measuring health system efficiency in low- and middle

MEASURING HEALTH SYSTEM EFFICIENCY IN LOW- AND MIDDLE-INCOME COUNTRIES: A RESOURCE GUIDE

Revisiting Health Financing Technical Initiative Efficiency Collaborative

Resource Guide Disclaimer

This Resource Guide was produced by the Joint Learning Network for Universal Health Coverage (JLN), an innovative learning platform where practitioners and policy makers from around the globe co-develop global knowledge that focuses on the practical “how-to” of achieving universal health coverage. For questions or inquiries about this manual or other JLN activities, please contact JLN at [email protected] or Naina Ahluwalia at [email protected].

The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Nothing herein shall constitute or be considered to be a limitation upon or waiver of the privileges and immunities of The World Bank, all of which are specifically reserved.

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Suggested citationHafez, R., ed. Measuring Health System Efficiency in Low- and Middle-Income Countries: A Resource Guide. Joint Learning Network for Universal Health Coverage, 2020

MEASURING HEALTH SYSTEM EFFICIENCY IN LOW- AND MIDDLE-INCOME COUNTRIES: A RESOURCE GUIDE iii

Acknowledgments

The Resource Guide was co-produced with the following country members of the JLN Efficiency Collaborative, and facilitated by the Joint Learning Network’s Efficiency Collaborative technical team.

TECHNICAL EDITORReem Hafez

COUNTRY CONTRIBUTORS:BangladeshAyesha Afroz Chowdhury, Ministry of Health & Family Welfare.Subrata Paul, Ministry of Health & Family Welfare.EthiopiaMideksa Adugna, Oromiya Regional Health Bureau.GhanaKwabena Boakye Boateng, Ghana Health Services.Kwakye Kontor, Ministry of Health.Titus Sorey, National Health Insurance Authority.Vivian Addo-Cobbiah, National Health Insurance Authority,.IndiaKavita Singh, Ministry of Health and Family Welfare.IndonesiaAhmad Ansyori, Council of National Social Security (DJSN).Eka Yoshida, Ministry of Health.Pandu Harimurti, The World Bank.MongoliaB. Munkhtsetseg, Ministry of Health.NigeriaBolaji Aduagba, Federal Ministry of Health.Kayode A. Obasa, Ministry of Budget and National Planning.Nneka Orji, Federal Ministry of Health.PhilippinesAndrea Margreth S. Ora-Corachea, Department of Health.Lavinia A. Oliveros, Department of Health.Martha de la Paz, Department of Health.Paulus Magnus Bacud, PhilHealth.VietnamNguyen Khanh Phuong, Health Strategy and Policy Institute.

Special thanks are owed to the contributors of the two country pilots:Kenya team: Ahmed Omar, Ministry of Health, Esther Wabuge, JLN CCG Coordinator, Isabel Maina, Ministry of Health, Kenneth Munge, KEMRI Wellcome Trust, Mercy Mwangangi, Ministry of Health

Malaysia team: Abdul Hakim Bin Abdul Rashid, Ministry of Health, Lee Kun Yun, Ministry of Health, Mohammad Najib Bin Baharuddin, Ministry of Health, Nor Izzah binti Hj. Ahmad Shauki, Ministry of Health, Yussni Binti Aris A. Haris, Ministry of Health

Other country contributors to the Guide include: Md. Abul Bashar Sarker, Md. Anwar Sadat and Md. Sarwar Bari from Bangladesh; Genet Mulugeta Hirpesa, Eyerusalem Animut and Tseganeh Guracha, from Ethiopia; Daniel Adin Darko, Emmanuel Ankrah Odame, and Ernest Asiedu Konadu from Ghana; Alok Saxena and B K Datta from India; Alice Wangui, Anne Musuva, Andrew Mulwa, Cyrus Matheka and Joseph Githinji from Kenya; Noraryana Bt Hassan from Malaysia; A. Khishigbayar, Bayanjargal Ganbol, L. Munkhzul and Ts. Tsolmongerel from Mongolia; Okechukwu Okwudili, Oritseweyimi Ogbe, Shamsudeen Saad and Uchenna Eugenes Ewelike from Nigeria; Arturo Alcantara and Israel Pargas from the Philippines; Nguyen Lan Huong from Vietnam

The Revisiting Health Financing Technical Initiative team would like to thank the Country Core Groups across JLN countries, as well as the JLN Steering Group for continued support for leveraging existing resources for health, as ‘efficiency’ is a very high technical priority for the JLN.

Valuable feedback was received by the Health Financing Global Solutions Area under the leadership of Christoph Kurowski and other members of the World Bank HNP Leadership team. The technical facilitation team wishes to express special thanks to David Wilson for chairing the peer review process and Owen Smith, David Evans and Laura Di Giorgio for their time and effort in reviewing the guide.

The Resource Guide could not have been co-produced without the financial cooperation of the Government of Japan, the Rockefeller Foundation, the Bill and Melinda Gates Foundation and Australian Aid. The International Decision Support Initiative (iDSI), represented through the Center for Global Development (CGD), were technical partners to the World Bank for facilitation of the Efficiency Collaborative. Management Sciences for Health (MSH) provided continued support and external perspective as the JLN Network Manager.

A special thanks also to the World Bank JLN focal points from Bangladesh, Ethiopia, Ghana, India, Indonesia, Kenya, Malaysia, Mongolia, Nigeria, Philippines and Vietnam in facilitating and encouraging this work.

The Guide technical facilitation team was guided by Reem Hafez from the World Bank and included Somil Nagpal, Danielle Bloom, Naina Ahluwalia and Lydia Ndebele. Melissa Ouellet provided research and data support. Dorothee Chen, Marvin Ploetz, Alessia Thiebaud, Moritz Piazzi, Federica Secci, Manuela Villar Uribe provided data and input to several fact sheets. Ruth Young and QUO Global provided editing, data visualization, layout and production support.

MEASURING HEALTH SYSTEM EFFICIENCY IN LOW- AND MIDDLE-INCOME COUNTRIES: A RESOURCE GUIDEiv

Genesis of the efficiency collaborative

The Joint Learning Network (JLN) is a global network connecting practitioners and policy makers from 31 countries around the globe to co-develop knowledge that focuses on the practical ‘how-to’ of universal health coverage reform in low- and middle-income country contexts.

Over a series of consultations, JLN members reported that one of the most common issues facing their health care systems was how to assess the efficient use of resources. As a result, the Efficiency Collaborative (EC) was launched in April 2017. Participants from 11 JLN member countries (Bangladesh, Ethiopia, Ghana, Indonesia, Kenya, Malaysia, Mongolia, Nigeria, Philippines, Sudan, Vietnam) decided on two strategic work streams:

i) the measurement and information stream (MIS) which aims to provide a framework for identifying and measuring efficiency, and

ii) the systematic priority setting stream (SPS) which aims to support countries in maximizing their stated health sector priorities within a given resource envelope.

There are three complementary products tied to these workstreams – Health Priority Setting: A Practitioner’s Handbook* and a Health Priority Setting and Resource Allocation Tool and Database** under the SPS stream – and this guide, which introduces readers to the concept of efficiency and gives guidance on how to assess efficiency in a practical setting. It includes a list of indicators most often used for tracking health system performance and several ‘fact sheets’ in Annex 2 meant to inform practitioners on how best to use, visualize, and interpret each indicator.

The development of this guide and the final list of proposed indicators was the result of an iterative process between technical facilitators and country participants over four in-person meetings, two webinars, and individual discussions with four countries testing out the validity and feasibility of the proposed approach.

Health Priority Setting: A Practitioner’s Handbook

Health Priority Setting and Resources Allocation Benchmarking Database

* https://www.jointlearningnetwork.org/resources/health-priority-setting-a-practitioners-handbook/.

** https://www.jointlearningnetwork.org/resources/health-priority-setting-and-resource-allocation-tool/

MEASURING HEALTH SYSTEM EFFICIENCY IN LOW- AND MIDDLE-INCOME COUNTRIES: A RESOURCE GUIDE v

Acknowledgments iii

Genesis of the efficiency collaborative iv

Introduction 1

Concepts and principles of efficiency 7

A framework for measuring efficiency: a benchmarking plus approach 9

Why this approach? 13

From indicators to decision making 15 Conclusion 23

References 24

Annex 1: Indicator lists reviewed 25Annex 2: Indicator fact sheets 26

CONTENTS

MEASURING HEALTH SYSTEM EFFICIENCY IN LOW- AND MIDDLE-INCOME COUNTRIES: A RESOURCE GUIDEvi

List of boxes, figures and tables

BoxesBox A What are the main sources of inefficiency in health? 1

Box B What makes health spending particularly prone to inefficiency? 2

Box C A hypothetical application 11

Box D What potential sources are available to collect information for efficiency analysis? 16

Box E Kenya pilot 18

Exhibit E.1 Ratio of nurses per 100,000 population by county (latest year available) 19

Exhibit E.2 Maternal health coverage indicators, %, (2014–2018) 19

Exhibit E.3 Maternal service readiness index by county (2013) 19

Exhibit E.4 Maternal service readiness index by ownership and tier of care (2013) 19

Box F Malaysia pilot 20

Exhibit F.1 Diabetes prevalence (%), 2017 21

Exhibit F.2 Diabetes care cascade in Malaysia (absolute number and %), 2017 21

Exhibit F.3 Adherence to clinical practice guidelines for diabetes mellitus in Malaysia (%), 2017 21

FiguresFigure 1 Ways to benchmark 8

Figure 2 Life Expectancy vs. Government Health Expenditure per capita (2014) 9

Figure 3 The results chain or theory of change 10

Figure 4 Using data to inform decision making 15

TablesTable 1 Common indicators used to assess efficiency in hospital and pharmaceutical sub-systems 13

Table 2 Common indicators that can be used in an efficiency analysis 14

MEASURING HEALTH SYSTEM EFFICIENCY IN LOW- AND MIDDLE-INCOME COUNTRIES: A RESOURCE GUIDE 1

Universal health coverage (UHC) will inevitably require governments to find additional resources for health; and efficiency is critical to achieving that objective. Health expenditure growth1 has exceeded gross domestic product (GDP) growth in nearly every country in the world over the past two decades. While there are several ways in which additional fiscal space for health can be met,2 the pressure to extract greater value for money from health spending is mounting as health systems increasingly absorb a larger share of government, employer, and household incomes. But perhaps more important than how much countries are spending on health is whether they are using their limited resources efficiently (World Bank, 2019). The 2010 World Health Report highlighted that between 20–40% of all resources spent on health are wasted. It identified human resources, hospitals, and pharmaceuticals as the biggest sources of inefficiency (Box A) which result in potential savings of ~USD 1.2 billion3 globally from these sources alone (Chisholm & Evans, 2010; Couffinhal & Socha-Dietrich, 2017). A more comprehensive description of sources of inefficiency can be found here.4

Box A: What are the main sources of inefficiency in health?

The main sources of inefficiency can be grouped into two types:

1. Inefficiencies arising from system-level resource allocation decisions – from poor planning or slow response to the changing health needs of the population:• Inappropriate or costly input/staff mix;• Inappropriate hospital size;• Sub-optimal deployment of health workers and facilities.

2. Inefficiencies that result from facility- or physician-level decisions linked to poor incentives or lack of accountability measures:• Inappropriate hospital admissions or length of stay;• Over-use of health care technology;• Sub-optimal quality of care and medical error;• Under-use of generic drugs;• Irrational use of drugs.

1 Health expenditure growth is driven by rapidly ageing populations, growing burdens of noncommunicable and chronic diseases, technological progress, and rising population expectations.

2 A parallel JLN Collaborative on Domestic Resource Mobilization for health covers four of these methods in greater detail. In short, they are: i) conducive macroeconomic conditions and increases in overall government revenue; ii) earmarking and specific commitment devices for health; iii) re-prioritization of health in the overall budget; and iv) access to health sector grants and donor aid.

3 Inflated to current 2019 USD from USD 965 billion in 2009.4 http://pubdocs.worldbank.org/en/616561494511379987/UHC-Efficiency-Background-Paper-V10-170419-1100.pdf0.

Introduction

MEASURING HEALTH SYSTEM EFFICIENCY IN LOW- AND MIDDLE-INCOME COUNTRIES: A RESOURCE GUIDE2

Box B: What makes health spending particularly prone to inefficiency?

There is a common perception amongst Ministries of Finance and Budget and Planning that health is more inefficient than other sectors. However, there are many factors that are beyond the control of the Ministry of Health that impact health system efficiency:i. Uncertainty: The demand for health services is unknown. It is difficult to predict when and where people get

sick. This makes the planning and management of resources challenging. Public financial management (PFM) systems often restrict the ability to make mid-year adjustments to respond to the changing health needs of the population.

ii. Information asymmetry: The average patient generally has less information than providers about the need for and quality of health care. This leads to complicated principal-agent relationships in which a patient (the principal) delegates decision making authority to a better-informed doctor (the agent). The assumption is that the doctor will act in the best interests of the patient, but this can sometimes be at odds with the doctor’s own interest of wanting to maximize income. The end result is an increase in unnecessary (and total) health expenditures.

iii. Difficulty in linking inputs to outcomes: Health outcomes are more difficult to observe than outcomes in other sectors (e.g., a road being built or the number of students completing secondary education). In health, budgeting is often linked to inputs such as human resources or infrastructure (e.g., hospitals) and not to what is being purchased or delivered (e.g., health care services or treatment) and even less to health outcomes (e.g., health status or financial protection). Budget format and PFM rigidities also mean there is little flexibility to move funds around between line items.

iv. Fragmented financing: The health sector in low- and lower-middle income countries is marked by the presence of several specific programs (such as HIV/AIDS, malaria, tuberculosis, immunization, and family planning). Many of these programs are funded through external assistance. Often, this means that there are multiple donors present in the sector; health funds do not always flow through government budgets; and there is a lack of clarity on the full resource envelope. This not only poses a challenge for budgeting and planning but also leads to the duplication and waste of resources, the misalignment of resources with country priorities, and the use of parallel procurement, financial management, and monitoring and evaluation systems – placing a significant burden (financial and time) on the Ministry of Health.

This compromises Ministry of Health officials’ ability to argue for additional resources for the sector. The JLN’s Domestic Resource Mobilization Collaborative is producing a forthcoming messaging guide on how to make the case for health. It aims to facilitate discussion between Ministry of Health and Ministry of Finance officials highlighting the particularities of the health sector.

5 Stakeholders involved in the priority setting or benefit package selection process may include Ministries of Finance, Ministries of Health, health insurance agencies, market authorization and quality control agencies for drugs, national procurement agencies, professional medical associations, health technology agencies or academia, pharmaceutical lobbies, industry, the media, and the public.

Some inefficiencies will be easier to address than others. Several characteristics of the health sector make health spending particularly prone to inefficiency. Uncertainty in the demand for health, informational asymmetries between patients and providers, difficulties in linking inputs to outcomes, and fragmented sources of financing often lead to lower actual spending on health than the allocated budget (Box B). Improving allocative efficiency is generally more difficult, as many stakeholders outside the Ministry of Health are involved and decisions are often constrained by civil servant regulations and budget rigidities.5 The SPS produced a Handbook and a Resource Allocation Benchmarking Database to help tackle these issues. In comparison, addressing inefficiencies that result from facility- or physician-level decisions are more straightforward as accountable entities are easier to identify and fall directly under the supervision of the Ministry of Health (for example, regional hospital managers, hospital managers, hospital departments, or individual practitioners). It is the latter type of inefficiency that this guide focuses on.

While defining efficiency is relatively simple, measuring efficiency is challenging. The most basic definition of efficiency is maximizing outcomes relative to inputs. Therefore, the first key characteristic of efficiency analysis is that it requires information on both inputs and outputs. Efficiency analysis attempts to explain the unexplained variation across accountable entities – that is, why some individual providers, facilities, or health systems perform better than others. Therefore, a second key characteristic of efficiency analysis is that it involves making a comparison. The standard methods for economists to assess the efficiency of health care systems – stochastic frontier analysis (SFA) or data envelopment analysis (DEA) – empirically measure the productive efficiency of country health systems at the macro-level, or facilities at the micro-level. They take a production function approach, modeling the maximum attainable output achieved given a set of inputs. Yet, they struggle to produce actionable policy recommendations on how to improve efficiency. There are notable challenges to this approach. First, efficiency scores and rankings relative to the best performers are highly sensitive to model specification and the data used. Second, the results rarely help policy makers identify how they can improve efficiency, as efficiency scores – on their own – do little to tell a policy makers why the health system is inefficient.6 Finally, they are data intensive, time consuming, and require particular expertise. In low- and middle-income countries SFA and DEA are almost always carried out by external consultants as a one-off exercise, which does not help institutionalize routine monitoring of the efficiency of health care resources.

This guide proposes an alternative ‘benchmarking plus’ approach that can be readily used by practitioners and policy makers – the primary audience of the Efficiency Collaborative. The focus is less on empirical measurement and more on enabling the routine assessment of health system performance from an efficiency perspective. Benchmarking is simple to perform and easy to interpret; but on its own, it too will not tell policy makers how to improve efficiency. To be able to pinpoint the cause of variation, it will almost always be necessary to look at several indicators along the process of how inputs get transformed into particular outcomes and complement that with additional contextual knowledge in order to identify the appropriate policy action.

This guide gives a brief overview of concepts and principles of efficiency, and provides a framework for identifying and measuring efficiency in a practical way. It provides a list of indicators most often used for tracking health system performance and gives guidance on how they can be used to measure efficiency. It does not try to reiterate the work of the more comprehensive references from which it draws (World Bank, 2017) (Smith, 2012) (Cylus, et al., 2016) (Papanicolas & Smith, 2017) but rather attempts to operationalize the approach first suggested by (Smith, 2012)7 for low- and middle-income country settings.

6 Health systems have many goals – be it maximizing health status, financial protection, and/or patient satisfaction. There are also many sub-systems or sectors that contribute to the efficiency of the overall health system – such as preventive and promotive health, primary health care, the hospital sector, and the procurement, distribution, and use of pharmaceuticals and medical equipment. One part of the system may operate better than others.

7 Smith describes a “partial indicator” approach, which suggests benchmarking indicators across the entire results chain from inputs, to outputs, and outcomes.



A BENCHMARKING PLUS APPROACH TO EFFICIENCY ANALYSIS

There are two key characteristics of efficiency analysis:1. Efficiency requires information on both inputs and outcomes

The most basic definition of efficiency is

MAXIMIZING

relative toOUTCOMES INPUTS

OUTCOMESINPUTS

If only inputs, the analysis is a resource mapping exercise that answers the question “What resources do I have and how are they distributed?”

