achievement of outcomes. Based on this, we
expect the sector will be able to draw strategic inferences regarding what they need to focus on to
better respond to the needs of the people of Kenya. All Kenyans should be able to enjoy the right to
health in a manner that doesn’t discriminate against them and using scarce resources in the most
efficient manner.
This first analytical report of the health sector has been developed by the National Ministry of Health in
line with its mandate to monitor and strategically guide health focus in the country. Its development has
been carried out under my direct oversight, together with Dr. Isabella Maina – head of the health sector
Monitoring and Evaluation unit and with technical guidance from Dr. Humphrey Karamagi and Mr.
Hillary Kipruto from the World Health Organization, plus Dr. Edwine Baraza, a post-doctoral researcher
at the KEMRI Wellcome Trust Research Program.
I urge all the Health Stakeholders to read the report take note of the lessons learnt and ensure the
recommendations are implemented. In particular, the County Health management are encouraged to
ensure that data management is strengthened and take cognizance of the disparities in equity and
efficiency and endeavour to improve this which would eventually improve health outcomes and
consequently the health of the Kenyan people.
Dr. Nicholas Muraguri
DIRECTOR OF MEDICAL SERVICES
Abbreviations
AHSPR Annual Health Sector Performance Report
CDF Constituency Development Fund
CEC County Executive Committee
DHIS District Health Information System
GDP Gross Domestic Product
FMS Free Maternity Services
HIV Human Immunodeficiency Virus
KES Kenya Shillings
LLITN Long Lasting Insecticide Treated Nets
MAL Malaria
MFL Master Facility List
NCD Non Communicable Disease
NTD Neglected Tropical Disease
RMNCAH Reproductive, Maternal, Newborn, Child, Adolescent Health
SARAM Service Availability and Readiness Assessment Mapping
SE Scale Efficiency
TB Tuberculosis
TE Technical Efficiency
US$ United States Dollars
WHO World Health Organization
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CHAPTER 1: BACKGROUND
Introduction The health sector in Kenya is one of the highly devolved sectors, implying service provision and
management is now primarily a function of the counties. This calls for more in-depth and innovative
ways of identifying areas of focus in order to attain the desired health goals not only for the counties
(that are now responsible for service delivery) but also for the country at large.
At present, the health sector has instituted a process to monitor sector performance. This focuses on
monitoring the achievements against planned targets for different services and indicators, and is a
critical management tool. However, there is still a lack of an analytical process that will generate the
required knowledge to guide decision makers at national and county levels on the key actions they need
to strategically focus on, if they are to achieve their health goals.
The development of this analytical report is a step in this direction. It aims to provide a more critical
analysis of the existing health data and information, to assist the sector draw strategic inferences
regarding what they need to focus on to better respond to the needs of the people of Kenya.
Data sources The report is based on analysis of the existing health sector information. It doesn’t generate new
information, but rather relies on whatever information exists relating to health, to draw the analysis and
inferences needed to guide strategic health decision making. This of course means that it relies on the
accuracy of the information as reported by the health sector. But, it is the view of the health sector that
however inaccurate the information is, it still provides a sneak preview into what is going on in the
health sector – rather than having no information at all. Over time, as it is apparent what health
information is used for, the quality of data generated for health will continue to improve.
As the report is an analysis of health, not just of Ministry of Health, it draws on information from
multiple sources, depending on the area of focus. The key information sources include:
i) The Annual Health Sector Performance Report (AHSPF), July 2013 – June 2014 from the Ministry
of Health [1]that captures sector performance for the financial year under analysis
ii) National mortality statistics 2013 – 2014 from the Civil Registration Department that captures
information on mortality trends
iii) National Service Availability and Readiness Assessment Mapping 2014 from the Ministry of
Health [2]that mapped the state of all health sector inputs at the beginning of the period of
analysis
iv) Kenya Economic Survey 2014 [3]by the Ministry of Planning that captures overall economic
indicators and county poverty rankings for an equity analysis
v) Assessment of the impact of Free Maternity Services (FMS) [4]by the Ministry of Health that
documents progress, issues and challenges with the FMS policy implementation
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vi) Assessment of the status of service delivery in primary care facilities[5], and hospitals [6] by the
Ministry of Health in 2014
vii) The Ministry of Health employee, work environment and client satisfaction survey of 2014 [7]
This report is not able to provide all the different angles of analysis that can be carried out from the data
in these various sources of information. It is, rather, an attempt to illustrate the depth of inferences that
can be drawn from the existing health information, without the need for additional data. It is our hope
that this effort will be continued in subsequent years, and this form of analysis deepened in scope and
breadth to provide more detailed evidence for decision making in the health sector.
The report is structured in two key formats. First it presents overall observations of health achievements
at the financing, investment, output, outcome and impact levels, based on the existing data from the
various sources. Following this, we introduce some analytical methods that can be used to understand,
better, the sector information. For this particular report we focus on analysis of efficiency and equity in
delivery of services using known tools. It is out hope that you find the reading of the report quite
beneficial to you.
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CHAPTER 2: OBSERVATIONS REGARDING SECTOR ACHIEVEMENTS
Observations relating to health sector achievements are captured following the overall KHSSP
framework, linking impact to financing (see below).
Figure 1: KHSSP Framework for Implementation
INPUTS/PROCESSES OUTPUTS OUTCOMES IMPACT
This is based on the premise that:
1. Health resources are used to finance different inputs and processes needed to make the health
system functional;
2. The levels and forms on investments in the health system are meant to attain desired
improvements in health outputs, captured across improvements in access to services (physical,
financial and socio-cultural), quality of care (client experiences, patient safety, effectiveness of
care), and demand for services (healthy behaviours, health seeking behaviour);
3. Achievement of these outputs are what drive the improvements in coverage with KEPH services,
measured by better utilization of health and related services across the six sector objectives;
4. These improvements in coverage with KEPH give the desired sector impact of better responsive
health
We reflect on the kinds of achievements made across each of these domains during the year under
review. The information is from various sources, as highlighted in the previous chapter.