If only outcomes, the analysis is an effectiveness analysis that answers the question “Does this intervention work?” or “Is it doing what it is supposed to do?”

Efficiency analysis

Looks at both inputs and outcomes and answers the question “Are we achieving maximum benefits at cheapest cost given our resources?”

2. Efficiency involves making a comparison

Benchmarking makes a comparison based on average performance, relative to the best performer, relative to a clinical norm or target, or relative to past performance.

Efficiency analysis attempts to explain the unexplained variation across accountable entities – that is why some individual providers, facilities, or health systems perform better than others.


OUTPUTS OUTCOMESSERVICE

DELIVERYINPUTS$

Inefficiencies can occur at any point in the results chain which transforms inputs into outcomes

Benchmarking indicators along an entire results chain could help look at service-delivery bottlenecks through an efficiency lens.

Benchmarking is helpful at detecting emerging issues such as stagnating outcomes, outliers, or significant changes from one period to the next. However, on its own, it is not enough.

Policy makers have a range of policy levers to influence the behavior of accountable entities. Together, they affect how resources are allocated to different goods and services and how inputs are used to produce a given set of goods and services.

The ‘plus’ in benchmarking plus…

…allows more actionable policy recommendations

As specific issues emerge, additional indicators and key informant interviews may be necessary to better understand whether an issue is truly an inefficiency or not.

FINANCING PROVIDER INCENTIVES

REGULATIONORGANIZATION

A BENCHMARKING PLUS APPROACH TO EFFICIENCY ANALYSIS (CONTINUED)


Concepts and principles of efficiency

The most basic definition of efficiency is maximizing outcomes relative to inputs.

• Inputs: Most often, people use inputs to refer to the costs, resources, or investments used to buy or produce health care inputs; and while financing for the health care system is one of the most fundamental inputs, others include health workforce (e.g., doctors, nurses, midwives, community extension workers); physical infrastructure (e.g., health care facilities, medical supply stores); drugs and medical products, equipment (e.g., MRI machines), and information – often the most overlooked input (e.g., data on civil registration and vital statistics, disease-specific registries, patient reported health outcomes, drug stocks).

• Outcomes can refer to the consequences, effectiveness, or benefits of service delivery interventions. In most health care systems around the world, the main outcomes of interest concern health status, financial risk protection, and public satisfaction. However, in practice, most efficiency metrics use intermediate outcomes or outputs. Outputs include information on the quantity (e.g., availability, access, coverage) and quality (e.g., diagnostic accuracy, treatment success rates) of the goods and services provided.

A key characteristic of efficiency analysis is that it requires information on both inputs and outcomes. If only inputs are examined, the assessment is essentially a resource mapping exercise. The question being asked is ‘what resources do I have and how are they distributed?’ If only outcomes are examined, the assessment is called an efficacy or effectiveness analysis.8 This asks the question ‘does this intervention work?’ or ‘is this intervention doing what it is supposed to do?’ Efficiency looks at both resources used (inputs) and effectiveness of interventions (outcomes), and assesses:

i) whether the maximum benefit to society is reached given the current use of inputs (‘doing the right things’), and

ii) whether the same benefits to society can be reached at cheaper cost (‘doing things in the right way’).

INPUTS

INTERMEDIATE OUTCOMES OR

OUTPUTS

Health care services

Fina

ncin

g

Quality

Financial protection

Health status

Public satisfaction

QuantityAvailability AccessCoverage

Equipment Physical infrastructure

Health workforce Drugs

Medical products Information

FINAL OUTCOMES

8 Efficacy refers to whether an intervention produces the expected result under ideal circumstances among a particular test group (such as a randomized control trial). Effectiveness refers to whether an intervention is beneficial to the wider public under ‘real world’ clinical settings.


Figure 1: Ways to benchmark

Relative to a clinical/ international norm or target

Before and after

Time

Effic

ienc

y m

easu

re

Out

puts

Inputs

Based on averageperformance

Out

puts

Relative to best performer

Inputs

Ideally, policy makers want to do the right things and do them in the right way. To improve efficiency, decision makers can either:

• Reorganize the current level of inputs (direct resources to different priorities), or adjust how services are delivered to increase benefits, which is sometimes referred to as allocative efficiency; or

• Find a way to produce the same amount of benefit but at a cheaper cost, which is sometimes referred to as technical efficiency.

A second key characteristic of efficiency analysis is that it involves making a comparison. To identify whether resources are being used efficiently, there needs to be some standard by which a comparison can be made. Benchmarking makes comparisons based on average performance, relative to the best performer, relative to a clinical norm or target, or relative to past performance.


Figure 2: Life Expectancy vs Government Health Expenditure per capita (2014)

A framework for measuring efficiency: a benchmarking plus approach

Typically, the first step to benchmarking is to define the scope of the analysis. At the system level, the focus is on how health sector investments are contributing to overall health system objectives. At the sub-system or frontline-provider level, the focus might be on whether spending on broad health categories improves intermediate health outcomes, or on how facility resources are increasing service utilization.

Defining the scope of the analysis will help inform the choice of comparator. This could be countries of similar socioeconomic status or level of health system development; geographic regions or population groups within a country; or individual facilities or providers. Internal benchmarking is likely the most relevant for countries as understanding distribution across regions, hospitals, clinics, and population groups can help policy makers and program managers target resources more effectively.

While benchmarking can help identify outliers or raise flags, looking at discrete indicators does not tell policy makers how to improve efficiency. Figure 2 assesses efficiency at the health system level by plotting government health spending versus the average life expectancy of several countries. Countries can assess their efficiency relative to the average (red line). However, there are a number of drawbacks to this approach. First, it focuses on a single input (government health spending) and a single outcome (life expectancy) but many other inputs contribute to life expectancy, and the health system may have many other important outcomes of interest. Second, it does not tell us why for the same level of expenditure, one country is performing better than another given the same level of government spending (for example, Nigeria vs. Sudan). Information on the processes required to translate inputs into outcomes is missing; this is sometimes referred to as the ‘black box’ of service delivery.

Source: (World Bank, 2019)

100 250 500 1,000 2,500 10,000

GGHE Per capita, PPP

85

75

65

55

45

Life

exp

ecta

ncy

System level Frontline-provider levelSub-system level

INPUTS INPUTS INPUTSOUTCOME OUTPUT OUTPUT

Total health spending

Spending on primary health care

Number of specialists

Life expectancy

Immunization coverage

Number of admissions

Sudan

Philippines

Kenya

Malaysia

Vietnam

Indonesia Mongolia

Bangladesh

Ethiopia

Ghana

Nigeria


Inefficiencies can occur at any point in the results chain that transforms financing into outcomes. Instead of focusing on financing and final outcomes – the two ends of the results chain – it is more informative to benchmark indicators along an entire results chain to help pinpoint areas of inefficiency. Indicators that provide information on the management of resources, and on access to and quality of services are especially informative. Pinpointing areas of inefficiency will help narrow down the appropriate accountable entity for follow-up action – for example, regional hospital managers, hospital departments, or individual practitioners.

It is recommended to have discussions with key informants who can provide additional contextual knowledge and help get a sense of the relevance and extant of a particular source of inefficiency. They can help fill in the gaps when data is unavailable, incomplete, or unreliable, and be the starting conversation for thinking through appropriate policy actions and helping to determine which areas to tackle first. Addressing inefficiency is politically complicated, as there will be winners and losers in the process. This highlights the importance of having a transparent process around prioritization. For more information, see here,9 and here.10

Having identified the areas of inefficiency, policy makers have a range of policy levers to influence the behavior of accountable entities. We use the systems framework presented in listing financing, provider incentives, organization, and regulation as the key policy instruments to improve efficiency (Yip & Hafez, 2015).

i. Financing is one of the most powerful levers for influencing the efficient delivery of care, especially through the design of the benefits package. For example, a common practice for health insurance schemes in South and East Asian countries has been to focus on low probability, high-cost inpatient care over high probability, low-cost outpatient care, reasoning that catastrophic and impoverishing health expenditures are more likely to occur from expensive hospital services. But by covering only hospital care, the incentive is to admit patients even for simple conditions. Instead, offering free health promotion activities and primary health care can prevent the development of more serious and costly conditions. Another example of a financing lever is tiered cost-sharing arrangements that incentivize the use of lower-level facilities over tertiary hospitals (such as higher co-payments at hospitals versus primary health care facilities).

ii. Provider incentives – financial or otherwise – are another powerful policy lever that influences the efficiency (and quality) of care. For example, in prospective and bundled payments (such as capitation and case-based payments) greater risk is borne by the provider, incentivizing them to manage resources more efficiently. However, they may also incentivize other undesirable behaviors such as skimping on costs and avoiding chronic or sicker patients. For these reasons, most countries use a combination of payment methods to balance out the strengths and weaknesses of various methods. Non-financial provider incentives include relative social ranking, frequent interim feedback, and information availability. For more on provider payment methods please see here.11

iii. Organization covers a broad set of policies for managing and coordinating the delivery system. For example, for clinical interventions and diagnostic services, purchasing from both the private and public sector reinforces competition, and generally leads to lower prices and improved quality where effective regulatory and monitoring capacity are in place. But for goods and services with low-profit margins (for example, public health interventions), the public sector is recommended to ensure the socially efficient level of provision as these types of services are labor intensive, have low-profit margins, and are generally under-provided by the private sector. Also, policies that encourage disease prevention, gatekeeping, coordinated care, and a shift from hospital-based to ambulatory care help promote the cost-effective use of care.

iv. Regulation involves setting the rules, standards and operating guidelines within which the system is meant to operate. These may include medical and clinical protocols, national essential drug lists, procurement

Figure 3: The results chain or theory of change


DELIVERYINPUTS$

9 https://www.jointlearningnetwork.org/resources/health-priority-setting-a-practitioners-handbook10 https://www.jointlearningnetwork.org/resources/health-priority-setting-and-resource-allocation-tool11 https://www.jointlearningnetwork.org/resources/assessing-health-provider-payment-systems-a-practical-guide-for-countries-w/


Box C: A hypothetical application

By benchmarking several indicators across the entire results chain, JLNotopia hopes to better understand why it underperforms on infant mortality compared to countries with similar government health spending.

To narrow down the cause of high infant mortality, JLNotopia needs to look at other indicators such as availability of providers and capacity to deliver immunization services.

The problem does not appear to be staff numbers.

Government health

expenditure per capita

Nurses and midwives per 1,000 people

Percent of fully vaccinated children

Infant mortality rate

Immunization specific readiness

index• Availability of guidelines• Number of trained staff• Equipment availability• Drug availability


DELIVERYINPUTS$

Nurses and midwives

100 250 500 1,000 2,500 10,000


20

10

5

2.5

1.2

.6

Per 1

,000

birt

hs

100 250 500 1,000 2,500 10,000


80

40

20

10

5

Rate

per

1,0

00 li

ve b

irths

Infant mortality


However, facilities have on average only 42% of all service-specific readiness requirements for immunization. Low availability of drug and vaccines and a lack of staff trained in child immunization are likely impacting service delivery.

As a result, the number of fully vaccinated children is low, and the infant mortality is high.

Primary health centers

By benchmarking several indicators across the entire results chain the analysis was able to pinpoint issues in the supply chain and in provider training. However, there is not enough information to know what specifically in the supply chain or training of providers is the issue. Taking supply chain as an example, the issue could be poor inventory management and capacity to forecast needed drugs, in which case the implementation of an inventory management or tracking system may be a solution. Or, the issue could be delays in the distribution of medicines, in which case outsourcing the distribution function to third parties might yield better outcomes. The ‘plus’ in the ‘benchmarking plus’ approach refers to the fact that it will almost always be likely to dig deeper. It might be necessary to look at additional indicators used to measure supply chain efficiency (for example, unit price of vaccines compared to international reference, time to process orders, number of days items were unavailable, etc.) and/or interview key informants at the national procurement agency, medical storage centers, and facilities to narrow down the right accountable entity for follow-up action. Only then can a conversation be had on how to reprioritize resources towards the right intervention based on a country’s prioritization process.

42%

42%

0% 20% 40% 60% 80% 100%

78%

31%

82%

77%

15%

13%

78%

78%

34%

34%

33%

32%

24%

5%

31%

22%

Overall

Guidelines for child immunizationStaff trained in child immunization

Cold box/vaccine carrier with ice packs

Refrigerator

Sharps container/safety box

Auto-disposable syringes

Temperature monitoring device in refrigerator

Adequate refrigerator temperature

Immunization cards

Immunization tally sheets

Staff and guidelines

Equipment availability

Measles vaccine

DPT-Hib+HepB vaccine

Oral polio vaccine

BCG vaccine

Pneumococcal vaccine

Rotavirus vaccine

IPV (Inactivated Poliovirus vaccine)

HPV (Human Papillomavirus)

Drug/vaccine availability

Children

% fully vaccinated* IMR per 1,000 live births

Residence

Urban 40.5 57

Rural 14.8 88

Wealth quintile

Lowest 4.4 90

Highest 56.1 51

Total 25.7 70.3

Note: *BCG, measles, and three doses each of DPT and polio vaccine

For a more in-depth discussion of various policy instruments to address inefficiencies, see here.12

guidelines for hospital equipment and drugs, quality control of drugs, manuals for claims management and audit (including fraud detection), certification and accreditation of facilities, licensing of the health workforce, price controls, and private sector service delivery.

12 http://pubdocs.worldbank.org/en/616561494511379987/UHC-Efficiency-Background-Paper-V10-170419-1100.pdf


Why this approach?

The approach reflects the realities of many low- and middle-income countries attempting to measure health system efficiency. Every JLN deliverable is co-produced and driven by country participants over a series of face-to-face meetings. Technical experts help facilitate meetings to produce an end product that is based on topical expertise and country demand. The initial request was to produce a list of indicators to routinely assess the efficiency of health sector spending, especially in areas that are known to be major sources of inefficiency and consume the most resources, such as hospitals and pharmaceuticals (Table 1). However, many ‘true’ efficiency measures that combine inputs and outputs in a ratio, such as average consultations per doctor or unit cost per episode of care, are not readily available. Even if data is available, collection is difficult as datasets are often paper-based and housed in different units or administrative levels requiring a significant investment to standardize, aggregate, and clean data for analysis. In the absence of clear-cut efficiency metrics, benchmarking health system performance indicators along a results chain could help look at service delivery bottlenecks through an efficiency lens.

Table 1. Common indicators used to assess efficiency in pharmaceutical and hospital sub-systems

Pharmaceuticals Hospitals

• Pharmaceutical spending as % of total health expenditure (THE)

• Antibiotics spending as % of total pharmaceutical spending

• Unit price of drugs/medical consumables• Unit price compared to international reference

prices (especially for high-cost/use items)• Cost of freight/distribution to facilities• Order/use of high-cost items• High-use items• Number or % of expired items• Value of expired items • Stock-outs• Antibiotic prescription rates• Percent of encounters that end up in antibiotics

being prescribed• Time to process orders• Time to pay suppliers• Drug availability• Rate of anti-microbial resistance

• Spending by function (e.g., outpatient, inpatient, pharmaceutical, primary health care, public health or prevention, curative care) as % of government health expenditure (GGHE)

• Hospitals per 100,000 population, hospital bed density, bed occupancy rate

• General service readiness • Number of visits/admissions per day/month/year/per capita• Share of outpatient/inpatient• Diagnostic accuracy for tracer condition• Adherence to clinical guidelines• Number of incidents per 1,000 patient days

(e.g., center line-associated bloodstream infections, standardized infection ratio)

• Avoidable admissions for chronic obstructive pulmonary disease (COPD), asthma, hypertension, diabetes

• Referral rate• Average length of stay • Readmission rate• Caesarean section (C-section) rates

While many of the indicators considered are more commonly used to assess health system performance, any health system performance indicator can be viewed from an efficiency angle. For example, quality indicators such adherence to clinical guidelines, diagnostic accuracy, and treatment success rates would become the outputs, and a doctor’s visit or consultation the input. These could be benchmarked against a target (such as 100% diagnostic accuracy) relative to other providers, or compared to own past performance. In this way, indicators help fulfill the two key characteristics of efficiency analysis – first, it considers both inputs and outcomes and second, it involves making a comparison.


Table 2. Common indicators that can be used in an efficiency analysis

Financing (management)

Inputs (management)

Outputs (access)

Outputs (quality)

Outputs (risk factors)

Outputs (management)

Outcomes (health status)

Outcomes (financial protection)

• Total health expenditure (THE) as % of GDP or in per capita terms

• Government health expenditure (GGHE) as % of GDP, as % of budget, as % of THE, or in per capita terms

• Share of pre-paid/pooled spending as % of THE; out-of-pocket expenditure (OOP) as % of THE; external funds as % of THE

• THE, GGHE, OOP spending by function (e.g. outpatient, inpatient, pharmaceutical, primary health care, public health or prevention, curative care) as % of THE, as % of GGHE, as % of OOP expenditure, or in per capita terms

• Density of doctors, nurses, midwives

• Hospital density or hospital bed density

• Availability of basic facility infrastructure, essential medicines, equipment

• General services readiness or service-specific readiness for tracer condition(s)

• Service utilization

• Antenatal coverage

• Births attended by skilled health personnel

• Immunization coverage

• TB case detection

• TB treatment success rate

• Diagnostic accuracy for tracer condition(s)

• Adherence to clinical guidelines for tracer condition(s)

• Diabetes control

• % of incidents per 1,000 patient days

• Avoidable admissions

• Raised blood glucose/diabetes among adults

• Children under 5 who are stunted

• Tobacco use among persons aged 18+

• Caseload

• Absenteeism

• TB notification rate

• Average length of stay in hospital

• Bed occupancy rate

• Referral rate

• Hospital readmission rates

• C-section rates

• Expired drugs

• Stock-outs

• Claims ratio

• Budget execution rates

• Life expectancy at birth

• Under-five mortality rate

• Maternal mortality rate

• TB burden

• Mortality between 30-70 years age from cardiovascular diseases, cancer, diabetes or chronic respiratory diseases

• Catastrophic health expenditure

Note: Based on stated country priorities, indicators for immunization and maternal health were used as tracer conditions for assessing efficiency of primary health care, tuberculosis as a tracer condition for assessing efficiency of communicable diseases and a functioning surveillance system, and diabetes as a tracer condition for assessing efficiency of non-communicable diseases. Maternal health and diabetes can also be used as proxy conditions for assessing the efficiency of a functioning referral or integrated system.