QUALITY OF CARE
Eliminate Communicable
conditions
Halt / reverse Non
Communicable Diseases
Reduce violence and
injuries
Provide essential health
care
Minimize risk factor
exposure
Strengthen cross sectoral
collaboration
BETTER HEALTH,
IN RESPONSIVE
MANNBER
HEA
LTH
RES
OU
RC
ES
DEMAND FOR CARE
ACCESS TO SERVICES
Organization of
Service Delivery
Human Resources
for Health
Health
Infrastructure
Health Products &
Technologies
Health Information
Health Leadership
Health Financing
Health Research
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Financing of health According to the 2014 Kenya Economic Survey report[8], the overall economy continued to grow during
the FY 2013/14. Public revenues increased by 21.8%, from KES 831.1bn to KES 1,017.7bn. Public
expenditure was at KES 1.3trillion, of which KES 395.4bn was on the social sector (a 7.1% increase from
the previous year). The contribution of the health industry to the overall GDP is shown below.
Table 1: Contribution of health to GDP
2009 2010 2011 2012 2013
OVERALL GDP at market prices 2,375,971 2,570,334 3,047,392 3,403,534 3,797,988
Total Health industry 60,196 64,738 74,237 81,850 72,914
Private actors contribution 33,525 34,920 38,805 42,153 45,112
Government contribution 26,671 29,818 35,432 39,697 27,803
Source: Economic Survey 2014, table 2.3
The contribution of the health industry to the overall GDP has remained low, and showed a reduction to
1.9% from 2.4%. The increases in GDP are benefitting other sectors relative to health.
The proportional contribution of the private sector (corporations, households, and non-profit
institutions) to the overall health GDP increased during the period of review.
Figure 2: Contribution of public and private sectors to health GDP
Source: Calculated from Economic Survey 2014
The trends in the annual production accounts for health in the country also show a reduction in the
health output (expenditure), with the per capita health spending suggestive of a reduction from KES
3,046 (US$ 35.84) to KES 2,722 (US$ 32).
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Table 2: Annual production accounts for health, 2009 - 2013
Variable 2009 2010 2011 2012 2013
Output at basic prices 86,468 91,712 107,432 123,989 113,791
Intermediate consumption 26,272 26,974 33,195 42,139 40,877
Gross value added at basic prices 60,196 64,738 74,237 81,850 72,914
Compensation of employees 40,904 44,107 51,983 58,355 47,590
Gross operating surplus / mixed income 19,293 20,630 22,254 23,495 25,324
Total health expenditure per capita (KES) 2,293.58 2,382.13 2,719.80 3,046.41 2,722.27
Total health expenditure per capita (US$) 26.98 28.03 32.00 35.84 32.03
Employee compensation as % of total expenditure 47.3% 48.1% 48.4% 47.1% 41.8%
Source: Economic Survey 2014, table 2.6
We see a marked reduction in the compensation of employees during 2013 (over 10billion KES), which
appears to be the key driver in the reduction of the health expenditures. This could be a statistical
artefact due to delays in incurring PE expenditures that occurred during the period.
The increases occasioned by the other health factors of production are minimal, suggesting there is
minimal increases (if any) in health financing during the period of our analysis.
Investments in health The Service Availability and Readiness Assessment Mapping (SARAM) exercise provided the health
sector with a comprehensive baseline regarding availability of services, and investments at the
beginning of the reporting period. It is therefore possible to review the changes in investments from the
time, to build a picture of what the sector constituents have been investing in.
We start by reviewing the state of health infrastructure. We use the overall availability of functional
facilities as captured in the SARAM exercise at the beginning of the reporting period, and from the
Master Facility List at the end of the reporting period. The data suggests the health sector has been able
to increase the number of functional health facilities (described as a facility that is providing some form
of health services) by about 20%, with a current estimate of 9,642 health facilities being functional. The
largest increase in facility types was from the private health facilities (36%), while faith based facilities
showed the smallest increase (5%). Public facilities increased by only 15%.
Table 3: Trends in facility types, before and after the analysis period
Before After Variance % change
Total number of facilities 7995 9642 1647 20.6%
Type of facility by level of care
Hospitals 607 720 113 18.6%
Primary care facilities 7388 8922 1534 20.8%
Facility by ownership
Public 3978 4584 606 15.2%
Faith based 1327 1388 61 4.6%
Private 2690 3670 980 36.4%
Source: SARAM 2013, and Annual sector performance report 2013/14
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The increases in facilities is not uniform across the country, with the largest increases seen in Nyeri,
Kirinyaga, Kajiado and Kiambu counties. The trends, by county per 10,000 populations are shown below.
Figure 3: Trends in facilities per 10,000 populations per county at beginning, and end of the reporting period
By the end of the reporting period, the sector had 2.2 facilities per 10,000 persons, as compared to 1.8
facilities per 10,000 persons. This increase could be attributed to better reporting on facility
functionality by counties, but is more likely a result of county efforts to make functional a number of
health facilities they inherited that were either in the process of construction, or which were not yet
functional. The CDF, local authorities, and other public sources of financing had been investing in health
in the period prior to county functionality, which could explain the public facilities. On the other hand,
the large increase in private facilities could point to better reporting of public health facilities in the
Master Facility List.