The final list of indicators presented in this guide reflects a balance of current data availability in participating countries as well as some aspirational indicators. There are many health indicator lists that have been developed by international organizations, academics, advocacy groups, and others (including the JLN) grouped for different purposes – for tracking progress towards universal health coverage (UHC), assessing primary health care (PHC), ensuring hospital quality, and/or evaluating health provider payment systems (Annex 1). These existing lists were used as a starting point to compile a consolidated list of over 200 indicators. Indicators were organized according to the levels of the results chain (financing, inputs, outputs, and outcomes) and by domains of interest, such as access to care, quality, management, health status, risk factors, and financial protection. During the collaborative process, countries were asked to narrow down indicators based on:

i) countries’ health system priorities; ii) the availability of data in-country; and iii) the frequency of collection.

Indicators that were not frequently collected included those that covered the quality dimension, as these are more difficult to assess and time consuming to collect via facility surveys, vignettes, or patient record review. Hospital-centric indicators (e.g. average length of stay, bed occupancy rates, avoidable admissions, referrals) were also absent. As a result, the indicators that were initially chosen by participants focused only on financing, outputs related to the access dimension, and outcomes, as definitions were standardized and availability widespread. However, it was important to include indicators that may not be currently collected by health information systems in all participant countries. These are the indicators that will help countries move from assessing ‘coverage’ of key interventions towards assessing ‘effective coverage’. It may also help generate demand for collecting these indicators. Table 2 summarizes the final list of chosen indicators.


Figure 4: Using data to inform decision making

From indicators to decision making

The intention is not to have countries track all indicators listed above. The choice of indicators should be driven by country priorities. Given the mix of country issues and varying level of data availability, countries should view Table 2 as a menu of indicators from which to choose. Figure 4 describes a process for using data to inform decision making. This section provides additional guidance on how to get started. It also includes case studies from two participant countries – Kenya and Malaysia – which have piloted the benchmarking plus approach.

Step 1 – Identifying strategic priorities: As mentioned in the introduction, the main sources of inefficiency have been widely documented in the literature; having discussions with key informants around the top ten can help get a sense of the relevance and extant of a particular source of inefficiency. This can be a starting point for countries thinking through which are the most important in their settings, and which they should tackle first. Addressing inefficiencies can be politically sensitive. Therefore, it is helpful to be transparent around the process. Health sector priorities listed in a National Health Strategic Plan, and routine reviews of health sector performance and budget implementation are natural entry points.

Step 2 – Selecting indicators: Having identified a health priority area, for example, maternal health, focus on choosing the right mix of inputs, outputs, and outcomes that clearly highlight the theory of change across the entire results chain – that is, indicators that are relevant for improving maternal health. For example, spending on maternal health, the number of nurses and midwives, the availability of minimum level of equipment and medicines for basic obstetric care, antenatal coverage, adherence to clinical guidelines for antenatal care activities, diagnostic accuracy for post-partum hemorrhage or hypertensive disorders such as eclampsia and preeclampsia, and maternal mortality.

Identify strategic priorities

Routinely report data

Assess options for

action

Select indicators

Collect data

Interpret the results


Step 3 – Collecting data: Policy makers tend to underestimate the availability of data and evidence in their countries. Routine administrative and health financing data are amongst the most valuable sources of information for conducting efficiency analysis. However, the lack of standardized reporting and accounting formats, the low prevalence of electronic health records and patient reported health outcomes, unreliable internet connectivity, and poor reporting compliance mean that significant effort and investment are needed to collect and process data. Until transactional data, administrative data, and/or health management and information systems are able to routinely generate dashboards or alerts for timely analysis and action by decision makers, countries can rely on several other less-frequent sources such as population and health facility surveys. In the meantime, it is helpful to document any data quality issues (e.g., completeness, validity, timeliness) so that an action plan to improve the quality and use of data for decision making can be implemented. Box D summarizes the main sources of data used in efficiency analysis.

Step 4 – Reporting data: Routine reporting provides the first indication that something may be wrong – reports are pushed to users who are then expected to ask the right follow-up questions to extract meaning from the report and take appropriate action. Ideally, the data reporting process is automated; however, standard reporting formats and regular review meetings to discuss areas of concern and action are also effective.

Step 5 – Interpreting the results: It is important to review and question the data to gain better insight. Fact sheets were developed to help guide practitioners in the use of indicators. For each indicator, the fact sheet provides a simple definition, identifies potential sources of data, suggests potential comparators, and gives guidance on benchmarking and interpretation. This includes suggestions on other indicators that might help provide additional insight. The fact sheets are presented in Annex 2.

Box D: What potential sources are available to collect information for efficiency analysis?

National health accounts, budget documents, and public expenditure reviews/public expenditure tracking surveys are used to assess how public resources – financial, human, in-kind – are allocated and managed and how they flow across administrative levels (e.g., from central government to frontline providers).

Routine or administrative data is generated as part of the operation of the health care system for purposes other than research – typically for administrative reasons or to support care. They include infectious disease surveillance data, medical records, hospital episode data, claims data, and supply chain data on medicines and commodities.

Health facility surveys are used to measure different dimensions of health facility performance supply-side readiness, provider training/knowledge, and provider effort.

Household or population surveys can be used to better understand health-seeking behavior and health status based on household characteristics (such as income, education, location, risk preferences, etc.).

Qualitative surveys or key informant interviews are also valuable instruments to better understand the political, organizational, and institutional environment in which decisions are made and interventions implemented.

The strengths and weaknesses of various data sources are summarized in Annex 1 of the SPS’s Health Priority Setting: A Practitioner’s Handbook.13

13 https://www.jointlearningnetwork.org/resources/health-priority-setting-a-practitioners-handbook/


i. Indicators may not always have a recommended benchmark. Where there are clear recommendations, targets or clinical guidelines, benchmarking provides a nice signpost. For example, stock-out rates should ideally be zero, and C-section rates, of higher than 10% at the population level are not associated with reductions in maternal and newborn mortality. But where there are no set norms, benchmarks should be used with caution. For example, numerous health-spending targets have been recommended – 15% of government budgets, or 5% of GDP. While these targets can serve as global benchmarks, they are usually not helpful for determining appropriate levels of spending at the country level. Many countries spend more than these targets and have yet to provide a basic package of services to their population, while others spend less and achieve near universal levels of population coverage. This again highlights the importance of considering both inputs and outputs when interpreting indicators through an efficiency lens.

ii. It is important to make the right comparisons. Cross-country comparisons and national averages may mask regional variations or socioeconomic inequalities – especially in decentralized contexts. For most output indicators related to service delivery it is best to compare across facility types or providers within a country or region (internal benchmarking) as clinical protocols may vary from country to country. Looking at trends and past performance is also a helpful comparison – especially if there is no obvious comparator – as it informs policy makers on whether there is improvement, no change, or worsening performance.

iii. Inefficiency is difficult to attribute. As already mentioned, benchmarking is helpful for detecting emerging issues such as stagnating outcomes, outliers, or significant changes from one period to the next; however, on its own, it is not enough. Many other factors need to be considered such as time lags, data availability and quality, issues in costing, disease burden, and economies of scale. For example, the cost of service delivery can be much higher to achieve the same outcomes in remote areas that require significant outreach or in districts where the disease burden is more pronounced. As specific issues emerge, additional indicators and key informant interviews may be necessary to delve deeper and understand whether an issue is truly an inefficiency or not. The fact sheets in Annex 2 give additional guidance on benchmarking, choice of comparator, and interpretation for each indicator.

Step 6 – Assess options for action: Once inefficiencies have been identified, decision makers need an action plan to take recommendations forward. This requires having clear accountability processes in place and knowing the responsible unit or budget holder. Most countries reported that they typically collect hundreds of indicators as part of their regular monitoring and evaluation of the health sector – indicators that feed into annual budget reviews, annual program reports, key performance indicator dashboards, and statistical yearbooks. However, few were able to articulate whether and how this information was being used. These ‘reporting’ tools fall shy of the ‘analysis’ that might generate insight to guide policy and decision-making. While participant countries are already undertaking steps 1 through 4, only a few are assessing results, and even fewer are coming up with an action plan to address issues identified.

Ghana, Kenya, Malaysia, and the Philippines volunteered as pilot countries to test the validity and feasibility of the benchmarking plus approach. Boxes E and F summarize the Kenya and Malaysia case studies, respectively, applying the steps from identifying strategic priorities and choice of indicators to assessing options for action.


Box E: Kenya pilot

What was the main issue? Maternal mortality remains a challenge in Kenya, where the maternal mortality rate (MMR) has been stagnant since 1993, prompting the government to set reduction of MMR as a flagship program in the Kenya Health Sector Strategic Plan 2014–2018 (KHSSP). The aim was to reduce maternal mortality by half or decrease from 400 at baseline to 150 maternal deaths per 100,000 live births between 2014 and 2018. In the current KHSSP 2018–2022, the MMR baseline was 250 with a target reduction of 30% (Kenya Ministry of Health, 2018). Participants from Kenya’s JLN team applied the ‘benchmarking plus’ approach to identify inefficiencies in the service delivery chain.

What indicators were used and what were the findings? The team looked at the number of doctors and nurses, access to antenatal care (ANC) services and skilled birth attendance (SBA), and readiness to deliver basic obstetric care services as a proxy for quality of care. According to the 2018 Kenya Economic Report, Kenya has a significant shortage of health workers, exacerbated by poor distribution in the 47 counties in the country. There are 0.25 doctors, 1.82 registered nurses and 3.08 enrolled nurses per 1,000 population compared with the WHO recommendations of 3.0 doctors, 2.6 registered nurses and 5.4 enrolled nurses (Exhibit E.1) (KIPPRA, 2018). The percentage of women attending at least one ANC is high (82%), however, those attending four or more ANC visits was much lower at 49%. Data on SBA stood at 65% (Exhibit E.2). On average facilities only had 32% of items necessary to provide maternal health services with readiness highest at hospitals and private facilities; however, national averages mask wide variations (Exhibits E.3 and E.4).

What were the limitations? Was additional contextual information provided? Regular and up-to-date data for strategic planning, resource prioritization, and routine performance monitoring is not easily available. For example, administrative data does not routinely capture disaggregated data on the availability of personnel, medicines, and equipment, or utilization of services. However, population and facility surveys such as the Demographic and Health Surveys, the Service Availability and Readiness Assessment, and the Service Delivery Indicators were able to provide snapshots at a point in time. In addition, it was shared that only 36% of health facilities conducted audits for maternal deaths between July 2015–June 2016; only 43.7% of providers adhered to clinical guidelines; and only 44.6% of providers were able to manage maternal and neonatal complications based on clinical vignettes (World Bank, 2013). The team also shared that nurse strikes in December 2016 and June 2017 significantly affected the percentage of women receiving ANC and SBA services.

What recommendations are suggested? The main bottleneck happens early in the service delivery chain with workforce shortages. Therefore, next steps should focus on mapping out the distribution of providers, considering the labor market dynamics, and assessing the cost-effectiveness of various strategies, such as curriculum reform in medical schools and training, to increase the supply and retention of primary health care workers; introducing new and alternative cadres of health workers including options for telemedicine; monetary and non-monetary incentives for providers working in rural and primary health care practices; and regulatory measures that require workers to practice in remote areas for a certain number of years. In addition, increased supervision and oversight may enhance adherence to clinical protocols, but non-compliance may also be due to overworked personnel given shortages.

Source: Dr. Isabel Maina, Kenneth Munge, Dr. Mercy Mwangangi, Dr. Ahmed Omar, Esther Wabuge


Exhibit E.1: Ratio of nurses per 100,000 population by county (latest year available)

Exhibit E.3: Maternal service readiness index by county (2013)

Exhibit E.2: Maternal health coverage indicators, %, (2014-2018)

Pregnant woman with at least 1 ANC visit

Delivery by skilled birth attendant

Pregnent women with 4 ANC visits

Postnatal care

2014 2015 2016 2017 2018

76.481.9

10.4–50.0

50.1–75.0

75.1–100.0

100.1–131.9

53.5

65.0

35.9

48.8

0

22.8

Source: (Kenya Ministry of Health, 2018)

32%

Source: (Government of Kenya, 2014)

Source: (Government of Kenya, 2014)

Exhibit E.4: Maternal service readiness index by ownership and tier of care (2013)

Public

Private

Hospitals

Health centers

Dispensaries

Medical clinics

0 10 20 30 40 50 60 70 80 90

6 33 47

27 43 58

17 28 42

28 62

7 30 69

20 56 84

70

60

50

40

30

20

10

0

Kir

inya

ga

Elge

yo-M

arak

wet

Mer

u

Bom

et

Bar

ingo

Kit

ui

Embu

Mac

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s

Ker

icho

Kili

fi

Ken

ya

Kaj

iado

Gar

issa

Mak

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Man

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Mar

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t

Bun

gom

a

Laik

ipia

Lam

u

Hom

a B

ay

Kis

ii

Kak

ameg

a

Mig

ori

Kia

mbu

Bus

ia

Kis

umu

Kw

ale

Isio

lo

Tran

s-N

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Wes

t Po

kot

Nai

robi

Turk

ana

Mom

basa

Sam

buru

Nan

di

Waj

ir

Uas

in G

ishu

Tana

Riv

er

Nar

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Nye

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Nak

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Tha

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Nit

hi

Siay

a

Vih

iga

Nya

ndar

ua

Tait

a Ta

veta

Nya

mir

a

Mur

anga

Avg.


Box F: Malaysia pilot

What was the main issue? In 2017, Malaysia had the highest diabetes prevalence (16.9%) in the East Asia Pacific region (Exhibit F.1) (International Diabetes Federation, 2019). Compared to other countries, Malaysia has high rates of hospital admission for long-term complications of diabetes (such as premature heart disease and stroke, blindness, amputations, and kidney failure), which are 10–12 times costlier to treat than regular follow-up diabetes care (Mustapha, et al., 2017). In Malaysia, diabetes is also the fifth highest cause of death and disability combined (IHME, 2019). For this reason, the JLN Malaysia team sought to explore potential efficiency gains along the results chain that could lead to improved outcomes for diabetes.

What indicators were used and what were the findings? The team looked at indicators along the diabetes care cascade – diabetes prevalence, percent of diabetes patients who are diagnosed, and among diagnosed the percent of patients who are on treatment and who have their diabetes under control (Exhibit F.2). Findings from an annual clinical audit of type 2 diabetes patients provided additional insight into service delivery by looking at adherence to clinical guidelines at public health care facilities and the rate of complications (Exhibit F.3). The breakdown happens early in the continuum of care, as more than half (59%) of diabetes patients remain undiagnosed. While 86.5% of diagnosed patients receive annual Ac1 screening, fundoscopy and renal and liver function tests are less prevalent. Among diabetes patients 58.2% had good glycaemic control Ac1<8% while 1 in 5 patients had an HbAc1>10%. In terms of complications, in 2017, 11.5% of diabetes patients developed nephropathy, 9.6% developed retinopathy, 5.1% developed ischaemic heart disease, 1.5% developed cerebrovascular disease, 1.3% developed a diabetic foot ulcer, and 0.7% had a limb amputation.

What were the limitations? Was additional contextual information provided? Population-level data on the percent of diagnosed diabetes patients that are on treatment, the percentage whose treatment adheres to clinical guidelines, and the rate of complications was not available. In the absence of such data, the percentages from the National Diabetes Registry (NDR) Clinical Audit sample-based study were used. While the data from the NDR does not include patients seen in the private sector, the 2015 National Health and Morbidity Survey reported that only 15.1% and 3.6% of diabetes patients sought care in private clinics and private hospitals respectively, making these reasonable estimates for the broader population. The Malaysia team complimented the analysis with additional insight on how an increasing number of patients were referred to tertiary hospitals due to certain limitations at primary health care facilities in diagnosing and managing diabetes care. However, there is now a push to have more fundus cameras available and more Assistant Medical Officers certified and trained to carry out fundus exams at district-level primary health care clinics. Multiple efforts have been undertaken by various sectors to overcome the inadequate adherence to clinical guidelines for diabetes in health care facilities as well as the mismatch of knowledge on diabetes, poor compliance of medications, and aversion to insulin delivery methods among patients.

What recommendations are suggested? Based on these findings, recommendations are divided into four perspectives – healthcare providers, patients, policy makers, and researchers. For healthcare providers, additional training is required to improve the knowledge and skills to detect diabetes early on. This will also help promote best practices in managing diabetes and its related complications for improved quality of care. For patients, continued effort by the health promotion team to improve awareness and increase health literacy among diabetic patients. Policy makers should consider alternative approaches to service delivery such as financial incentives to the case managers who oversee long-term care especially among high-risk patients, community empowerment programs that conduct screening, or a complete patient-centered integrated care model that incorporates community resources, patient self-management, integrated information systems, and innovative delivery systems such as clinical decision support applications, mobile health devices, and bundled payment options between primary health care and hospital settings. Researchers should focus on the effectiveness, efficiency, and cost-effectiveness of diabetes interventions to provide evidence and assist policy makers in the prioritization and decision-making process.

The authors would like to thank the Director General of Health Malaysia for his permission to publish this article.

Source: Dr Nor Izzah Hj Ahmad Shauki, Dr Lee Kun Yun, Dr Abdul Hakim b Abdul Rashid, Dr Mohd Najib b Baharuddin, Dr Yussni bt Aris


Exhibit F.1: Diabetes prevalence (%), 2017

Malaysia

Fiji

Vanuatu

Singapore

India

China

New Zealand

Philippines

Thailand

Timor

Indonesia

Vietnam

Japan

Australia

Myanmar

Cambodia

Laos

16.7

14.5

12.0

11.0

10.4

9.7

8.1

7.1

7.0

6.9

6.3

6.0

5.7

5.1

4.6

4.0

4.0

0 5 10 15 20

Source: International Diabetes Federation (https://idf.org/our-network/regions-members/western-pacific/members/108-malaysia.html)

Note: *Based on sample-based clinical audit of diagnosed diabetes patients. http://www.iku.gov.my/images/IKU/Document/REPORT/nhmsreport2015vol2.pdf

Sources: International Diabetes Federation (https://idf.org/our-network/regions-members/western-pacific/members/108-malaysia.html) and *National Diabetes Registry of Malaysia

4,000,000

3,500,000

3,000,000

2,500,000

2,000,000

1,500,000

1,000,000

500,000

0

1009080706050403020100

%

Abs

olut

e nu

mbe

r

16.9

40.6

86.5

58.2

Prevalence Diagnosed Among diagnosed, on treatment*

Among diagnosed,

under control (HbA1c<8%)*

Exhibit F.2: Diabetes care cascade in Malaysia (absolute number and %), 2017

Exhibit F.3: Adherence to clinical practice guidelines for diabetes mellitus in Malaysia (%), 2017

Note: Sample size 160,075

Source: National Diabetes Registry of Malaysia

Had exam in past year %Patients who underwent HbA1c 86.5

Patients who underwent fundoscopy 51.8

Patients who underwent foot exam 76.3

Patients who underwent proteinuria 67.0

Patients who underwent urine microalbumin 52.9


The findings from the pilot exercise highlighted challenges to applying the proposed framework as well as lessons learned.