Looking at the health workforce, useable information is difficult to come by, particularly as a result of
the transition of the health workforce management to counties during the period. However, we can
infer that the disruptions in personnel emoluments for health workers noted during the year reduced
their productivity. The effects on health services arising from this reduced productivity would be most
marked in the counties that witnessed severe disruptions in services and less so in those counties that
managed to maintain personnel emoluments for their staff. Additionally, this effect should be blunted
by the – as of now anecdotal evidence of – increased availability of lower level cadres recruited by
counties to make functional their lower level facilities. It is however imperative that both levels of
government must put in order mechanisms of monitoring and reporting on the health workforce
delivering health services both at the county and at the National level.
Health products availability and use also is quite difficult to discern in the absence of county-wide
information. Anecdotal evidence from Counties suggests there is increased investment in purchase of
commodities due to local purchasing by counties to complement the nationally available budgets.
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However, information from the state of health services reports [5], [6] suggests a still weak picture on
the ground. At the end of the reporting period, less than 50% of primary care facilities had all the tracer
commodities in stock with 47.5% and 26.9% availability of the 16 tracer drugs in health centres and
dispensaries respectively. Similarly, only 50% of health centres had all the 16 tracer non-
pharmaceuticals in stock while only 19.2% of health centres were fully stocked indicating a major
challenge for dispensaries since tracer commodities must be available in all facilities at all times for
efficient delivery of quality services. Regarding expiries, there was a 15.0% and 11.5% of expired drugs in
health centres and dispensaries respectively which was way higher than the recommended level of 5%.
Regarding health financing, counties implemented, to different levels, the free maternity and user fees
policies that the government enacted just prior to the reporting period. The assessment of the free
maternity services showed the policy is implemented in various forms in all the counties and has
reduced financial barriers to maternal services in the country. Efforts to scale up insurance mechanisms
and other approaches to improve efficiency in use of resources however did not show any significant
improvements during the year.
The health information systems strengthening witnessed a number of challenges, with reduced
reporting in the DHIS noted in the 1st half of the year, which however improved towards the end of the
reporting period due to direct advocacy efforts with counties to improve on reporting. In addition, the
efforts to improve availability and use of vital statistics started to bear fruits, with the country
production of the 1st birth / death statistics of the past few years achieved. Coordination of health
research through the newly established research unit in the MOH also showed improvements during the
period under review.
Efforts at improving service delivery systems were also noted during the year. The referral strategy
launch, and various referral system strengthening initiatives were noted across counties. Most counties
recognized the critical role of community systems and prioritized these, though only a few counties
(Nakuru, Bungoma, Kilifi for example) made significant investments to improve availability of the
comprehensive community care services. Anecdotal evidence suggests supportive supervision and
mentoring systems reduced, with oversight and support to lower level facilities reducing in most
counties. The KEPH was defined, but its operationalization not well disseminated to counties and
facilities limiting its use in planning. Finally, there were no coordinated efforts discerned at monitoring /
supporting the quality of facility based services.
Finally looking at leadership and governance, we see the general trend in most of the counties being
that of calling for quick results in health outcomes. To facilitate these, the health management teams in
counties were largely left intact, with changes primarily at the political and administrative levels where
CECs and Chief Officers were appointed. These provide the required political and administrative
oversight of health activities as required in the constitution. We have, as a result, seen at least two
trends in management structures emerging in the counties depending on how the technical health
functions are managed
1. A single, versus multiple Directors (public health, clinical / medical)
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2. A single large county management team, versus a small county management team
complemented by multiple sub county teams
The technical utility of these different arrangements, in terms of their ability to deliver health results,
needs to be further analysed to provide required guidance to counties on the most effective ways of
managing the health technical functions.
Health outputs As highlighted, it is expected that improvements in access, quality of care, and demand for services
resulting from the various investments made in the sector. From this perspective, it is noted the sector
primarily focused on improvements in access to services as opposed to quality of care, or demand for
services. Such access improvements are most noted with physical access (more reported facilities / staff
/ commodities) and financial access (free maternity services, and free primary care services), though
there is no clear evidence of improved socio-cultural access. The effects of this are quite varied across
counties, as seen by the marked differences in OPD utilization across the counties (see figure below).
Figure 4: Trends in OPD per capita utilization by County, 2013/14
Quality of care initiatives are mostly still at the drawing board, with very limited roll out across
implementing units effected. The community based, and advocacy efforts to improve demand and use
of available services were being scaled up in selected counties – with no clear evidence these were
having significant impact by the end of the reporting period.
Health outcomes The result of this skewed focus on health outputs relating to access improvements is seen in the levels
of health outcomes achieved. The most marked improvements in utilization are those that are most
affected by improved access, while the least improvements are seen for those services reliant more in
improvements in quality of care, or demand for services
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Figure 5: Trends in Skilled Birth Attendances, 2010 – 2014
We also see a varied picture in terms of utilization of different health and related services across the
counties, with the picture driven by the specific services a county is focusing on. There is no county
witnessing a comprehensive and universal improvement in utilization across all the health outcome
indicators, reflecting the skewing of investments towards specific services. We have derived an index
from selected indicators purposively selected based on their focus on RMNCAH (the index being the
average value for the indicators), to illustrate the differences across the counties. The indicators used to
derive the index are the following:
% Fully immunized infants
% HIV + pregnant mothers receiving preventive ARV’s
% under 5’s treated for diarrhoea
% School age children de-wormed reported in health facilities
% Deliveries by skilled attendant in Health facilities to expected total deliveries (eligible pop.)
% of Women of Reproductive age receiving Modern methods of Family planning
% of newborns with normal birth weight
% pregnant women attending at least 4 ANC visits
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The specific county achievement against this index is shown in the table below.
Figure 6: Distribution of health outcomes, measured by an RMNCAH index
We see a range from 20% in Mandera County, to 59% in Kericho County across the index highlighting the
varied levels of utilization across the counties.
These variations in outcomes are seen across many services, and so represent real differences in access,
quality and demand for care across the country.