Challenges:• Availability of data: A key limiting factor was the availability of data at the right level of disaggregation to make meaningful comparisons (e.g., by geographical region, across population groups) – especially for decentralized countries. It was also difficult to get data across different parts of the health system as information is held in different departments or programs within the Ministry of Health or was spread out across several Ministries/Agencies or levels of government. However, when pressed, all pilot countries were able to bring in anecdotal evidence or refer to country studies to help explain their data.

• Defining the scope of the efficiency analysis: All four countries attempted to present country data for the entire list of priority indicators. This made it harder to focus on any one area and to delve deeper into identifying where along the results chain inefficiencies might lie.

• Interpreting the data: There was little interpretation of what the data might mean and/or packaging the information into a policy-relevant story line.

Lessons learned:• Focus the scope of the analysis: That is, looking at hospital efficiency, pharmaceuticals or a specific tracer

condition within primary health care, such as maternal health or tuberculosis, where relevant indicators were more likely to be available under responsible units or budget holders.

• Additional indicators should be analyzed when specific issues emerge in the course of routine monitoring: This includes going beyond indicators and drawing on practitioner’s valuable knowledge of the sector to help fill in the gaps in the storyline when data is not available. This will help with the interpretation of findings and the assessment of options for further action.

• Efficiency analysis needs to be embedded into the formal decision-making process, not a one-off exercise. Many health system performance indicators are already regularly collected and reported as part of the routine monitoring and evaluation of the health sector. Building on existing processes and applying an efficiency lens to reviewing indicators that question why some regions, hospitals, or providers perform better than others is the first step to institutionalizing efficiency analysis.


Conclusion

There are inefficiencies in all health care systems, the scale of which can be significant. But while the main sources of inefficiency have been widely discussed in the literature, what is less available is a practical approach that policy makers can use to inform change. Stochastic frontier analysis (SFA) or data envelopment analysis (DEA) are the standard methods for economists to assess efficiency. However, these methods are often time consuming, data intensive, and require particular expertise. In addition, they do not necessarily identify the truly efficient behavior, nor do they tell policy makers how to improve efficiency.

This guide presents an alternative ‘benchmarking plus’ approach that can be readily used by practitioners and policy makers. In setting up routine monitoring systems, countries should ideally benchmark a few common efficiency metrics in areas that consume the most resources or are prone to inefficiency such as caseload, average length of stay, avoidable readmissions, unit cost per episode of care, stock-out rates, etc. However, if not available, benchmarking indicators along the process of how inputs are used to deliver services, which produce outputs that contribute to outcomes, can help pinpoint service delivery bottlenecks for further inspection. Discussion with key informants provides additional contextual information on the relevance and extant of identified problems. Only then can appropriate recommendations be considered based countries’ prioritization criteria and resource constraints.

However, even this simpler approach presented challenges for participating countries given data limitations. Almost all participating countries lacked indicators to measure those aspects of service delivery that are related to quality. Instead, available outputs mostly focused on availability, access, and coverage. But quality metrics are needed to help monitor countries’ progress from assessing ‘coverage’ of key interventions towards assessing ‘effective coverage’. In 2018, poor quality of health services contributed to more amenable deaths (5 million) than non-utilization of services (3.6 million) across lower-middle income countries. Hospital quality metrics are especially important given recent findings that 10% of hospitalized patients acquire infections during their stay; clinical guidelines are followed in less than 45% of cases; and harmful medical errors and preventable complications account for 15% of hospital costs (Kruk, et al., 2018). All of these represent sources of inefficiency because resources are spent on services that produce reduced or no added health benefit.

Countries cannot improve what they cannot measure; however, other resources are likely available to support efficiency analysis. In the absence of regular reporting on key efficiency metrics collected by health management and information systems, almost all countries will be able to fill in the gaps using past studies, ad hoc population and facility surveys, and interviews with key informants, as was demonstrated by the Kenya and Malaysia case studies. For this reason, a benchmarking plus approach is recommended while investments for foundational healthcare analytic systems are made.

Fortunately, countries are increasingly digitizing data which will help fill in these gaps and increase the potential for efficiency analysis. The demand for data-driven decision-making and the rising pressure to reduce healthcare spending while improving patient outcomes is driving the growth of healthcare analytics – a USD 14 billion industry globally that is projected to reach USD 50.5 billion by 2024. Currently, low- and lower-middle income countries still struggle to produce basic descriptive analytics – a precursor to more advanced predictive and prescriptive analytics. Technological advances in text and image storage, cloud computing, and big data are paving the way to extract more value from data by integrating electronic medical records, patient monitoring systems, claims data, and other core administrative data. However, the biggest limiting factor is the broad range of skills necessary to develop a healthcare analytics team that includes information technology specialists to deal with hardware, software, and connectivity issues; clinical coders and data management specialists to enable standardization and interoperability; and policy makers, health care providers, and data scientists to develop applications that help answer policy-relevant questions. All must work together.


References

Chisholm, D. & Evans, D. B., 2010. Improving health system efficiency as a means of moving towards universal coverage, Geneva: The World Health Organization.

Couffinhal, A. & Socha-Dietrich, K., 2017. Ineffective spending and waste in health care systems: frameworks and findings. In: Tackling wasteful spending on health. Paris: OECD Publishing, pp. 17–59.

Cylus, J., Papanicolas, I. & Smith, P. C., 2016. Health system efficiency: how to make measurement matter for policy and management. London: European Observatory on Health Systems and Policies.

Drummond, M. F. et al., 2015. Methods for economic evaluation of health care programmes, 4th Edition. Oxford: Oxford University Press.

Government of Kenya, 2014. Kenya Service Availability and Readiness Assessment Mapping (SARAM), Nairobi: Ministry of Health.

Hafez, R., Somanathan, A. & Wilson, D., 2016. Raising funds for health - Background paper for 1st universal health coverage financing forum. Washington DC: The World Bank.

IHME, 2019. Global Burden of Disease, s.l.: http://www.healthdata.org/malaysia.

International Diabetes Federation, 2019. Diabetes Atlas, s.l.: https://diabetesatlas.org/across-the-globe.html.

Kenya Institute for Public Policy Research and Analysis (KIPPRA), 2018. Kenya Economic Report 2018: Boosting Investments for Delivery of the Kenya Vision 2030, Nairobi.

Kenya Ministry of Health, 2018. Kenya Health Sector Strategic Plan (KHSSP), July 2018 - June 2022, Nairobi: Ministry of Health.

Kruk, M. E. et al., 2018. High quality health systems in the sustainable development goals era: time for a revolution. Lancet Glob Health , Volume 6, pp. e1196–252.

Morgan , D., 2010. Chapter 1: how much is too much? value for money in health spending. In: Value for money in health spending. Geneva: OECD, pp. 21–42.

Mustapha, F. I. et al., 2017. What are the direct medical costs of managing Type 2 Diabetes Mellitus in Malaysia. Med J Malaysia, 72(5), pp. 271–277.

Papanicolas, I. & Smith, P. C., 2017. Theory of system level efficiency in healh care. In: Encyclopedia of Health Economics. San Diego: Elsevier, pp. vol. 3: 386–394.

Smith, P. C., 2012. What is the scope for health system efficiency gains and how can they be achieved?. Eurohealth Observer, pp. 18(3)3–6.

Tandon, A. & Cashin, C., 2010. Assessing Public Expenditure on Health From a Fiscal Space Perspective, Washington, DC: The World Bank.

UNFPA, 2014. The state of the world’s midwifery, https://www.unfpa.org/data/sowmy/KE: UNFPA.

WHO, 2019. Global Health Observatory Database, Geneva: WHO.

World Bank, 2013. Kenya Service Delivery Indicators, Washington, DC: World Bank.

World Bank, 2017. Greater efficiency for better health and financial protection: Background paper for 2nd Annual UHC Financing Forum, April 20-21, 2017, Washington DC: The World Bank.

World Bank, 2019a. Health Equity and Financial Protection Database, Washington, DC: The World Bank.

World Bank, 2019b. World Development Indicators, Washington, DC: The World Bank.

Yip, W. & Hafez, R., 2015. Reforms for improving the efficiency of health systems: lessons from 10 country cases, Geneva: The World Health Organization.

Yip, W., Hafez, R. & Chi, Y.L., 2014. Strategic purchasing for universal health coverage: policy brief for the Eastern-Mediterranean region, Geneva: The World Health Organization.


Annex 1 – Indicator lists reviewed

The World Health Organization-World Bank UHC Measurement FrameworkThe WHO-World Bank UHC Measurement Framework tracks eight core health service coverage indicators chosen because they involve interventions for which every individual should benefit – mainly maternal and child health, and infectious disease control. Not currently included are tracers for non-communicable diseases. In addition, the framework also tracks two measures of financial risk protection, namely out-of-pocket spending as a share of total consumption and health expenditures that drive households beneath the poverty line. These indicators are generally collected from frequent household surveys such as the demographic and health survey, the multiple indicator cluster survey, and/or household consumption surveys. The limited number of indicators and their relative straightforward measurability allow for comparability across countries making them a useful common framework for tracking progress towards UHC globally.For more information: https://www.who.int/healthinfo/universal_health_coverage/report/2015/en

The World Health Organization’s Global Reference List of 100 Core Health IndicatorsThis list is a more comprehensive (though not exhaustive) set of indicators organized by four domains i) health status, ii) risk factors, iii) service coverage, and iv) health systems. In addition to a broader range of maternal and child health, and infectious disease control indicators, the global reference list also includes a range of risk factors associated to non-communicable diseases, and outcomes that act as proxies for access and quality of care. Unlike the UHC monitoring framework that looks at a limited section of the results chain (i.e. outputs and outcomes), this expanded list allows policy makers to identify weaknesses along the entire results chain. In fact, they also provide the same list of indicators re-organized by sections of the results chain. However, the focus remains on areas associated with the Millennium Development Goals agenda.For more information: https://www.who.int/healthinfo/indicators/2018/en

Primary Health Care Performance InitiativeThe PHCPI focuses exclusively on primary health care. The 36 Vital Signs indicators align and complement the Global Reference List of 100 Core Health Indicators by focusing on important service delivery processes that are crucial for achieving UHC and other global priorities. It looks at indicators across the entire results chain and goes one step further in collecting information related to provider knowledge (e.g., diagnostic accuracy), effort (e.g., caseload), and quality of service delivery (e.g., continuity of care) – indicators that are more complex to produce requiring vignettes, and health facility surveys that are less commonly produced and standardized limit comparability across countries and over time. These might be more useful for a within country comparison (e.g., regional variation).For more information: https://improvingphc.org/phcpi-core-indicators

Organization for Economic Cooperation and Development Health at a Glance DashboardThe OECD dashboard of indicators is meant to provide an overview of health system performance – organized around similar domains of health care resources, health status, and risk factors. However, unlike the WHO and PHCPI lists, the focus of the access to and quality of care indicators is on non-communicable diseases – a reflection of these countries’ epidemiology and level of health system development. The dashboard is used for cross-country comparisons.For more information: https://www.oecd.org/health/health-systems/health-at-a-glance-19991312.htm

Commonwealth Fund Scorecard for State Health System PerformanceThe 44 indicators on the Commonwealth Fund Scorecard – used to track performance over time and rank US states relative to one another – fall into four domains: access and affordability, prevention and treatment, avoidable hospital use and cost, and healthy lives. Similar to the OECD dashboard, indicators are more focused on hospital use and care, non-communicable diseases and associated risk factors – again a reflection of the US’s disease burden and health care system.For more information: https://www.commonwealthfund.org/publications/fund-reports/2018/may/2018-scorecard-state-health-system-performance

Joint Learning Network’s Indicators for Monitoring Provider Payment Methods Finally, the JLN developed a list of indicators specifically geared towards monitoring provider payment methods. Given the diversity of health care systems and provider payment methods across countries, these would ideally be used for within-country comparison at the facility/provider level.For more information: https://www.jointlearningnetwork.org/resources/data-analytics-for-monitoring-provider-payment-toolkit

Annex 2 – Fact sheets

Financing (management) • Total health expenditure as % of GDP • Government health expenditure (GGHE) as % of GDP, as % of budget, as % of THE, or in

per capita terms •Healthexpenditurebysourceoffinancing • Current health expenditure (CHE) by function

Inputs (management) • Health worker density • Hospital bed density

Outputs (access) • Antenatal care • Skilled birth attendance

Outputs (quality) •TBcasedetection,TBnotificationrate,andTBtreatmentsuccessrate •Generalservicereadiness;Servicespecificreadiness • Diagnostic accuracy for a tracer condition • Adherence to clinical guidelines • Diabetes control • Avoidable hospital admissions for primary care sensitive conditions

Outputs (risk factors) • Children under 5 who are stunted • Tobacco use among persons aged 18+

Outputs (management) • Caseload • Absenteeism • Average length of stay (ALOS) vs. Bed occupancy rate (BOR) • Hospital re-admission rate • Caesarean section (C-section rates) • Expired drugs • Drug stockouts • Claims ratio • Budget execution rates (BERs)

Outcomes (health status) • Life expectancy at birth •Under-fivemortality • Maternal mortality rate • TB burden

Outcomes (financial protection) • Catastrophic health expenditure

Health Financing Technical Initiative: The Efficiency CollaborativeIndicator Fact Sheets


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Perc

enta

ge

71.2

21.7

31.1

40.8

39.9

25.9

43.1

57.8

Hea

lth e

xpen

ditu

re b

y so

urce

of fi

nanc

ing

Sim

ple

defin

itio

n: S

hare

of

gove

rnm

ent

spen

ding

, vol

unta

ry a

nd/o

r pr

ivat

e H

I, O

OP,

res

t of

the

wor

ld a

s %

of T

HE.

Th

e ov

eral

l cur

rent

spe

ndin

g on

hea

lth in

clud

e ex

pend

iture

s of

gov

ernm

ent s

chem

e (H

F.1)

, vol

unta

ry s

chem

es (H

F.2)

, out

of p

ocke

t spe

ndin

g (H

F.3)

, res

t of t

he w

orld

(HF.

4) a

nd o

ther

not

els

ewhe

re c

lass

ified

ex

pend

iture

s (H

F.ne

c).

Freq

uenc

y of

dat

a co

llect

ion:

Ann

ual.

Sugg

este

d co

mpa

rato

r: A

cros

s co

untr

ies;

if c

ount

ry is

dec

entr

aliz

ed s

ubna

tion

al c

ompa

rison

s ar

e hi

ghly

info

rmat

ive;

pas

t pe

rfor

man

ce/t

rend

dat

a.

By c

onst

ruct

ion,

the

hig

her t

he s

hare

of g

over

nmen

t an

d/or

pre

paid

spe

ndin

g th

e lo

wer

the

sha

re o

f OO

P; a

nd t

he lo

wer

the

sha

re o

f OO

P sp

endi

ng, t

he b

ette

r. G

loba

lly, c

ount

ries

that

are

clo

sest

to

atta

inin

g un

iver

sal h

ealt

h co

vera

ge (U

HC)

ha

ve O

OP

spen

ding

leve

ls t

hat

are

less

tha

n 15

-20%

of t

otal

hea

lth

spen

ding

.

Pote

ntia

l sou

rce

of d

ata:

Abo

ut 9

5 co

untr

ies

have

pro

duce

d fu

ll na

tiona

l hea

lth

acco

unts

. The

W

HO

’s G

loba

l Hea

lth

Expe

nditu

re

Dat

abas

e al

so h

as in

tern

atio

nally

co

mpa

rabl

e da

ta o

n he

alth

spe

ndin

g fo

r clo

se t

o 19

0 co

untr

ies

from

200

0 to

201

6. In

-cou

ntry

sou

rces

incl

ude

natio

nal h

ealt

h ac

coun

t re

port

s,

publ

ic e

xpen

ditu

re re

port

s, s

tatis

tical

ye

arbo

oks

and

othe

r per

iodi

cals

, and

re

port

s an

d da

ta p

rovi

ded

by c

entr

al

stat

istic

al o

ffice

s an

d m

inis

trie

s.

http

://ap

ps.w

ho.in

t/nh

a/da

taba

se/S

elec

t/In

dica

tors

/en

Benc

hmar

king

gui

delin

esN

o es

tabl

ishe

d be

nchm

ark.

Sug

gest

be

nchm

arki

ng a

gain

st p

ast

or o

wn

coun

try

perf

orm

ance

.

Not

e: U

sing

the

WH

O’s

Glo

bal H

ealth

Exp

endi

ture

dat

abas

e G

GH

E-D

+OO

PS+E

XT+V

HI+

OTH

ER=1

00%

or H

F.1+

HF.

2+H

F.3+

HF.

4+H

F.ne

c=CH

ESo

urce

: WH

O (2

019)

. Glo

bal H

ealth

Exp

endi

ture

Dat

abas

e

Fina

ncin

g (m

anag

emen

t)

MEASURING HEALTH SYSTEM EFFICIENCY IN LOW- AND MIDDLE-INCOME COUNTRIES: A RESOURCE GUIDE30 Curr

ent h

ealth

exp

endi

ture

(CH

E) b

y fu

nctio

nSi

mpl

e de

finiti

on: T

he s

hare

of c

urre

nt h

ealth

exp

endi

ture

by

func

tion

(e.g

. cur

ativ

e ca

re, p

harm

aceu

tical

s, p

reve

ntiv

e ca

re, a

dmin

istr

ativ

e). U

sing

the

WH

O’s

Glo

bal H

ealth

Exp

endi

ture

dat

abas

e de

finiti

ons:

· CH

E= C

urat

ive

care

(HC.

1) +

Reh

abili

tativ

e ca

re (H

C.2)

+ L

ong-

term

car

e (H

C.3)

+ A

ncill

ary

serv

ices

(HC.

4) +

Med

ical

goo

ds (H

C.5)

+ P

reve

ntiv

e ca

re (H

C.6)

+ G

over

nanc

e an

d he

alth

sys

tem

and

fina

ncin

g ad

min

istr

atio

n (H

C.7)

+ O

ther

hea

lth c

are

serv

ices

not

els

ewhe

re c

lass

ified

(HC.

9)· C

urat

ive

care

(HC.

1)=I

npat

ient

cur

ativ

e ca

re (H

C.1.

1) +

Day

cur

ativ

e ca

re (H

C.1.

2) +

Out

patie

nt c

urat

ive

care

(HC.

1.3)

+ H

ome-

base

d cu

rativ

e ca

re (H

C.1.

4) +

Uns

peci

fied

cura

tive

care

(HC.

1.ne

c)· P

rimar

y he

alth

car

e ex

pend

iture

=Out

patie

nt C

urat

ive

Care

(HC.

1.3)

+ H

ome-

base

d cu

rativ

e ca

re (H

C.1.

4) +

Out

patie

nt lo

ng-t

erm

car

e (H

C.3.

3) +

Hom

e-ba

sed

long

-ter

m c

are

(HC.

3.4)

+ 8

0% o

f med

ical

goo

ds (H

C.5)

+

Prev

entiv

e ca

re (H

C.6)

+ 8

0% o

f Gov

erna

nce,

and

hea

lth s

yste

m a

nd fi

nanc

ing

adm

inis

trat

ion

(HC.

7). H

owev

er, i

t is

reco

mm

ende

d to

adj

ust t

his

defin

ition

to b

ette

r refl

ect c

ount

ry c

onte

xt.

Freq

uenc

y of

dat

a co

llect

ion:

Ann

ual.

Sugg

este

d co

mpa

rato

r: A

cros

s co

untr

ies;

Pas

t per

form

ance

/tre

nd d

ata.

Pote

ntia

l sou

rce

of d

ata:

Beyo

nd O

ECD

cou

ntrie

s, o

nly

a se

lect

nu

mbe

r of c

ount

ries

have

sta

rted

re

port

ing

expe

nditu

res

by fu

nctio

n to

th

e W

HO

’s G

loba

l Hea

lth E

xpen

ditu

re

Dat

abas

e (h

ttp:

//app

s.w

ho.in

t/nh

a/da

taba

se/S

elec

t/In

dica

tors

/en)

. Th

e W

HO

has

pub

lishe

d th

e fir

st e

ver

com

para

ble

mea

sure

s of

prim

ary

heal

th c

are

spen

ding

usi

ng d

ata

from

20

16 fo

r 46

low

- an

d m

iddl

e-in

com

e co

untr

ies

(LIN

K: h

ttps

://ap

ps.w

ho.in

t/iri

s/bi

tstr

eam

/han

dle/

1066

5/27

6728

/W

HO

-HIS

-HG

F-H

F-W

orki

ngPa

per-

18.3

-eng

.pdf

?ua=

1).

In-c

ount

ry s

ourc

es m

ay in

clud

e na

tiona

l hea

lth a

ccou

nt re

port

s an

d pu

blic

exp

endi

ture

revi

ews

(htt

p://

sear

ch.w

orld

bank

.org

/per

).

Benc

hmar

king

gui

delin

esN

o es

tabl

ishe

d be

nchm

ark.

Sug

gest

be

nchm

arki

ng a

gain

st c

ount

ries

of s

imila

r inc

ome/

heal

th s

yste

m

deve

lopm

ent.

This

indi

cato

r is

diffi

cult

to c

ompa

re a

cros

s co

untr

ies.

Spe

ndin

g by

func

tion

is g

ener

ally

driv

en b

y fa

ctor

s su

ch a

s co

untr

y an

d he

alth

sys

tem

dev

elop

men

t, d

emog

raph

ics

and

epid

emio

logy

, tre

atm

ent p

roto

cols

, and

pat

ient

pre

fere

nces

, etc

. For

ex

ampl

e, in

cou

ntrie

s w

here

the

qual

ity o

f prim

ary

care

is p

oor o

r lon

g te

rm c

are

optio

ns a

re li

mite

d, s

pend

ing

on c

urat

ive

care

/hos

pita

ls w

ill b

e hi

gher

. It i

s al

so im

port

ant t

o no

te th

at m

ore

than

20%

of c

urre

nt h

ealth

exp

endi

ture

rem

ains

unc

lass

ified

in

som

e co

untr

ies

sugg

estin

g a

lack

of a

vaila

bilit

y of

mor

e gr

anul

ar d

ata

for p

rodu

cing

hea

lth a

ccou

nts.

Spec

ial n

ote

on p

harm

aceu

tical

spe

ndin

g: F

our p

ossi

ble

fact

ors

influ

ence

a c

ount

ry’s

phar

mac

eutic

al s

pend

ing:

i) c

ount

ry p

opul

atio

n an

d vo

lum

e of

dru

gs c

onsu

med

, ii)

drug

util

izat

ion

per p

erso

n, ii

i) ty

pe a

nd m

ix o

f dru

gs c

onsu

med

(e.g

., ge

neric

s ve

rsus

bra

nd-n

ame

drug

s), a

nd iv

) pric

es a

t whi

ch d

rugs

are

sol

d. C

ompa

ring

drug

pric

es a

cros

s co

untr

ies

is a

com

plic

ated

and

impe

rfec

t pro

cess

, prim

arily

bec

ause

of t

he p

ropr

ieta

ry n

atur

e of

the

reba

tes

drug

man

ufac

ture

rs o

ffer

diff

eren

t pay

ers.

Not

es: B

oxpl

ots

show

the

inte

rqua

rtile

rang

e of

val

ues

with

the

med

ian

mar

ked

by a

line

insi

de th

e ba

r and

the

mea

n m

arke

d by

an

X.

The

lines

from

the

bar e

xten

d to

the

max

imum

and

min

imum

val

ues

with

out

liers

exc

lude

d. In

patie

nt c

urat

ive

=HC1

.1+H

C1.2

; Out

patie

nt

cura

tive=

HC1

.3+H

C1.4

; Med

ical

goo

ds=H

C5; P

reve

ntiv

e ca

re=H

C6; G

over

nanc

e an

d ad

min

istr

atio

n=H

C7. D

ata

is fo

r 34

low

- an

d lo

wer

-m

iddl

e in

com

e co

untr

ies

for w

hich

dat

a is

ava

ilabl

e: A

fgha

nist

an; A

rmen

ia; B

urun

di; B

urki

na F

aso;

Bhu

tan;

Cot

e d’

Ivoi

re; C

ongo

, Dem

. Rep

.; Co

ngo,

Rep

; Cab

o Ve

rde;

Djib

outi;

Eth

iopi

a; G

eorg

ia; G

hana

; Gui

nea;

Gua

tem

ala;

Hai

ti; In

dia;

Ken

ya; K

yrgy

z Re

publ

ic; C

ambo

dia;

Lib

eria

; Sri

Lank

a; M

oldo

va; M

ali;

Mau

ritan

ia; N

epal

; Phi

lippi

nes;

Tog

o; T

ajik

ista

n; T

imor

-Les

te; T

unis

ia; T

anza

nia;

Uga

nda;

Zam

bia

Sour

ce: W

HO

(201

9). G

loba

l Hea

lth E

xpen

ditu

re D

atab

ase

(htt

p://a

pps.

who

.int/

nha/

data

base

/Sel

ect/

Indi

cato

rs/e

n)

55.0

50.0

45.0

40.0

35.0

30.0

25.0

20.0

15.0

10.0 5.0

0.0

Curr

ent

heal

th e

xpen

ditu

re b

y fu

ncti

on (2

016)

Inpa

tient

cu

rativ

eO

utpa

tient

cu

rativ

eM

edic

al

good

sPr

even

tive

care

Gov

erna

nce,

an

d ad

min

istr

atio

n

Percent of CHE

Fina

ncin

g (m

anag

emen

t)


Sour

ce: W

orld

Ban

k (2

019)

. Wor

ld D

evel

opm

ent I

ndic

ator

s. (h

ttps

://da

taba

nk.w

orld

bank

.org

/sou

rce/

wor

ld-d

evel

opm

ent-

indi

cato

rs)

Phys

icia

ns, n

urse

s, a

nd m

idw

ives

, by

coun

try

(per

1,0

00 p

eopl

e)

(Mos

t rec

ent v

alue

in 2

010-

2016

)

05

1015

2025

High

inco

me

Uppe

r mid

dle

inco

me

Low

er m

iddl

e in

com

e

Low

inco

me

Hea

lth w

orke

r den

sity

Sim

ple

defin

itio

n: N

umbe

r of

phy

sici

ans,

nur

ses,

and

mid

wiv

es a

vaila

ble

(in a

reg

ion)

per

1,0

00 h

abit

ants

.

Freq

uenc

y of

dat

a co

llect

ion:

Ann

ual.

Sugg

este

d co

mpa

rato

r:

• In

the

ory,

the

rel

ativ

e st

raig

htfo

rwar

d an

d st

anda

rdiz

ed m

easu

rabi

lity

of t

hese

indi

cato

rs s

houl

d al

low

for

com

para

bilit

y ac

ross

cou

ntrie

s •

It m

ay a

lso

be o

f int

eres

t to

pol

icy

mak

ers

to c

ompa

re b

y re

gion

(e.g

. pro

vinc

e, u

rban

/rur

al)

Glo

bal d

ata

is le

ss fr

eque

nt t

han

natio

nal s

ourc

es a

nd m

ost

rece

nt v

alue

in a

5 o

r 10

year

per

iod

may

be

need

ed fo

r cro

ss-c

ount

ry c

ompa

rison

s.

It is

impo

rtan

t to

che

ck h

ow c

ount

ry d

efini

tions

diff

er fr

om g

loba

l pra

ctic

e if

com

parin

g to

oth

er c

ount

ries.

In p

ract

ice,

dat

a co

mpa

rabi

lity

is li

mite

d by

diff

eren

ces

in d

efini

tions

and

tra

inin

g of

med

ical

per

sonn

el. I

n pa

rtic

ular

, com

mun

ity

heal

th w

orke

rs (C

HW

s) a

re g

ener

ally

not

incl

uded

whi

ch m

ay b

e pr

oble

mat

ic in

cou

ntrie

s th

at a

re in

crea

sing

ly s

hift

ing

task

s to

CH

Ws

and

rely

ing

on t

hem

to

prov

ide

basi

c he

alth

ser

vice

s.

Her

e to

o it

may

be

mor

e us

eful

to c

onsi

der c

ount

ry c

onte

xt -

wha

t is

need

ed to

reac

h he

alth

sys

tem

goa

ls a

nd p

rovi

de e

quita

ble

acce

ss to

hea

lth c

are

serv

ices

. Thi

s is

esp

ecia

lly re

leva

nt in

spa

rsel

y po

pula

ted

and

hard

to re

ach

area

s w

ithin

a c

ount

ry.

It w

ould

be

good

to

exam

ine

this

indi

cato

r at

the

subn

atio

nal l

evel

alo

ngsi

de t

he c

asel

oad

indi

cato

r to

asse

ss a

lloca

tive

and

tech

nica

l (w

orke

r pro

duct

ivity

) effi

cien

cy.

Pote

ntia

l sou

rce

of d

ata:

The

WH

O c

ompi

les

data

from

ho

useh

old

and

labo

r for

ce s

urve

ys,

cens

uses

, and

adm

inis

trat

ive

reco

rds

( htt

p://a

pps.

who

.int/

gho/

data

/nod

e.m

ain?

show

only

=HW

F ). D

ata

can

also

co

me

dire

ctly

from

nat

iona

l sou

rces

in

clud

ing

prof

essi

onal

regi

strie

s an

d H

R sy

stem

s.

Benc

hmar

king

gui

delin

esTh

e W

HO

est

imat

es t

hat

at le

ast

2.5

med

ical

sta

ff (p

hysi

cian

s, n

urse

s an

d m

idw

ives

) per

1,0

00 p

eopl

e ar

e ne

eded

to

prov

ide

adeq

uate

co

vera

ge w

ith p

rimar

y ca

re

inte

rven

tions

. Alt

erna

tivel

y, t

he S

DG

in

dex

thre

shol

d is

4.4

5 ph

ysic

ians

, m

idw

ives

and

nur

ses

per 1

,000

for

UH

C an

d SD

G fu

lfilm

ent.

Inpu

ts (m

anag

emen

t)


Glo

bal d

ata

is le

ss fr

eque

nt t

han

natio

nal s

ourc

es a

nd m

ost

rece

nt v

alue

in a

5 o

r 10

year

per

iod

may

be

need

ed fo

r cro

ss-c

ount

ry c

ompa

rison

s.A

maj

or li

mita

tion

of h

ospi

tal d

ensi

ty is

tha

t it

does

not

tak

e in

to a

ccou

nt t

he s

ize

of fa

cilit

ies

and

it m

ay b

e m

ore

info

rmat

ive

to lo

ok a

t in

patie

nt b

ed d

ensi

ty. H

owev

er, d

iffer

ence

s in

defi

nitio

ns a

nd w

heth

er m

ater

nal b

eds

and/

or fa

ith-

base

d be

ds a

re in

clud

ed m

ay u

nder

/ove

rest

imat

e nu

mbe

rs. T

here

fore

, it

is im

port

ant

to c

heck

how

cou

ntry

defi

nitio

ns d

iffer

from

glo

bal p

ract

ice

if co

mpa

ring

to o

ther

cou

ntrie

s.

Sim

ilar t

o hu

man

reso

urce

s, it

may

be

mor

e us

eful

to

cons

ider

cou

ntry

con

text

- w

hat

is n

eede

d to

reac

h he

alth

sys

tem

goa

ls a

nd p

rovi

de e

quita

ble

acce

ss t

o he

alth

car

e se

rvic

es. H

ere,

it w

ould

be

good

to

exam

ine

bed

dens

ity a

t th

e su

bnat

iona

l lev

el a

long

side

the

bed

occ

upan

cy ra

te in

dica

tor t

o as

sess

allo

cativ

e an

d te

chni

cal e

ffici

ency

.

Sour

ce: W

HO

, var

ious

yea

rs

Hos

pita

l Bed

s pe

r 1,0

00 P

eopl

e, L

ates

t Ye

ar A

vaila

ble

Keny

a

Hos

pita

l bed

den

sity

Sim

ple

defin

itio

n: N

umbe

r of

hos

pita

ls o

r ho

spit

al b

eds

per

1,00

0 po

pula

tion

.

Freq

uenc

y of

dat

a co

llect

ion:

Ann

ual.

Sugg

este

d co

mpa

rato

r:

• In

the

ory,

the

rel

ativ

e st

raig

htfo

rwar

d an

d st

anda

rdiz

ed m

easu

rabi

lity

of t

hese

indi

cato

rs s

houl

d al

low

for

com

para

bilit

y ac

ross

cou

ntrie

s.

• It

may

als

o be

of i

nter

est

to p

olic

y m

aker

s to

com

pare

by

regi

on (e

.g. p

rovi

nce,

urb

an/r

ural

).

Pote

ntia

l sou

rce

of d

ata:

The

WH

O c

ompi

les

data

from

cou

ntry

fo

cal p

oint

s fr

om m

inis

trie

s of

hea

lth

thro

ugh

base

line

faci

lity

surv

eys.

Dat

a ca

n al

so c

ome

dire

ctly

from

na

tiona

l adm

inis

trat

ive

reco

rds.

Serv

ice

Prov

isio

n A

sses

smen

ts (S

PA),

a ty

pe o

f Dem

ogra

phic

and

Hea

lth

Surv

ey, m

ay a

lso

be h

elpf

ul t

o es

tabl

ish

estim

ates

.So

urce

sW

HO

: htt

p://a

pps.

who

.int/

gho/

data

/nod

e.m

ain.

HS0

7?la

ng=e

nSP

A: h

ttps

://dh

spro

gram

.com

/Wha

t-W

e-D

o/Su

rvey

-Typ

es/S

PA.c

fmD

HS:

htt

ps://

dhsp

rogr

am.c

om/

Benc

hmar

king

gui

delin

esU

sual

ly t

here

is a

cou

ntry

tar

get

for

faci

lity

dens

ity.

WH

O s

elec

ts a

n ar

bitr

ary

benc

hmar

k of

2.5

inpa

tient

bed

s pe

r 1,0

00 b

ased

on

glo

bal,

low

er a

nd u

pper

-mid

dle

inco

me

coun

try

aver

ages

of 2

.7, 1

.8,

and

3.9

hosp

ital b

eds

per 1

,000

re

spec

tivel

y.

Inpu

ts (m

anag

emen

t)


36.8

51.3

51.7

56.3

57.6

58.7

62.3

63.4

73.1

01020304050607080

Pote

ntia

l sou

rce

of d

ata:

Usu

ally

der

ived

from

hou

seho

ld

surv

eys

with

repr

esen

tativ

e sa

mpl

es

of w

omen

of r

epro

duct

ive

age

such

as

the

Dem

ogra

phic

and

Hea

lth

Surv

ey (D

HS)

or M

ultip

le In

dica

tor

Clus

ter S

urve

ys (M

ICS)

.ht

tps:

//dhs

prog

ram

.com

/dat

a/av

aila

ble-

data

sets

.cfm

http

s://m

ics.

unic

ef.o

rg/s

urve

ys

Benc

hmar

king

gui

delin

esU

sed

to b

e 4

visi

ts, m

ost

rece

nt W

HO

re

com

men

datio

n is

8 v

isits

. How

ever

, w

hat

is m

ore

impo

rtan

t th

an t

he

num

ber o

f vis

its i

s th

e qu

ality

of

AN

C vi

sits

and

the

inte

rven

tions

de

liver

ed d

urin

g an

AN

C vi

sit.

Perc

ent

of p

regn

ant

wom

en w

ho h

ad a

ll 4

AN

C vi

sits

, Ken

ya (2

014)

Sour

ce: K

enya

Dem

ogra

phic

and

Hea

lth S

urve

y 20

14

Nor

th

East

ern

Rift

Val

ley

Tota

lCo

ast

Nai

robi

Wes

tern

East

ern

Nya

nza

Cent

ral

Ant

enat

al c

are

Sim

ple

defin

itio

n: P

erce

ntag

e of

wom

en a

ged

15-4

9 w

ith

a liv

e bi

rth

wit

hin

a gi

ven

tim

e pe

riod

that

rec

eive

d an

tena

tal c

are

prov

ided

by

a sk

illed

hea

lth

prof

essi

onal

(e.g

. phy

sici

ans,

mid

wiv

es, n

urse

s) d

urin

g th

eir

preg

nanc

y. A

nten

atal

car

e in

clud

es s

cree

ning

or

prov

idin

g tr

eatm

ent

to p

reve

nt o

r re

duce

the

ris

k of

poo

r pr

egna

ncy

outc

omes

.