Health impact Information on impact of the actions in the year are difficult to discern, as the data is usually collected
through surveys. However with routine information, from civil registration (overall deaths registered),
and the client satisfaction surveys. The Demographic and Health Survey that would provide information
on age-specific mortality cannot be used, as
- The DHS statistics are an average for a period of time, not only the year under consideration (for
example, Maternal Mortality Rates are average for the preceding 10 years)
- The DHS data was not completed, at the time of completion of this report.
The civil registration data on deaths is not complete. However, it represents the only available and
comprehensive data source, particularly when analysing trends over time where systematic errors in the
data should be the same across years.
Comparison of the deaths per 1,000 persons by Counties for 2012 and 2013 shows significant variations
across Counties. Nationally, there were 4.44 deaths per 1,000 persons in 2014 as compared to 4.3
deaths per 1,000 persons in 2013, representing a minor increase in deaths by 0.15 per 1,000 persons. 22
counties registered reductions in overall registered deaths, with 25 counties registered increases. The
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county with the highest increase in deaths registered is Garissa County (2.19 more deaths per 1,000
persons), while Bungoma County registered the largest reduction in deaths registered (2.17 less deaths
per 1,000 persons).
There is therefore no any hard evidence of reductions in overall mortality. However, we see some
rudimentary evidence of changes in geographical mortalities, though there is no specific discernible
pattern (e.g. counties with more donor support having larger reductions in deaths).
Figure 7: Variations in deaths of 2013 versus 2012
However it is expected that there shall be changes in the mortality patterns arising from the
achievements reported by the sector. Given the improvements in access noted, it is expected that
mortality due to conditions associated with urgent care (communicable diseases, acute events, or
conditions in mothers, children or neonates) to have reduced. The reduction however is affected by the
counter effect of non-improvement in quality of care, reducing its potential effect on overall mortality.
Responsiveness of the services to the legitimate needs of clients represents one of the key impact thrusts of the sector. From the employee, client and work environment survey, overall patient satisfaction appears fair, with at least 63% of people reporting good access and 69% satisfied with the services (highest satisfaction being with immunization, while emergency and mortuary services had least satisfaction levels).
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CHAPTER 3: ANALYSIS OF SECTOR PERFORMANCE
In this chapter, we analyse the sector performance in the following ways:
1. We look at the technical efficiency with which available inputs are used to produce the desired
outcomes. By analysing the technical efficiency in production of health, we are able to provide
guidance on how much more outputs can be produced with the given investments, or looking at
it the other way we can tell by how much we can reduce the current investments, without
affecting the achieved outcomes. We therefore are able to make best use of the resources
available.
2. We look at the equity / distribution of investments to better understand the variations in
capacities to produce outcomes. We recognize some areas of the country are disadvantaged and
so an analysis that compares investments across different equity levels allows us see how fairly
investments are being made.
Efficiency in production of health outcomes
The health sector is unique, in that it requires multiple types of inputs / investments to produce a
multiple set of outputs. A simple technical efficiency analysis therefore is difficult to conduct, as it is
arguable which input / output best represents the health sector effort.
Attempts have, however, been made with various methodologies to capture the multiple input / output
nature of health sector actions, to allow for efficiency analyses. Efficiency is calculated relative a frontier
function using either non-parametric mathematical programming methods or econometric/regression
methods. Each have advantages / disadvantages, but the non-parametric mathematical programming
method – the Data Envelopment Analysis (DEA) – is the most commonly used in health[9]. Its main
advantage is that it is able to deal with multiple inputs and multiple outputs or services. It not only
identifies inefficiencies, but also permits analysis of sources of inefficiency and quantification of
magnitudes of inefficiencies in the production of outputs. It is for these reasons that, we considered DEA
appropriate for the purposes of this study.
For an appropriate technical efficiency analysis, we require a set of inputs, and outputs that are
managed by, and the result of actions by a given Decision Making Unit (DMU). Such a Decision Making
Unit should have a level of autonomy over its decision making process. We use each County as a
standalone DMU as it possesses the decision making autonomy required for it to be a DMU. Our
efficiency analysis is therefore based on comparison of the 47 Counties. We analyse the production by
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counties of the maximum possible outputs from its available investments. Being a non-parametric
method of efficiency analysis, we compare counties against each other, with efficiency of a county being
a measure relative to other counties. A county is rated as ‘efficient’ if the other counties cannot show
evidence of use of inputs in a better way. Its inputs (or outputs) cannot be improved without worsening
some other inputs (or outputs).
The DEA methodology will give us a number of ‘efficient Counties’, plus levels of variation from these
efficient counties of the others. Given the multiple input / output nature of the health sector, there are
various mixes of inputs / outputs that are considered efficient. The set of ‘efficient’ counties form the
frontier of efficiency, against which the ‘inefficient’ counties will vary.
From the method, we are able to derive two forms of efficiency: technical efficiency, and scale
efficiency. Technical efficiency looks at the level of efficiency of a county that cannot be attributed to
deviations from the optimal scale (where there are constant returns to scale – one unit of input gives
one unit of output). On the other hand, scale efficiency looks at the extent to which a county deviates
from the optimal scale (one unit of input produces less or more than one unit of output). The technical
efficiency is therefore able to tell us by how much we can reduce investments for a given level of output
(or increase the outputs for a given level of investment), while scale efficiency is able to tell us by how
much any additional investment will give us the desired level of outputs.
Given the multiple inputs/multiple output nature of health, it is important to agree on a few inputs /
outputs that will be used for the efficiency analysis. We focused on those indicators which are able to
give a wide range of impacts across the sector, and have fair data across the counties. The input data
was for the beginning of the reporting period (June 2013 – sourced from the AHSPR, SARAM and MFL),
while the output data was for the end of the reporting period (July 2014 – sourced from the AHSPR, and
DHIS). The variables used, together with the standard deviations of the data across the counties are
shown below.