Freq

uenc

y of

dat

a co

llect

ion:

Usu

ally

eve

ry 5

yea

rs.

Sugg

este

d co

mpa

rato

r: A

cros

s co

untr

ies.

It m

ay a

lso

be o

f in

tere

st t

o po

licy

mak

ers

to c

ompa

re b

y re

gion

(e.g

. pro

vinc

e, u

rban

/rur

al) o

r so

cioe

cono

mic

sta

tus

(e.g

. wea

lth

quin

tile

, lev

el o

f ed

ucat

ion)

. Pas

t pe

rfor

man

ce/t

rend

dat

a.

If a

vaila

ble,

it w

ould

be

impo

rtan

t to

look

for i

nfor

mat

ion

on A

NC

serv

ice-

spec

ific

avai

labi

lity

and

read

ines

s an

d ad

here

nce

to q

ualit

y as

sura

nce

prot

ocol

s/gu

idel

ines

from

faci

lity

surv

eys

such

as

WH

O’s

Serv

ice

Avai

labi

lity

and

Read

ines

s A

sses

smen

t (S

ARA

) (ht

tps:

//ww

w.w

ho.in

t/he

alth

info

/sys

tem

s/sa

ra_i

ntro

duct

ion/

en/)

or t

he W

orld

Ban

k’s

Serv

ice

Del

iver

y In

dica

tors

(SD

I) (h

ttps

://w

ww

.sdi

ndic

ator

s.or

g). D

HS

surv

eys

also

usu

ally

hav

e a

deta

iled

brea

kdow

n of

the

com

pone

nts

of a

nten

nal c

are.

Out

puts

(acc

ess)

MEASURING HEALTH SYSTEM EFFICIENCY IN LOW- AND MIDDLE-INCOME COUNTRIES: A RESOURCE GUIDE34 Skill

ed b

irth

atte

ndan

ce

Pote

ntia

l sou

rce

of d

ata:

Usu

ally

der

ived

from

hou

seho

ld

surv

eys

with

repr

esen

tativ

e sa

mpl

es

of w

omen

of r

epro

duct

ive

age

such

as

the

Dem

ogra

phic

and

Hea

lth

Surv

ey (D

HS)

or M

ultip

le In

dica

tor

Clus

ter S

urve

ys (M

ICS)

. ht

tps:

//dhs

prog

ram

.com

/dat

a/av

aila

ble-

data

sets

.cfm

http

s://m

ics.

unic

ef.o

rg/s

urve

ys

Benc

hmar

king

gui

delin

esN

o es

tabl

ishe

d be

nchm

ark.

Her

e to

o,

wha

t is

mor

e im

port

ant

than

the

pe

rcen

tage

of b

irths

att

ende

d by

a

skill

ed h

ealt

h pe

rson

nel i

s th

e qu

ality

of

mat

erna

l and

new

born

car

e.

Birt

hs a

tten

ded

by s

kille

d he

alth

sta

ff b

y in

com

e qu

inti

le in

Sub

-Sah

aran

Afr

ica

(L

ates

t yea

r ava

ilabl

e)

Sour

ce: W

B (2

019)

. HEF

PI d

atab

ase

(htt

p://d

atat

opic

s.w

orld

bank

.org

/hea

lth-e

quity

-and

-fina

ncia

l-pr

otec

tion)Q4:

Ric

her

Q5:

Ric

hest

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Ethi

opia

Ghan

aKe

nya

Libe

riaM

adag

asca

rM

alawi

Mali

Mau

ritan

iaM

ozam

biqu

eN

iger

Nig

eria

Rwan

daSo

mal

iaSu

dan

Tanz

ania

Ugan

daZa

mbi

a

Q1:

Poo

rest

Q2:

Poo

rer

Q3:

Mid

dle

Sim

ple

defin

itio

n: P

erce

ntag

e of

live

birt

hs a

tten

ded

by s

kille

d he

alth

per

sonn

el d

urin

g a

spec

ified

tim

e pe

riod.

Freq

uenc

y of

dat

a co

llect

ion:

Usu

ally

eve

ry 5

yea

rs.

Sugg

este

d co

mpa

rato

r: A

cros

s co

untr

ies.

It m

ay a

lso

be o

f in

tere

st t

o po

licy

mak

ers

to c

ompa

re b

y re

gion

(e.g

. pro

vinc

e, u

rban

/rur

al) o

r so

cioe

cono

mic

sta

tus

(e.g

. wea

lth

quin

tile

, lev

el o

f ed

ucat

ion)

. Pa

st p

erfo

rman

ce/t

rend

dat

a.

Ther

e ar

e so

me

issu

es in

defi

ning

who

is in

clud

ed a

s a

skill

ed h

ealt

h pr

ofes

sion

al. I

n pa

rtic

ular

, com

mun

ity h

ealt

h w

orke

rs (C

HW

s) a

re g

ener

ally

not

incl

uded

whi

ch m

ay b

e pr

oble

mat

ic in

cou

ntrie

s w

here

CH

Ws

are

used

as

mai

n se

rvic

e pr

ovid

er t

o de

liver

bab

ies.

Whe

n co

nsid

erin

g w

heth

er t

o in

clud

e CH

Ws

or n

ot, i

t is

enc

oura

ged

to lo

ok a

t w

hat

trai

ning

eac

h ca

dre

has

and

wha

t se

rvic

es t

hey

are

mea

nt t

o pr

ovid

e.If

ava

ilabl

e, it

wou

ld b

e im

port

ant

to lo

ok fo

r inf

orm

atio

n on

ser

vice

-spe

cific

ava

ilabi

lity

and

read

ines

s fo

r bas

ic o

bste

tric

and

new

born

car

e, a

dher

ence

to

qual

ity a

ssur

ance

pro

toco

ls/g

uide

lines

, and

pro

vide

r kno

wle

dge

(e.g

. abi

lity

to

diag

nose

, tre

at, a

nd m

anag

e lif

e th

reat

enin

g co

mpl

icat

ions

suc

h as

pos

t-pa

rtum

hem

orrh

age

and

birt

h as

phyx

ia) f

rom

faci

lity

surv

eys

such

as

WH

O’s

Serv

ice

Avai

labi

lity

and

Read

ines

s A

sses

smen

t (S

ARA

) (ht

tps:

//ww

w.w

ho.in

t/he

alth

info

/sy

stem

s/sa

ra_i

ntro

duct

ion/

en/)

or t

he W

orld

Ban

k’s

Serv

ice

Del

iver

y In

dica

tors

(SD

I) (h

ttps

://w

ww

.sdi

ndic

ator

s.or

g)

Out

puts

(acc

ess)


Sim

ple

defin

itio

n:

TB c

ase

dete

ctio

n: P

erce

ntag

e of

est

imat

ed n

ew a

nd re

laps

e TB

cas

es d

etec

ted

in a

giv

en y

ear.

TB n

otifi

catio

n ra

te: N

umbe

r of n

ew a

nd re

laps

ed T

B ca

ses

notifi

ed in

a g

iven

yea

r. A

not

ifica

tion

mea

ns t

hat

the

case

was

repo

rted

on

the

natio

nal s

urve

illan

ce s

yste

m.

TB t

reat

men

t su

cces

s ra

te: P

erce

nt o

f TB

case

s th

at w

ere

succ

essf

ully

tre

ated

out

of a

ll no

tified

TB

case

s du

ring

a sp

ecifi

ed p

erio

d of

tim

e.

Freq

uenc

y of

dat

a co

llect

ion:

Adm

inis

trat

ive

data

cou

ld b

e co

llect

ed m

onth

ly, y

earl

y. F

acili

ty s

urve

ys a

re u

sual

ly a

d ho

c w

hen

fund

ing

is a

vaila

ble.

Inve

ntor

y or

logi

stic

s m

anag

emen

t sy

stem

s ca

n pr

oduc

e da

ily/r

eal-

tim

e re

port

s.

Sugg

este

d co

mpa

rato

r: A

cros

s co

untr

ies

or c

ompa

red

to p

ast

perf

orm

ance

/tre

nd d

ata.

It m

ay a

lso

be o

f int

eres

t to

pol

icy

mak

ers

to c

ompa

re b

y re

gion

(e.g

. pro

vinc

e, u

rban

/rur

al),

acro

ss f

acili

ty t

ype

(prim

ary

heal

th c

linic

vs

hosp

ital

) or

prov

ider

typ

e (p

ublic

/priv

ate)

.

TB c

ase

dete

ctio

n, T

B no

tifica

tion

rate

, and

TB

trea

tmen

t suc

cess

rate

Pote

ntia

l sou

rce

of d

ata:

Adm

inis

trat

ive

data

from

faci

litie

s,

surv

eilla

nce

syst

ems,

TB

regi

ster

s.

Ann

ual c

ase

notifi

catio

ns a

re

repo

rted

by

coun

trie

s to

the

WH

O

and

then

repo

rted

in T

B an

nual

re

port

s an

d in

the

cou

ntry

pro

files

.ht

tps:

//ww

w.w

ho.in

t/tb

/cou

ntry

/dat

a/profi

les/en/

Benc

hmar

king

gui

delin

esFo

r TB

case

det

ectio

n an

d no

tifica

tion

rate

the

re is

no

esta

blis

hed

benc

hmar

k, b

ut t

he h

ighe

r the

bet

ter.

Sugg

est

benc

hmar

king

aga

inst

pas

t pe

rfor

man

ce. F

or T

B tr

eatm

ent

succ

ess

rate

– a

t le

ast

90%

.

TB c

asca

de in

Indo

nesi

a, 2

017

Sour

ce: G

loba

l Tub

ercu

losi

s Re

port

201

8 (h

ttps

://w

ww

.who

.int/

tb/p

ublic

atio

ns/g

loba

l_re

port

/en/

)

It w

ould

be

help

ful t

o lo

ok a

t in

dica

tors

alo

ng t

he e

ntire

TB

casc

ade

to e

nsur

e co

ntin

uity

of c

are.

TB

trea

tmen

t su

cces

s ra

tes

will

onl

y be

a g

ood

mea

sure

of a

per

form

ance

if d

etec

tion

of T

B is

als

o hi

gh. T

reat

men

t su

cces

s ra

tes

also

rely

on

the

outc

ome

of t

he t

reat

men

t be

ing

reco

rded

in ro

utin

e in

form

atio

n sy

stem

s an

d so

tre

atm

ent

succ

ess

rate

s w

ill a

lso

rely

on

havi

ng ro

bust

sys

tem

s. R

outin

e ca

se n

otifi

catio

ns m

ay b

e bi

ased

if t

he p

rivat

e se

ctor

doe

s no

t/un

der-

repo

rts

new

ca

ses.

Sur

veill

ance

sys

tem

may

als

o on

ly re

gist

er b

acte

riolo

gica

lly c

onfir

med

cas

es -

esp

ecia

lly if

tes

ting

and

diag

nost

ic c

apac

ity is

wea

k.If

ava

ilabl

e, it

wou

ld b

e im

port

ant

to lo

ok fo

r inf

orm

atio

n on

TB

serv

ice-

spec

ific

avai

labi

lity

and

read

ines

s an

d pr

ovid

er k

now

ledg

e su

ch a

s di

agno

stic

acc

urac

y of

TB,

and

adh

eren

ce t

o qu

ality

ass

uran

ce p

roto

cols

/gui

delin

es fr

om fa

cilit

y su

rvey

s su

ch a

s W

HO

’s Se

rvic

e Av

aila

bilit

y an

d Re

adin

ess

Ass

essm

ent

(SA

RA) (

http

s://w

ww

.who

.int/

heal

thin

fo/s

yste

ms/

sara

_int

rodu

ctio

n/en

/) o

r the

Wor

ld B

ank’

s Se

rvic

e D

eliv

ery

Indi

cato

rs (S

DI)

(htt

ps://

ww

w.s

dind

icat

ors.

org)

A h

igh

TB n

otifi

catio

n ra

te m

ay a

lso

act

as g

ood

prox

y fo

r the

eff

ectiv

enes

s of

hea

lth

surv

eilla

nce

syst

ems

over

all.

8420

00

4462

6044

2172

3837

83.6

TB in

cide

nce

TB d

etec

tion

TB n

oti�

cati

onTB

trea

tmen

t and

suc

cess

53%

99%

87%

Number of patients

Out

puts

(qua

lity)

MEASURING HEALTH SYSTEM EFFICIENCY IN LOW- AND MIDDLE-INCOME COUNTRIES: A RESOURCE GUIDE36 Gen

eral

ser

vice

read

ines

s; S

ervi

ce s

peci

fic re

adin

ess

Benc

hmar

king

gui

delin

esN

o es

tabl

ishe

d be

nchm

ark,

th

e hi

gher

the

bet

ter.

Sugg

est

benc

hmar

king

ag

ains

t pa

st p

erfo

rman

ce,

aver

age

faci

lity/

prov

ider

, or

bes

t pe

rfor

mer

.

Gen

eral

ser

vice

read

ines

s an

d se

rvic

e sp

ecifi

c re

adin

ess

desc

ribes

the

ava

ilabi

lity

of t

he c

ompo

nent

s ne

eded

to

deliv

er s

ervi

ces

such

as

basi

c am

eniti

es, d

rugs

, and

equ

ipm

ent.

SA

RA, S

DI a

nd S

PA p

roto

cols

are

incr

easi

ngly

bei

ng s

tand

ardi

zed

to a

llow

gre

ater

com

para

bilit

y be

twee

n co

untr

ies.

Nev

erth

eles

s, s

tand

ard

defin

ition

s m

ay n

ot m

ake

sens

e fo

r the

ser

vice

del

iver

y co

ntex

t an

d as

sess

men

t ag

ains

t a

coun

try’

s ow

n st

anda

rds

and

prot

ocol

s is

als

o ad

vise

d.W

hile

oft

en u

sed

as a

pro

xy fo

r qua

lity

– se

rvic

e re

adin

ess

only

mea

sure

s av

aila

bilit

y of

tra

cer i

tem

s. It

is s

ugge

sted

to

com

plem

ent

this

mea

sure

with

oth

ers

such

as

diag

nost

ic a

ccur

acy,

adh

eren

ce t

o cl

inic

al g

uide

lines

, etc

.

All

faci

lity

type

s (4

9%)

0%20

%40

%60

%80

%10

0%0%

20%

40%

60%

80%

100%

0%20

%40

%60

%80

%10

0%

Priv

ate

Clin

ics

(67%

)PH

Cs (3

6%)

35%

40%

68%

36%

67%

50%

20%

36%

58%

79%

81%

74%

71%

77%

44%

52%

31%

34%

66%

31%

67%

45%

16%

32%

Basi

c A

men

ities

Infr

astr

uctu

re a

vaila

bilit

y (S

DI)

Basi

c eq

uipm

ent

Equi

pmen

t ava

ilabi

lity

(SD

I)

Infe

ctio

n pr

even

tion

Dia

gnos

tic c

apac

ity

Esse

ntia

l med

icin

e

Esse

ntia

l dru

g av

aila

bilit

y

Not

e: S

ARA

and

SD

I sur

veys

hav

e sl

ight

ly d

iffer

ent d

efini

tions

/che

cklis

t ite

ms

for b

asic

am

eniti

es/in

fras

truc

ture

ava

ilabi

lity,

equ

ipm

ent,

and

ess

entia

l med

icin

esSo

urce

: Nig

eria

Hea

lth F

acili

ty S

urve

y 20

15-2

016Gen

eral

ser

vice

read

ines

s by

faci

lity

type

, Nig

eria

.

Pote

ntia

l sou

rce

of d

ata:

Faci

lity

surv

eys

such

as

WH

O’s

Serv

ice

Avai

labi

lity

and

Read

ines

s A

sses

smen

t (S

ARA

) or W

orld

Ban

k’s

Serv

ice

Del

iver

y In

dica

tors

(S

DIs

); a

dmin

istr

ativ

e da

ta.

http

s://d

hspr

ogra

m.c

om/d

ata/

avai

labl

e-da

tase

ts.c

fmht

tps:

//mic

s.un

icef

.org

/sur

veys

Sim

ple

defin

itio

n: M

easu

res

the

capa

city

of h

ealt

h fa

cilit

ies

to p

rovi

de b

asic

ser

vice

s ba

sed

on t

he a

vaila

bilit

y of

sel

ecte

d tr

acer

item

s fo

r ba

sic

amen

itie

s, e

quip

men

t, e

ssen

tial

med

icin

es, d

iagn

osti

c ca

paci

ty,

and

stan

dard

pre

caut

ions

for

infe

ctio

n pr

even

tion

.

Freq

uenc

y of

dat

a co

llect

ion:

Sur

vey

data

is u

sual

ly d

one

ever

y 5

year

s. F

acili

ty s

urve

ys a

re u

sual

ly a

d ho

c w

hen

fund

ing

is a

vaila

ble.

Sugg

este

d co

mpa

rato

r: A

cros

s fa

cilit

ies

and/

or p

rovi

ders

. Acr

oss

diff

eren

t ty

pes

of s

ervi

ces.

Out

puts

(qua

lity)


Sim

ple

defin

itio

n: P

rovi

ders

cor

rect

ly d

iagn

ose

cond

itio

n X

base

d on

vig

nett

e su

rvey

dat

a.

Freq

uenc

y of

dat

a co

llect

ion:

Fac

ility

sur

veys

are

usu

ally

ad

hoc

whe

n fu

ndin

g is

ava

ilabl

e.

Sugg

este

d co

mpa

rato

r: A

cros

s fa

cilit

ies,

pro

vide

rs, c

ondi

tion

s.

Birt

h as

phyx

iaDi

abet

esDi

arrh

eaM

alar

iaPn

eum

onia

Post

-par

tum

hem

orrh

age

Tube

rcul

osis

PHCs

NC

NE

NW

SE SS SWPr

ivat

eNC

NE

NW

SE SS SW

0.0

100.

0

Dia

gnos

tic a

ccur

acy

for a

trac

er c

ondi

tion

Pote

ntia

l sou

rce

of d

ata:

Faci

lity

surv

eys

such

as

WH

O’s

Serv

ice

Avai

labi

lity

and

Read

ines

s A

sses

smen

t (S

ARA

) htt

ps://

ww

w.w

ho.

int/

heal

thin

fo/s

yste

ms/

sara

_rep

orts

/en

/ or W

orld

Ban

k’s

Serv

ice

Del

iver

y In

dica

tors

(SD

Is) h

ttps

://w

ww

.sd

indi

cato

rs.o

rg/in

dica

tors

Benc

hmar

king

gui

delin

esN

o es

tabl

ishe

d be

nchm

ark,

th

e hi

gher

the

bet

ter.

Sugg

est

benc

hmar

king

aga

inst

pas

t pe

rfor

man

ce a

vera

ge p

rovi

der,

or

bes

t pe

rfor

mer

.

Dia

gnos

tic

accu

racy

by

cond

itio

n, p

rovi

der t

ype,

and

regi

on (%

), N

iger

ia, 2

016

Prot

ocol

s ar

e in

crea

sing

ly b

eing

sta

ndar

dize

d to

allo

w g

reat

er c

ompa

rabi

lity

betw

een

coun

trie

s. H

owev

er, g

iven

pro

vide

r tra

inin

g an

d tr

eatm

ent

prot

ocol

s m

ay v

ary,

cou

ntrie

s ha

ve a

lso

conv

ened

focu

s gr

oups

to

disc

uss

vign

ette

in

terp

reta

tion

and

mak

e fin

ding

s m

ore

rele

vant

to

loca

l ser

vice

del

iver

y co

ntex

t. F

or t

his

reas

on it

may

be

bett

er t

o co

mpa

re d

iagn

ostic

acc

urac

y w

ithin

cou

ntry

acr

oss

regi

ons,

faci

litie

s, p

rovi

ders

.To

bet

ter a

sses

s pr

ovid

er k

now

ledg

e, it

is a

lso

reco

mm

ende

d to

look

at

addi

tiona

l ind

icat

ors

such

as

adhe

renc

e to

clin

ical

gui

delin

es, t

reat

men

t ac

cura

cy, a

nd a

bilit

y to

man

age

cert

ain

trac

er c

ondi

tions

whi

ch a

re g

ener

ally

als

o co

llect

ed a

s pa

rt o

f the

faci

lity

surv

ey v

igne

ttes

.

Not

e: N

iger

ia h

ealth

faci

lity

surv

ey, 2

015–

2016

Sour

ce: I

nven

tory

man

agem

ent s

yste

m, C

ount

ry X

. 201

7

Out

puts

(qua

lity)


Sim

ple

defin

itio

n: W

heth

er p

rovi

ders

ask

or

adhe

re t

o th

e qu

esti

ons

and

exam

s th

ey s

houl

d be

ask

ing

or d

oing

whe

n ch

ecki

ng a

pat

ient

for

a p

arti

cula

r co

ndit

ion.

Freq

uenc

y of

dat

a co

llect

ion:

Sur

vey

data

is u

sual

ly d

one

ever

y 5

year

s. F

acili

ty s

urve

ys a

re u

sual

ly a

d ho

c w

hen

fund

ing

is a

vaila

ble.

Sugg

este

d co

mpa

rato

r: A

cros

s fa

cilit

ies

and/

or p

rovi

ders

. Acr

oss

diff

eren

t ty

pes

of s

ervi

ces.

Adh

eren

ce to

clin

ical

gui

delin

es

Pote

ntia

l sou

rce

of d

ata:

• Fac

ility

sur

veys

suc

h as

WH

O’s

Serv

ice

Avai

labi

lity

and

Read

ines

s A

sses

smen

t (S

ARA

) htt

ps://

ww

w.

who

.int/

heal

thin

fo/s

yste

ms/

sara

_re

port

s/ o

r Wor

ld B

ank’

s Se

rvic

e D

eliv

ery

Indi

cato

rs (S

DIs

) htt

ps://

ww

w.s

dind

icat

ors.

org/

indi

cato

rs• H

ouse

hold

sur

veys

suc

h as

the

D

emog

raph

ic a

nd H

ealt

h Su

rvey

(D

HS)

htt

ps://

dhsp

rogr

am.c

om/d

ata/

avai

labl

e-da

tase

ts.c

fm. T

he U

NIC

EF

Mul

tiple

Indi

cato

r Clu

ster

Sur

vey

(MIC

S) h

ttps

://m

ics.

unic

ef.o

rg/s

urve

ys

can

also

be

used

if g

uide

lines

are

ex

plic

it as

to

wha

t se

rvic

es s

houl

d be

pro

vide

d fo

r cer

tain

typ

es o

f se

rvic

es e

.g. a

nten

atal

car

e vi

sits

.

Benc

hmar

king

gui

delin

esN

o es

tabl

ishe

d be

nchm

ark,

th

e hi

gher

the

bet

ter.

Sugg

est

benc

hmar

king

aga

inst

pas

t pe

rfor

man

ce, o

r bes

t pe

rfor

mer

av

erag

e fa

cilit

y/pr

ovid

er.

Sour

ce: N

iger

ia H

ealth

Fac

ility

Sur

vey

2015

-201

6

Impo

rtan

t pr

ecur

sors

to

bein

g ab

le t

o m

easu

re t

his

indi

cato

r inc

lude

cle

arly

defi

ned

trea

tmen

t pr

otoc

ols,

tra

ined

ser

vice

pro

vide

rs, a

nd g

ood

docu

men

tatio

n or

info

rmat

ion

syst

ems

to b

e ab

le t

o m

onito

r adh

eren

ce.

Adh

eren

ce t

o cl

inic

al g

uide

lines

may

als

o be

influ

ence

d by

mul

tiple

sys

tem

fact

ors.

It is

sug

gest

ed t

o co

mpl

emen

t th

is m

easu

re w

ith s

ervi

ce re

adin

ess

mea

sure

s su

ch a

s a

vaila

bilit

y of

dru

gs; d

iagn

ostic

cap

acity

and

pro

vide

r kno

wle

dge

mea

sure

s su

ch a

s di

agno

stic

acc

urac

y.

Adh

eren

ce t

o cl

inic

al g

uide

lines

by

cond

itio

n %

, Nig

eria

.

Dia

rrhe

a, 4

3

Pneu

mon

ia, 4

0D

iabe

tes,

41

Mal

aria

, 50

Birt

h A

sphy

xia,

17

Post

-par

tum

he

mor

rhag

e, 3

2

Tube

rcul

osis

, 44

Dia

rrhe

a, 3

1

Pneu

mon

ia, 2

7

Dia

bete

s, 2

3

Mal

aria

, 39

Birt

h A

sphy

xia,

9

Post

-par

tum

he

mor

rhag

e, 1

7

Tube

rcul

osis

, 30

Publ

icPr

ivat

e

Out

puts

(qua

lity)

MEASURING HEALTH SYSTEM EFFICIENCY IN LOW- AND MIDDLE-INCOME COUNTRIES: A RESOURCE GUIDE 39Dia

bete

s con

trol

Sim

ple

defin

ition

: Per

cent

age

of p

atie

nts

who

hav

e th

eir d

iabe

tes

unde

r con

trol

. Peo

ple

with

dia

bete

s ge

nera

lly h

ave

high

leve

ls o

f glu

cose

in th

e bl

ood

mea

sure

d by

a h

emog

lobi

n A

1c>6

.5%

and

/or b

lood

glu

cose

>12

6mg/

dL.

Freq

uenc

y of

dat

a co

llect

ion:

Ana

lyze

d m

onth

ly, q

uart

erly

, ann

ually

. Sur

vey

data

usu

ally

eve

ry 5

yea

rs.

Sugg

este

d co

mpa

rato

r: A

cros

s fa

cilit

ies/

hosp

itals

; aga

inst

pas

t per

form

ance

; acr

oss

regi

ons

with

in c

ount

ry.

Pote

ntia

l sou

rce

of d

ata:

Med

ical

reco

rds,

adm

inis

trat

ive

reco

rds

or s

urve

illan

ce s

yste

ms

like

natio

nal d

iabe

tes

patie

nt re

gist

ries.

H

ouse

hold

sur

veys

suc

h as

the

Dem

ogra

phic

and

Hea

lth S

urve

y (D

HS)

, the

UN

ICEF

Mul

tiple

Indi

cato

r Cl

uste

r Sur

vey

(MIC

S).

Benc

hmar

king

gui

delin

esTh

ere

are

clin

ical

gui

delin

es th

at

act a

s be

nchm

arks

. Whi

le ta

rget

s m

ay b

e sp

ecifi

c to

cou

ntrie

s’ c

linic

al

prot

ocol

s, in

gen

eral

dia

bete

s co

ntro

l re

quire

s an

HbA

1c te

st b

etw

een

7-8%

fo

r pat

ient

s w

ith ty

pe II

dia

bete

s or

Fa

stin

g bl

ood

gluc

ose

betw

een

80-

130

mg/

dL o

r 4.4

to 7

.2 m

mol

/L.

If a

vaila

ble,

it w

ould

be

impo

rtan

t to

look

for i

nfor

mat

ion

on i)

dia

bete

s se

rvic

e-sp

ecifi

c av

aila

bilit

y an

d re

adin

ess

(i.e.

bas

ic it

ems

need

ed t

o te

st a

nd t

reat

dia

bete

s), i

i) ad

here

nce

to q

ualit

y as

sura

nce

prot

ocol

s/gu

idel

ines

, and

iii)

prov

ider

kn

owle

dge

mea

sure

s su

ch a

s di

agno

stic

acc

urac

y fr

om fa

cilit

y su

rvey

s su

ch a

s W

HO

’s Se

rvic

e Av

aila

bilit

y an

d Re

adin

ess

Ass

essm

ent

(SA

RA) o

r the

Wor

ld B

ank’

s Se

rvic

e D

eliv

ery

Indi

cato

rs (S

DI).

It w

ould

als

o be

hel

pful

to

look

at

a ra

nge

of

indi

cato

rs a

long

the

ent

ire d

iabe

tes

casc

ade

(e.g

. dia

bete

s pr

eval

ence

; per

cent

dia

gnos

ed; p

erce

nt o

n tr

eatm

ent;

per

cent

und

er c

ontr

ol; d

iabe

tes-

rela

ted

hosp

ital a

dmis

sion

s; a

nd m

orta

lity

due

to d

iabe

tes)

Mal

aysi

a di

abet

es c

asca

de

Not

e: *

Bas

ed o

n sa

mpl

e-ba

sed

clin

ical

aud

it of

dia

gnos

ed d

iabe

tes

patie

nts.

htt

p://w

ww

.iku.

gov.

my/

imag

es/IK

U/D

ocum

ent/

REPO

RT/

nhm

srep

ort2

015v

ol2.

pdf

Sour

ces:

Inte

rnat

iona

l Dia

bete

s Fe

dera

tion

(htt

ps://

idf.o

rg/o

ur-n

etw

ork/

regi

ons-

mem

bers

/wes

tern

-pac

ific/

mem

bers

/108

-mal

aysi

a.ht

ml)

and

*Nat

iona

l Dia

bete

s Re

gist

ry o

f Mal

aysi

a

Prev

alen

ceD

iagn

osed

Am

ong

diag

nose

d*A

mon

g di

agno

sed,

un

der c

ontr

ol

(HbA

1c <

8%)*

16.9

40.6

86.5

58.2

4,00

0,00

0

3,50

0,00

0

3,00

0,00

0

2,50

0,00

0

2,00

0,00

0

1,50

0,00

0

1,00

0,00

0

500,

000 0

100

90 80 70 60 50 40 30 20 10 0

Absolute number

%

Out

puts

(qua

lity)


Pot

enti

ally

avo

idab

le a

dmis

sion

s fo

r ch

roni

c ill

ness

es t

hat

coul

d be

ave

rted

if t

he p

atie

nts

had

qual

ity

outp

atie

nt c

are.

Cond

itio

ns f

or w

hich

hos

pita

l adm

issi

ons

coul

d be

pre

vent

ed b

y in

terv

enti

ons

in c

omm

unit

y se

ttin

gs.

Freq

uenc

y of

dat

a co

llect

ion:

Usu

ally

ana

lyze

d m

onth

ly, q

uart

erly

or

annu

ally

. Hou

seho

lds

surv

eys

are

less

freq

uent

ly a

vaila

ble

and

not

sugg

este

d fo

r ro

utin

e m

onit

orin

g.

Sugg

este

d co

mpa

rato

r: A

cros

s fa

cilit

ies/

hosp

ital

s. It

may

be

usef

ul f

or p

olic

y m

aker

s to

hav

e co

mpa

rison

s ag

greg

ated

at

subn

atio

nal l

evel

(e.g

. pro

vinc

es, d

istr

icts

).

Avoi

dabl

e ho

spita

l adm

issi

ons

for p

rimar

y ca

re s

ensi

tive

cond

ition

s

Pote

ntia

l sou

rce

of d

ata:

Idea

lly c

laim

s da

ta a

nd/o

r med

ical

re

cord

s. S

ome

hous

ehol

d su

rvey

s w

ith a

det

aile

d he

alth

mod

ule.

Benc

hmar

king

gui

delin

esN

o be

nchm

ark,

the

low

er is

bet

ter.

Sugg

est

benc

hmar

king

aga

inst

pas

t pe

rfor

man

ce o

r bes

t pe

rfor

mer

av

erag

e fa

cilit

y/pr

ovid

er.

Hyp

erte

nsio

n an

d di

abet

es a

dmis

sion

by

faci

lity,

Nor

th M

aced

onia

, 201

6

Sour

ce: H

ealth

Insu

ranc

e Fu

nd o

f Nor

th M

aced

onia

vary

. Con

ditio

ns g

ener

ally

con

side

red

are

diab

etes

, hyp

erte

nsio

n, a

nd a

cute

res

pira

tory

con

ditio

ns s

uch

as b

ronc

hitis

, ast

hma,

and

chr

onic

obs

truc

tive

pulm

onar

y di

seas

e as

mos

t ar

e no

t as

soci

ated

with

com

plic

atio

ns a

nd s

houl

d no

t re

quire

ho

spita

l adm

issi

ons.

With

in c

ount

ry c

ompa

rison

is a

lso

reco

mm

ende

d as

adm

issi

on a

nd t

reat

men

t pr

otoc

ols

and

the

read

ines

s an

d qu

ality

of

prim

ary

heal

th c

are

to a

ppro

pria

tely

man

age

thes

e co

nditi

ons

may

var

y. A

s th

e qu

ality

of

clai

ms

data

impr

oves

, cas

e-m

ix s

ever

ity s

houl

d al

so b

e ta

ken

into

acc

ount

.

Out

puts

(qua

lity)

050100

150

200

250

300

350

General municip

al hospita

l in Sko

pjeBito

la Clinica

l Hospita

l

Endocrinology U

niversi

ty hospita

l in Sko

pje

Gos�va

r Gen

eral Hosp

ital

Struga General H

ospita

lSh

�p Clinica

l Hosp

ital

Tetovo Clin

ical h

ospita

lPrile

p General Hosp

ital

Kavadarts

i General H

ospita

l

Kicevo General H

ospita

lVeles G

eneral Hosp

ital

Kocani G

eneral Hosp

ital

Debar Gen

eral Hosp

ital

Kumanovo General Hosp

ital

Strumica

General Hosp

ital

Ohrid General H

ospita

l

Gevgelija

General Hospita

l

Number of admissionsH

yper

tens

ion

and

diab

etes

adm

issi

ons b

y fa

cilit

y, M

aced

onia

, 201

6

Hyp

erte

nsio

n w

ithou

t com

plic

a�on

sD

iabe

tes w

ithou

t sev

ere

com

plic

a�on

s


Inco

me

Coun

try

Mal

e

Fem

ale

Low

inco

me

Low

er m

iddl

e in

com

eU

pper

mid

dle

inco

me

Hig

h in

com

e

0204002040

Congo, Dem. RepNiger

EthiopiaChad

Sierra LeoneGuineaLiberia

CambodiaComoros

ZimbabweKorea, Dem. People’s Rep.

TogoTajikistan

Gambia, TheHaiti

Kyrgyz RepublicTimor-Leste

PakistanYemen, Rep.

GuatemalaZambia

IndiaIndonesia

BangladeshNigeria

DjiboutiCote d’Ivoire

PhilippinesMauritania

HondurasSenegal

GhanaSri LankaParaguayMoldovaEcuadorNamibia

GabonAzerbaijan

ThailandPeru

MexicoAlgeriaTunisiaTurkey

MontenegroJordan

Bosnia and HerzegovinaDominican Republic

JamaicaBarbados

KuwaitUnited States

Chile

Pote

ntia

l sou

rce

of d

ata:

Pote

ntia

l Sou

rce

Hou

seho

ld s

urve

ys

such

as

the

Dem

ogra

phic

and

Hea

lth

Surv

ey (D

HS)

(htt

ps://

dhsp

rogr

am.c

om),

the

UN

ICEF

Mul

tiple

Indi

cato

r Clu

ster

Su

rvey

(MIC

S) (h

ttps

://m

ics.

unic

ef.o

rg).

Nat

iona

l cen

sus

surv

eys.

Glo

bal

data

base

s fo

r cou

ntry

com

paris

ons:

th

e H

uman

Cap

ital P

roje

ct u

ses

data

on

stu

ntin

g fo

r con

stru

ctio

n of

its

HCI

from

the

UN

ICEF

-WH

O-T

he

Wor

ld B

ank:

Joi

nt C

hild

Mal

nutr

ition

Es

timat

es. (

http

s://w

ww

.wor

ldba

nk.o

rg/

en/p

ublic

atio

n/hu

man

-cap

ital) .

Benc

hmar

king

gui

delin

esN

o es

tabl

ishe

d be

nchm

ark,

the

lo

wer

the

bet

ter.

As

part

of t

he W

HO

G

loba

l Nut

ritio

n Ta

rget

s, a

40%

re

duct

ion

in t

he n

umbe

r of c

hild

ren

unde

r 5 w

ho a

re s

tunt

ed b

y 20

25

(thi

s is

roug

hly

10%

per

yea

r) u

sing

20

12 a

s ba

selin

e.

Prev

alen

ce o

f stu

ntin

g, c

hild

ren

unde

r age

5 (%

)

Stun

ting

refle

cts

poor

nut

ritio

n an

d in

fect

ion

expo

sure

dur

ing

preg

nanc

y an

d af

ter b

irth.