Table 4: Variables used for county efficiency analysis
Standard Deviation INPUT VARIABLES
Number of hospitals per county 13.55
Number of primary healthcare facilities per county 118.64
Monetary allocations per county (KES) KES 1,618,565,855.12 (USD 19041951.24)
Number of healthcare workers per county 1,228.97 OUTPUT VARIABLES
% Deliveries conducted by skilled attendant in Health facilities 14.52
% pregnant women attending at least 4 ANC visits 9.82
% Infants under 6 Months on exclusive breastfeeding 2013/14 19.71
Child Immunization coverage 35.16
% new outpatient with high blood pressure 1.15
% New outpatients with Diabetes 0.30
outpatient annual utilization (per capita) rate 0.61 Source: Input data from SARAM / MFL, output data from DHIS / AHSPR
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The input variables were chosen to represent a set of different investments, for which good information
exists across all the counties. All the input data was standardized to a per capita value, to weight the
different populations on counties into the measure.
Number of facilities (hospitals / primary care facilities) is a good input variable as it illustrates
the level of physical access to services. The data used is for all facilities in the county – public
and non-public as these are all recognized service provision points. The counties with more
facilities per person have more weight for this variable.
The monetary allocation looks at the budgeted amount of funds allocated by treasury, to each
county. This represents the potential amount of funds directly available to a county team to
provide services. It is useful, as it also indirectly captures many other input investments
(operational funds, medicines and supplies, etc). Different counties allocate different amounts
to health, with the counties allocating more of this allocation to health being more advantaged
with this input, as compared to those that use less of this available budget. The values used are
only for the on-budget allocations, and don’t include off budget amounts available to counties,
which are difficult to determine in a similar manner, and most of these funds are not under
direct control of the county teams – their utilization is determined by the implementing
partners.
The number of health workers looks at the total available health workforce in the county – both
public and non-public. The counties with more HWs have more weight for this variable.
On the other hand, the output variables used were aimed at having values that capture the wide scope
of outcomes that health is attempting to attain, focusing only on indicators which have fairly good data
across all the counties.
Two maternal health indicators were used, given the strong sector focus on improving services
to mothers – deliveries conducted by skilled birth attendants and ANC 4 visits. The percentage
utilization across counties was used, with counties that had higher utilization scoring higher.
The analysis used two indicators addressing child health – one for early childhood survival
(exclusive breastfeeding), and the other for fully immunized children. Again, percentage
utilization across counties was used, with counties that had higher utilization scoring higher.
The analysis included two indicators for Non Communicable Diseases (NCDs) – new outpatients
with high Blood Pressure (BP), and diabetes to ensure this increasing health threat is included in
the outcome analysis. As higher values denote lower success in a county at managing these
risks, the analysis scored counties with higher values lower.
Finally, the analysis included one general output indicator – per capita new OPD cases – to
include in the weighting most of the other health services that could not be included due to data
gaps
The technical efficiency scores for the various counties are therefore shown in the table below.
15 | P a g e
Table 5: Technical efficiency, by County
RELATIVE TECHNICALLY EFFICIENT COUNTIES RELATIVE TECHNICALLY INEFFICIENT COUNTIES
County name Efficiency score County name Efficiency score
Baringo 100 Turkana 93.3 Busia 100 Tharaka Nithi 73.46 Elgeyo Marakwet 100 Taita Taveta 61 Embu 100 Bomet 43.01 Homa Bay 100 Kirinyaga 41.99 Isiolo 100 Kisii 39.36 Kiambu 100 Garissa 38.07 Kilifi 100 Laikipia 30.3 Kisumu 100 Siaya 29.9 Kwale 100 Mombasa 28.16 Lamu 100 Mandera 21.03 Marsabit 100 Trans Nzoia 20.58 Migori 100 Nandi 17.93 Nairobi 100 Vihiga 17.65 Narok 100 Nyamira 14.56 Nyeri 100 Kericho 13.2 Samburu 100 Kajiado 10.58 Tana River 100 Muranga 9.84 Wajir 100 Uasin Gishu 8.76 West Pokot 100 Nyandarua 8.26 Makueni 7.22 Nakuru 5.68 Machakos 5.04 Kitui 4.33 Bungoma 3.16 Meru 3.04 Kakamega 2.72
The mean scores of pure TE and SE of the counties were 56.43% (SD 41.64) and 50.25% (SD 36.81),
respectively. Of the 47 county health systems, 20 (43 %) were technically efficient constituting the ‘best
practice frontier’. The remaining 57 % were technically inefficient, with an average TE score of 24.15 %
(SD 22.90).
This finding implies that these 27 inefficient counties could potentially produce 75.85 % more outputs by
utilizing the current levels of inputs (They could also reduce their current input endowment by 75.85 %
while leaving their output levels unchanged).
A significant proportion (40%) of counties had a technical efficiency score of less than 30%, highlighting
the significant variations in capacities to efficiently utilize available resources in the country (see figure
below). There is a significantly wide variation in the use of available resources to produce the desired
health outcomes.
These counties with high levels of inefficiencies can therefore be focused on, to produce more health
outcomes (or, their investments reduced without affecting their current outcomes). The sector has
significant scope for improving on its health outcomes, therefore, by focusing on these inefficient
counties to improve on health targets.
16 | P a g e
Figure 8: Proportion of Counties at different levels of technical efficiency
Additional efficiency analysis looked at scale efficiency, with the results shown in the next table.
Table 6: Scale efficiency, by County
County name Efficiency RTS Sum Lambda
County name Efficiency RTS Sum Lambda
Isiolo 100 CRS 1 Marsabit 36.52 DRS 1.1
Lamu 100 CRS 1 Kirinyaga 34.1 DRS 1.16
Vihiga 100 CRS 1 Siaya 32.32 DRS 1.11
Kitui 99.99 CRS 1 Embu 30.86 DRS 2.44
Kajiado 99.88 CRS 1 Bomet 28.53 DRS 1
Makueni 99.88 CRS 1 Garissa 28.45 DRS 1.15
Meru 99.88 CRS 1 Mombasa 27.36 DRS 1.16
UasinGishu 99.87 CRS 1 ElgeyoMarakwet 19.63 DRS 1.01
Muranga 99.84 CRS 1 Busia 16.91 DRS 1.33
Machakos 99.78 CRS 1 Kwale 16.89 DRS 1.11
Laikipia 99.73 CRS 1 West Pokot 14.89 DRS 1.01
Nyandarua 96.54 CRS 1 Turkana 14.88 DRS 1.01
Bungoma 91.85 CRS 1 Baringo 14.51 DRS 1
Nyamira 87.71 CRS 1 Wajir 13.99 DRS 1.03
Kakamega 76.25 CRS 1 Kisii 12.24 DRS 1.22
TaitaTaveta 75.49 DRS 1.01 Nyeri 11.83 DRS 1.18
Kericho 72.76 CRS 1 Narok 10.69 DRS 1
Tana River 70.78 DRS 1 Migori 8.6 DRS 1.38
Nandi 55.87 DRS 1 Kisumu 7.99 DRS 1.27
Samburu 54.81 DRS 1 Homa Bay 5.22 DRS 1.05
TharakaNithi 52.62 DRS 1.03 Kilifi 4.98 DRS 1.3
Nakuru 46.53 DRS 1.06 Kiambu 4.06 DRS 1.68
Trans Nzoia 44.28 DRS 1.13 Nairobi 1.08 DRS 1.21
Mandera 41.07 DRS 1
17 | P a g e
Only 3 (6 %) county health systems had an SE of 100%, implying thereby that they had the most
productive scale size (MPSS) for that particular input-output mix. The remaining 44 (94 %) county health
systems were found to be scale inefficient, manifesting a mean SE score of 53.45 % (SD 35.57).
This implies that, on average, the scale-inefficient county health systems could increase their output size
by 46.55 % at the current level of inputs. Alternatively, on average, the scale-inefficient county health
systems could reduce their input size by 46.55 % without affecting their current output levels.
Of the Scale inefficient counties, 31 (70%) had decreasing returns to scale (DRS) implying that these
inefficient counties need to scale down their operations to achieve constant returns to scale (CRS).
18 | P a g e
Equity in production of health outcomes The nature of health services means it is a right for people to have access to, and use them. A
comparison of the access and utilization of available health and related services is important in
understanding the fairness of the levels of health investments. Regular data on use of services by the
poor across counties during the period of the analysis is not directly available. However, we use
comparison data to probe into equity in access, and use of services. We look at the available data by
county in 2 key areas:
1. A comparison based on poverty levels of different investments in health as a probe into access,
2. A comparison across counties based on poverty levels of utilization of services
Poverty levels by county are derived from the Economic survey 2014, while the rest of the data is from
the AHSPR 2013/14. This analysis does not account for intra-county variations in poverty levels, which
may be significant on their own. In the future, it will be critical for such analyses to drill down within the
county for this kind of information.
Equity in access to services
We look at information on two key investment variables; infrastructure and health workforce. Beds (and
cots) per 1,000 in each county, compared against the poverty levels by county are shown in the figure
below
Figure 9: Beds per 1,000 vs poverty levels by county, 2014
19 | P a g e
Kajiado
Nairobi
Kirinyaga
Kiambu
Meru
Muranga
Nyeri
Lamu
Narok
Siaya
Mombasa
Nakuru
Embu
Vihiga
Homa BayKericho
Nyandarua
Nandi
Bomet
Migori
Nyamira
KisumuTharaka Nithi
Garissa
Trans Nzoia
Uasin Gishu
Laikipia
Bungoma
Kakamega
Taita Taveta
Elgeyo Marakwet
Baringo
Machakos
Kisii
KituiMakueni
Busia
West Pokot
Kilifi
Isiolo
Samburu
Kwale
Tana River
Marsabit
Wajir
Mandera
Turkana
.51
1.5
22
.53
3.5
4
Be
ds/
cots
pe
r 1
,00
0
10 20 30 40 50 60 70 80 90 100Poverty headcount(%)
Of the counties with high levels of poverty, Isiolo has the highest investments in beds per 1,000
population, while the counties of Turkana, Mandera, Marsabit and Wajir are investing least in
infrastructure. Additional infrastructure investments in these counties are needed, to improve access to
services. Lamu and Nairobi appear to invest more than they may require in terms of infrastructure. It is
important also to note that this analysis does not take into account the geographical distribution of the
beds within the county which could be a significant factor.
A further look at available health workforce per 1,000 persons in each county, compared against the
poverty levels by county are shown in the figure below
Figure 10: Health workforce per 1,000 vs poverty levels by county, 2014
20 | P a g e
Kajiado
Nairobi
Kirinyaga
Kiambu
Meru
Muranga
Lamu
Nyeri
Narok
Siaya
Mombasa
Nakuru
Embu
VihigaHoma Bay
Kericho
Nyandarua
MigoriBomet
Nandi
NyamiraKisumu
Garissa
Tharaka Nithi
Trans Nzoia
Laikipia
Uasin Gishu
Bungoma
Kakamega
Taita Taveta
Elgeyo Marakwet
Baringo
Machakos
Kisii
Kitui
Makueni
Busia
West Pokot
Kilifi
Isiolo
Samburu
Kwale
Tana River
Marsabit
Wajir
ManderaTurkana
0.5
11.
52
2.5
33.
54
4.5
55.
56
HW
s pe
r 10
00 p
erso
ns
0 10 20 30 40 50 60 70 80 90 100Poverty headcount(%)
Again, we see the Northern Arid Lands having fewer investments in health workforce as compared to
the levels of poverty. On the other hand, Embu, Mombasa and Uasin Gishu are outliers, with relatively
high numbers of health personnel as compared to their poverty levels. For better equity in distribution
of health workers, more staff need to be made available in these counties of Mandera, Turkana, Wajir,
and Marsabit.
Equity in utilization of services
This perspective of looking at equity now compares health outcomes, with poverty headcounts. We
would expect to have higher utilization of services with a higher poverty headcount, as we expect more
poor persons to require more health services. The patterns for select outcome indicators are now
discussed.
We start by comparing the health outputs resulting from the investments in health, with the poverty
headcount. The information, by County, is shown in the figure below.
We again see the same pattern with health investments reflected at this output level, with per capita
OPD utilization too low when compared with expected levels in the Northern Arid Counties of Turkana,
Mandera, Wajir and Marsabit. The levels in Tana River also appear low compared to the investment
level, and need further analysis.
Figure 11: OPD per capita utilization vs poverty levels by county, 2014
21 | P a g e
Kajiado
Nairobi
Kirinyaga
Kiambu
Meru
Muranga
Lamu
Nyeri
Narok
Siaya
Mombasa
Nakuru
Embu
VihigaHoma Bay
Kericho
Nyandarua
Bomet
Migori
Nandi
Kisumu
Nyamira
Garissa
Tharaka Nithi
Trans Nzoia
Uasin Gishu
Laikipia
Kakamega
Bungoma
Taita TavetaElgeyo MarakwetBaringo
Machakos
Kisii
KituiMakueni
Busia
West Pokot
Kilifi
Samburu
Isiolo
Kwale
Tana RiverMarsabit
Wajir
Mandera
Turkana
0.5
11.5
22.5
33.5
44.5
OP
D u
tilis
atio
n r
ate
0 10 20 30 40 50 60 70 80 90 100Poverty headcount(%)
On the other hand, Embu County is clearly an outlier, with a significantly higher OPD per capita
utilization as compared to all the other counties. The county did have a high health workforce count, but
the infrastructure was not significantly higher than its peer counties. It should be recalled that the
County was one of the frontier counties for technical efficiency, but scored poorly on scale efficiency
and had decreasing returns to scale. A more in-depth analysis is needed, to better understand the
dynamics occurring in Embu County.
Looking at actual health outcomes, we present information in a variety of ways. First, we look at the
RMNCAH index achievement by county, versus the poverty headcount. We would expect counties with
higher poverty levels to utilize more RMNCAH services. The county comparisons are shown in the
proceeding figure. We see the counties with low levels of poverty performing well in RMNCAH – a very
good sign that these counties are taking advantage of their existing investments to actually utilize
available services. Of the Northern Arid Land counties, Garissa appears the outlier, being able to achieve
RMNCAH outcomes higher than its peers.
Figure 12: RMNCAH index vs poverty levels by county, 2014
22 | P a g e
Kajiado
Nairobi
Kirinyaga
Kiambu
Meru
Muranga
Lamu
Nyeri
Narok
Siaya
Mombasa
Nakuru
Vihiga
Embu
Homa Bay
Kericho
Nyandarua
Nandi
Bomet
Migori
Kisumu
Nyamira
Garissa
Tharaka Nithi
Trans Nzoia
Uasin Gishu
Laikipia
Bungoma
Kakamega
Taita Taveta
Elgeyo Marakwet
Baringo
Machakos
Kisii
Makueni
Kitui
Busia
West Pokot
Kilifi
Samburu
Isiolo
Kwale
Tana River
Marsabit
Wajir
Mandera
Turkana
20
25
30
35
40
45
50
55
60
RM
NC
AH
Index
(%)
0 10 20 30 40 50 60 70 80 90 100Poverty headcount(%)
A further analysis of health outcomes compared across counties based on poverty headcount looked at
skilled birth attendance, HIV+ve mothers receiving ARVs and stunting levels. Regarding HIV +ve mothers
receiving ARVs, the county comparisons are shown in the figure below Figure 13: % HIV+ve mothers on ARVs vs poverty levels by county, 2014
Kajiado
Nairobi
Kirinyaga
KiambuMeru
Muranga
Lamu
Nyeri
Narok
Siaya
Mombasa
Nakuru
Embu
Vihiga
Kericho
Homa Bay
Nyandarua
MigoriNandi
Bomet
Kisumu
Nyamira
Garissa
Tharaka Nithi
Trans Nzoia
Uasin Gishu
Laikipia
Bungoma
KakamegaTaita Taveta
Elgeyo Marakwet
Baringo
Machakos
Kisii
Kitui
Makueni
Busia
West PokotKilifi
Samburu
Isiolo
Kwale
Tana River
Marsabit
Wajir
Mandera
Turkana
010
20
30
40
50
60
70
80
90
100
% o
f H
IV+ve
moth
ers
rece
ivin
g A
RV
s
0 10 20 30 40 50 60 70 80 90 100Poverty headcount(%)
23 | P a g e
We see the same NAL Counties performing poorly equitably, while we see Isiolo, Elgeyo Marakwet also
performing poorly when looking at equity in utilization. We also see Kajiado, Muranga, Nairobi and
Kirinyaga doing well – all counties with high levels of affluence. However, Siaya interestingly does well in
ensuring their coverage of HIV+ve mothers are receiving ARVs as compared to its levels of poverty.
The comparisons for deliveries by skilled birth attendants is shown below
Figure 14: Deliveries by skilled birth attendants vs. poverty levels by county, 2014
Kajiado
NairobiKirinyaga
Kiambu
Meru
Muranga
Nyeri
Lamu
Narok
SiayaMombasa
Nakuru
Vihiga
Embu
Kericho
Homa Bay
Nyandarua
Bomet
Migori
Nandi
Nyamira
Kisumu
Tharaka Nithi
Garissa
Trans Nzoia
Uasin GishuLaikipia
Bungoma
Kakamega
Taita Taveta
Elgeyo Marakwet
Baringo
Machakos
Kisii
Kitui
Makueni
Busia
West Pokot
Kilifi
Samburu
IsioloKwale
Tana River
Marsabit
Wajir
Mandera
Turkana
0.2
.4.6
.81
1.2
% D
eliv
erie
s co
nduc
ted
by s
kille
d at
tend
ant i
n H
ealth
faci
litie
s
10 20 30 40 50 60 70 80 90 100Poverty headcount(%)
The NAL counties of Mandera, Turkana, Wajir and Marsabit again show very low levels of deliveries,
compared to their high poverty headcount. On the other hand, Kiambu county is interesting, with a high
level of deliveries as compared to its poverty headcount. This suggests more issues are at play that are
leading to higher utilization of services.
The final variable we analyse is for levels of stunting. This represents a more long term indicator of
wellbeing, with high levels suggestive of long term malnutrition prevalent in the county. Comparing
levels of stunting with poverty levels should show increasing stunting with increasing levels of poverty.
The county picture is shown in the proceeding figure.
The pattern we see is slightly different, from the health outcome data we have been showing. Turkana
clearly has lower levels of stunting than would be expected given its poverty levels. We also see Kitui
and Laikipia counties having higher than expected levels of stunting, suggesting some issues are going
on there that need further investigation. On the other hand, Kisii, Elgeyo Marakwet, Trans Nzoia and
Nyandarua counties all have lower than expected levels of stunting, also suggesting some events are
taking place in these counties that are giving them better results than what would be expected for their
levels of poverty.
24 | P a g e
Figure 15: % children stunted vs poverty levels by county, 2014
Kajiado
Nairobi
Kirinyaga
Kiambu
MeruMuranga
Lamu
Nyeri
Narok
Siaya
Mombasa
Nakuru
Vihiga
Embu
Homa Bay
Kericho
Nyandarua
Nandi
Migori
Bomet
Nyamira
Kisumu
Garissa
Tharaka Nithi
Trans Nzoia
Uasin Gishu
Laikipia
KakamegaBungoma
Taita Taveta
Elgeyo Marakwet
Baringo
Machakos
Kisii
Makueni
Kitui
Busia
West Pokot
Kilifi
Isiolo
Samburu
KwaleTana River
Marsabit
Wajir
Mandera
Turkana
0
.01
.02
.03
.04
.05
.06
.07
% C
hild
ren
<5 y
ears
Stu
ntin
g 0-
59
mon
ths
2013
/14
0 10 20 30 40 50 60 70 80 90 100Poverty headcount(%)
25 | P a g e
CHAPTER 4: CONCLUSIONS AND RECOMMENDATIONS
From the data and analysis, we can infer the following:
1. The sector appears to have no significant increases in investments during the period under
review. It was however characterized by accelerated implementation of the constitution,
particularly devolution which changed the characteristics of determining and financing sector
priorities
2. There were significant investments made across the different investment areas of the sector,
though these were primarily focused in specific, visible areas relating to improving access to
services (physical, financial access) There were minimal investments in other required output
areas, particularly in quality of care
3. The resultant improvements in health outcomes appear to be skewed towards emergency /
acute / RMNCAH services, which may not specifically impact significantly on overall health goals
but is good for the health goals for the target populations
4. There are major efficiency gaps in the health sector, which if addressed can significantly increase
available resources, and improve on the health outcomes for the people in Kenya. Counties are
utilizing health resources with levels of efficiency that are staggeringly different
5. The NAL counties still exhibit significant equity in investments and outcomes for health
We therefore make the following key recommendations to improve on health sector performance
i) Improvement in financing of critical health investment areas, particularly those relating to
improving quality of care is needed
ii) The sector should focus more keenly on improving efficiency in the utilization of available
resources, focusing on the counties with the lowest relative efficiency values
iii) More efforts should be placed at improving the levels of investments in health in the NAL
counties. The current levels of investments are grossly inequitable when compared with
other counties, and it is impacting on the rights of people in these counties.
26 | P a g e
BIBLIOGRAPHY
[1] Government of Kenya, “Annual Health Sector Performance Report, July 2013 - June
2014,” 2014.
[2] Government of Kenya, “Kenya Service Availability and Readiness Assessment Mapping,
2013,” 2014.
[3] Government of Kenya, “Economic Survey, 2014,” 2014.
[4] Government of Kenya, “Monitoring the implementation of Free Maternity Services in the
devolved health system of Kenya,” 2014.
[5] Government of Kenya, “State of Health Service Delivery: Assessment report for primary
care facilities,” Nairobi, Kenya, 2014.
[6] Government of Kenya, “The state of Health Service Delivery: Assessment report for
hospitals,” Nairobi, Kenya, 2014.
[7] Government of Kenya, “Employee, Work Environment and Client Satisfaction Survey,
2014,” Nairobi, Kenya, 2014.
[8] Kenya National Bureau of Statistics, “Economic Survey 2014,” Nairobi, Kenya, 2014.
[9] A. Charnes, W. W. Cooper, and E. Rhodes, “Measuring the efficiency of decision making
units,” Eur. J. Oper. Res., vol. 2, no. 6, pp. 429–444, 1978.