The

refo

re, i

f ava

ilabl

e, it

wou

ld b

e im

port

ant

to lo

ok fo

r inf

orm

atio

n on

AN

C an

d ch

ild h

ealt

h se

rvic

e-sp

ecifi

c av

aila

bilit

y an

d re

adin

ess,

dia

gnos

tic

accu

racy

of c

omm

on c

hild

hood

con

ditio

ns li

ke d

iarr

hea,

and

adh

eren

ce t

o qu

ality

ass

uran

ce p

roto

cols

/gui

delin

es fr

om fa

cilit

y su

rvey

s su

ch a

s W

HO

’s Se

rvic

e Av

aila

bilit

y an

d Re

adin

ess

Ass

essm

ent

(SA

RA) (

http

s://w

ww

.who

.int/

heal

thin

fo/

syst

ems/

sara

_int

rodu

ctio

n/en

/) o

r the

Wor

ld B

ank’

s Se

rvic

e D

eliv

ery

Indi

cato

rs (S

DI)

(htt

ps://

ww

w.s

dind

icat

ors.

org)

.

Not

e: M

ost r

ecen

t val

ue in

201

2-20

15So

urce

: Wor

ld B

ank

(201

9). W

orld

Dev

elop

men

t Ind

icat

ors

Child

ren

unde

r 5 w

ho a

re s

tunt

edSi

mpl

e de

finit

ion:

Per

cent

age

of s

tunt

ed (m

oder

ate

and

seve

re) c

hild

ren

aged

0–5

9 m

onth

s (m

oder

ate

= he

ight

-for

-age

bel

ow -

2 st

anda

rd d

evia

tion

s fr

om t

he W

HO

Chi

ld G

row

th S

tand

ards

med

ian;

se

vere

= h

eigh

t-fo

r-ag

e be

low

-3

stan

dard

dev

iati

ons

from

the

WH

O C

hild

Gro

wth

Sta

ndar

ds m

edia

n).

Freq

uenc

y of

dat

a co

llect

ion:

Sur

vey

data

usu

ally

eve

ry 5

yea

rs. U

NIC

EF-W

HO

-The

Wor

ld B

ank:

Joi

nt M

alnu

trit

ion

Esti

mat

es u

pdat

ed a

nnua

lly.

Sugg

este

d co

mpa

rato

r: A

cros

s co

untr

ies.

It m

ay a

lso

be o

f in

tere

st t

o po

licy

mak

ers

to c

ompa

re b

y re

gion

(e.g

. pro

vinc

e, u

rban

/rur

al) o

r so

cioe

cono

mic

sta

tus

(e.g

. wea

lth

quin

tile

, lev

el o

f ed

ucat

ion)

. Pa

st p

erfo

rman

ce/t

rend

dat

a.

Out

puts

(ris

k fa

ctor

s)


KiribatiNauru

GreeceSerbia

Russian FederationBosnia and Herzegovi..

ChileIndonesia

JordanLebanonBulgaria

CubaCroatiaLatvia

Sierra LeoneLao PDR

Czech RepublicEstonia

AndorraSamoa

UkraineGermanyRomania

IsraelSpain

BahrainTonga

LithuaniaGeorgiaAlbania

HungaryPoland

Slovak RepublicFrance

ArmeniaBelarus

Korea, Rep.Turkey

MongoliaSeychellesPhilippines

FijiKyrgyz Republic

KazakhstanMaltaChina

NetherlandsLesotho

ArgentinaUruguay

60 50 40 30 20

Pote

ntia

l sou

rce

of d

ata:

Hou

seho

ld s

urve

ys s

uch

as t

he

Dem

ogra

phic

and

Hea

lth

Surv

ey

(DH

S),

the

UN

ICEF

Mul

tiple

Indi

cato

r Cl

uste

r Sur

vey

(MIC

S), W

orld

Hea

lth

Org

aniz

atio

n ST

EPw

ise

(STE

PS)

surv

ey a

nd n

atio

nal s

urve

ys.

http

s://d

hspr

ogra

m.c

omht

tps:

//mic

s.un

icef

.org

http

s://w

ww

.who

.int/

ncds

/sur

veill

ance

/st

eps/

repo

rts/

en/

Benc

hmar

king

gui

delin

esN

o es

tabl

ishe

d be

nchm

ark,

the

low

er

the

bett

er. G

loba

l vol

unta

ry t

arge

t of

30

% re

lativ

e re

duct

ion

in t

he u

se o

f to

bacc

o in

per

sons

ove

r 15

year

s by

20

20 (r

efer

ence

yea

r: 2

013)

.

Prev

alen

ce o

f sm

okin

g of

any

tob

acco

pro

duct

, 201

3

Surv

eys

may

hav

e di

ffer

ent

incl

usio

n cr

iteria

bas

ed o

n ag

e. F

or in

stan

ce, S

TEPS

sur

vey

will

gen

eral

ly a

sk t

hose

18

year

s or

old

er w

here

as D

HS

asks

tho

se w

ho a

re 1

5 ye

ars

or o

lder

. Cur

rent

use

defi

nitio

ns m

ay a

lso

vary

dep

endi

ng o

n th

e su

rvey

. Tob

acco

use

can

be

both

sm

oke

and

non-

smok

ed b

ased

use

d.

Toba

cco

use

amon

g pe

rson

s ag

ed 18

+Si

mpl

e de

finit

ion:

Pre

vale

nce

of c

urre

nt t

obac

co u

se a

mon

g pe

rson

s ag

ed 1

8+ y

ears

.

Freq

uenc

y of

dat

a co

llect

ion:

Sur

vey

data

usu

ally

eve

ry 5

yea

rs.

Sugg

este

d co

mpa

rato

r: A

cros

s co

untr

ies.

It m

ay a

lso

be o

f in

tere

st t

o po

licy

mak

ers

to c

ompa

re b

y so

cioe

cono

mic

sta

tus

(e.g

. wea

lth

quin

tile

, lev

el o

f ed

ucat

ion,

gen

der)

. Pas

t pe

rfor

man

ce/t

rend

dat

a.

Not

e: T

op 5

0 hi

ghes

t cou

ntrie

sSo

urce

: Wor

ld B

ank

(201

9). W

orld

Dev

elop

men

t Ind

icat

ors

(htt

p://w

di.w

orld

bank

.org

)

Out

puts

(ris

k fa

ctor

s)


ntia

l sou

rce

of d

ata:

Faci

lity

surv

eys

such

as

WH

O’s

Serv

ice

Avai

labi

lity

and

Read

ines

s A

sses

smen

t (S

ARA

: htt

ps://

ww

w.w

ho.

int/

heal

thin

fo/s

yste

ms/

sara

_rep

orts

/en

/ ) or

Wor

ld B

ank’

s Se

rvic

e D

eliv

ery

Indi

cato

rs (S

DIs

: htt

ps://

ww

w.s

dind

icat

ors.

org/

indi

cato

rs);

ad

min

istr

ativ

e/fa

cilit

y re

cord

s.

Benc

hmar

king

gui

delin

esN

o es

tabl

ishe

d be

nchm

ark.

Su

gges

t be

nchm

arki

ng a

gain

st p

ast

perf

orm

ance

or a

vera

ge fa

cilit

y.

In O

ECD

cou

ntrie

s ge

nera

l pr

actit

ione

rs s

ee o

n av

erag

e be

twee

n 11

to

30 p

atie

nts

a da

y w

hile

in S

ub-

Saha

ran

Afr

ica

whe

re m

ost

of t

he S

DI

surv

eys

have

tak

en p

lace

cas

eloa

d ra

nges

bet

wee

n 4

to 1

7.

Case

load

Sour

ce: W

orld

Ban

k (2

016)

. Nig

eria

Nat

iona

l Hea

lth F

acili

ty S

urve

y

Case

load

is a

com

plex

indi

cato

r to

inte

rpre

t. It

cou

ld b

e aff

ecte

d by

man

y su

pply

sid

e is

sues

suc

h as

the

ove

r- o

r und

er-s

uppl

y of

hum

an re

sour

ces

the

sala

ry a

nd t

imel

ines

s of

pay

men

t to

pro

vide

rs, a

bsen

teei

sm ra

tes,

the

num

ber o

f pa

tient

s re

gist

ered

to

a pr

imar

y he

alth

car

e fa

cilit

y, t

he a

vaila

bilit

y of

dru

gs a

nd e

quip

men

t, t

he fu

nctio

nalit

y of

faci

litie

s, t

he s

ervi

ce p

rovi

sion

resp

onsi

bilit

ies

of d

iffer

ent

cadr

es o

r by

dem

and

side

issu

es s

uch

as a

pre

fere

nce

for d

iffer

ent

prov

ider

typ

es o

r set

tings

, the

dis

ease

bur

den,

the

aff

orda

bilit

y of

car

e, t

he d

ista

nce

to fa

cilit

ies,

or t

he p

erce

ived

qua

lity

of c

are.

Thi

s m

akes

it d

ifficu

lt t

o co

mpa

re a

cros

s co

untr

ies

as t

he h

ealt

h ca

re c

onte

xt m

ay b

e di

ffer

ent.

For

exa

mpl

e,

com

mun

ity h

ealt

h w

orke

rs (C

HW

s) a

re n

ot in

clud

ed in

the

WB’

s SD

I defi

nitio

n w

hich

may

be

prob

lem

atic

in c

ount

ries

that

are

incr

easi

ngly

shi

ftin

g ta

sks

to C

HW

s an

d re

lyin

g on

the

m t

o pr

ovid

e ba

sic

heal

th s

ervi

ces.

Eve

n w

ithin

cou

ntrie

s,

it is

reco

mm

ende

d to

pai

r thi

s in

dica

tor w

ith o

ther

s (e

.g. a

bsen

teei

sm, d

elay

of p

aym

ent,

num

ber o

f pat

ient

s re

gist

ered

to

a fa

cilit

y or

pro

vide

r, av

erag

e nu

mbe

r of p

atie

nt v

isits

with

in a

yea

r etc

.) to

iden

tity

the

appr

opria

te p

olic

y ac

tion.

W

hile

cas

eloa

d is

gen

eral

ly a

n ou

tpat

ient

/prim

ary

heal

th c

are

mea

sure

, oth

er m

easu

res

of p

rodu

ctiv

ity s

uch

as t

ime

per p

atie

nt p

er v

isit

(tho

ugh

less

com

mon

) hav

e al

so b

een

used

.

©20

19 M

apbo

x ©

Ope

nStr

eetM

ap

0-1

1-1.

51.

5-2

2-2.

52.

5-3

3-4

>4

Case

load

Sim

ple

defin

itio

n: N

umbe

r of

out

pati

ent

visi

ts p

er c

linic

ian

per

day.

Freq

uenc

y of

dat

a co

llect

ion:

Fac

ility

sur

veys

are

usu

ally

ad

hoc

whe

n fu

ndin

g is

ava

ilabl

e. A

dmin

istr

ativ

e/fa

cilit

y re

cord

s ar

e ge

nera

lly a

naly

zed

mon

thly

, qua

rter

ly, o

r an

nual

ly.

Sugg

este

d co

mpa

rato

r: A

cros

s fa

cilit

ies,

typ

es o

f pro

vide

rs.

Out

puts

(man

agem

ent)

MEASURING HEALTH SYSTEM EFFICIENCY IN LOW- AND MIDDLE-INCOME COUNTRIES: A RESOURCE GUIDE44 Abs

ente

eism

ty fr

om th

e fa

cilit

y on

an

unan

noun

ced

visi

t.

Freq

uenc

y of

dat

a co

llect

ion:

Adm

inis

trat

ive

data

is u

sual

ly a

naly

zed

mon

thly

, qua

rter

ly, a

nd/o

r ann

ually

. Fac

ility

sur

veys

are

usu

ally

ad

hoc

whe

n fu

ndin

g is

ava

ilabl

e.

Sugg

este

d co

mpa

rato

r: A

cros

s fa

cilit

ies,

pro

vide

rs.

Pote

ntia

l sou

rce

of d

ata:

Adm

inis

trat

ive

data

. Fac

ility

sur

veys

su

ch a

s W

HO

’s Se

rvic

e Av

aila

bilit

y an

d Re

adin

ess

Ass

essm

ent (

SARA

) (h

ttps

://w

ww

.who

.int/

heal

thin

fo/

syst

ems/

sara

_int

rodu

ctio

n/en

/).

Or W

orld

Ban

k’s

Serv

ice

Del

iver

y In

dica

tors

(SD

Is) (

http

s://w

ww

.sd

indi

cato

rs.o

rg/)

.

Benc

hmar

king

gui

delin

esN

o es

tabl

ishe

d be

nchm

ark,

the

low

er

the

bett

er. S

ugge

st b

ench

mar

king

ag

ains

t pas

t per

form

ance

or a

vera

ge

faci

lity/

prov

ider

.

Onl

y un

-exc

used

abs

ence

s sh

ould

be

acco

unte

d fo

r - fo

r exa

mpl

e if

prov

ider

is o

n a

trai

ning

, sic

k, o

r on

leav

e th

ey d

o no

t cou

nt a

s ab

sent

. It i

s al

so im

port

ant t

o ch

eck

whi

ch p

rovi

ders

are

cou

nted

whe

n do

ing

cros

s co

untr

y co

mpa

rato

rs.

Sour

ce: A

dmin

istr

ativ

e ho

spita

l dat

a, C

ount

ry X

, 201

7

Abs

ente

eism

am

ong

prim

ary

heal

th c

are

prov

ider

s, c

ount

ry a

vera

ge (%

)

Uga

nda

(201

3)To

go

(201

3)G

uine

a Bi

ssau

(2

018)

Nig

er

(201

5)N

iger

ia

(201

3)Si

erra

Le

one

(201

8)

Keny

a (2

013)

Mad

agas

car

(201

6)M

ozam

biqu

e (2

014)

Sene

gal

(201

0)Ta

nzan

ia

(201

4)

46.7

37.6

34.2

33.1

31.7

29.9

27.5

27.4

23.9

2014

.3

50 45 40 35 30 25 20 15 10 5 0

Absenteeism rate (%)

Out

puts

(man

agem

ent)


ntia

l sou

rce

of d

ata:

Adm

inis

trat

ive

data

from

fa

cilit

ies,

bill

ing

or c

laim

s da

ta.

Benc

hmar

king

gui

delin

esA

LOS:

No

esta

blis

hed

benc

hmar

k.

Sugg

est

benc

hmar

king

aga

inst

pa

st p

erfo

rman

ce o

r to

coun

try

hosp

ital a

vera

geBO

R: A

n op

timal

bed

occ

upan

cy

rate

of 8

5% h

as o

ften

bee

n ci

ted

in

the

liter

atur

e al

thou

gh it

is u

ncle

ar

whe

ther

thi

s is

bas

ed o

n cl

inic

al

indi

catio

n. H

owev

er, b

ed o

ccup

ancy

ra

tes

abov

e 85

% h

ave

been

obs

erve

d to

resu

lt in

regu

lar b

ed s

hort

ages

an

d hi

gher

hea

lthc

are-

acqu

ired

infe

ctio

ns.

Sour

ce: M

inis

try

of H

ealth

, Mal

aysi

a (2

017)

Leng

th o

f sta

y an

d by

ext

ensi

on b

ed o

ccup

ancy

rate

s ca

n be

a re

flect

ion

of t

he e

ffici

ency

of h

ospi

tal s

ervi

ces

as a

sho

rter

sta

y w

ill re

duce

cos

ts a

nd s

hift

the

car

e fr

om in

patie

nt t

o ou

tpat

ient

set

tings

. How

ever

, it

is d

ifficu

lt t

o co

mpa

re t

his

mea

sure

acr

oss

coun

trie

s as

tre

atm

ent

prot

ocol

s m

ay v

ary.

ALO

S m

ay a

lso

refle

ct t

he re

adin

ess

and/

or q

ualit

y of

out

patie

nt/p

rimar

y he

alth

car

e. T

here

fore

com

paris

on b

etw

een

hosp

itals

with

in-c

ount

ry is

reco

mm

ende

d. A

s th

e qu

ality

of

clai

ms

data

impr

oves

it is

reco

mm

ende

d to

com

pare

ALO

S by

cas

e-m

ix.

Ave

rage

leng

th o

f sta

y (d

ays)

vs

bed

occu

panc

y ra

te (%

) by

stat

e, M

alay

sia,

201

7

BOR

(%)

ALOS (days)

PERL

IS

PULA

U P

INA

NG

PERA

K

SELA

NG

OR

WP

KUA

LA L

AM

PUR

WP

PUTR

AJA

YA

WP

LABU

AN

NEG

ERI S

EMBI

LAN

MEL

AKA

JOH

OR PA

HA

NG

TERE

NG

GA

NU

KELA

NTA

N

SABA

H

SARA

WA

K

3

3.54

4.55

3540

4550

5560

6570

7580

8590

9510

0

Ave

rage

ALO

S: 3

.81

days

Ave

rage

BO

R: 6

4.2

%

KEH

DA

H

Aver

age

leng

th o

f sta

y (A

LOS)

vs.

Bed

occ

upan

cy ra

te (B

OR)

Sim

ple

defin

itio

n: A

LOS:

Tot

al a

vera

ge n

umbe

r of

bed

-day

s fo

r a

hosp

ital

adm

issi

on o

r di

scha

rge.

Bed

-day

s re

fer

to t

he n

umbe

r of

bed

s in

the

fac

ility

mul

tipl

ied

by t

he n

umbe

r of

day

s ob

serv

ed.

BOR:

The

per

cent

age

of b

ed-d

ays

that

are

occ

upie

d ov

er a

spe

cifie

d pe

riod

of t

ime.

Occ

upie

d be

d-da

ys r

efer

s to

the

num

ber

of d

ays

the

beds

wer

e oc

cupi

ed.

Freq

uenc

y of

dat

a co

llect

ion:

Adm

inis

trat

ive

data

is u

sual

ly c

olle

cted

dai

ly a

nd a

naly

zed

mon

thly

, qua

rter

ly, a

nd/o

r an

nual

ly.

Sugg

este

d co

mpa

rato

r: A

cros

s ho

spit

als,

typ

e of

hos

pita

ls.

WP

KUA

LA L

UM

PUR

Out

puts

(man

agem

ent)

MEASURING HEALTH SYSTEM EFFICIENCY IN LOW- AND MIDDLE-INCOME COUNTRIES: A RESOURCE GUIDE46 Hos

pita

l re-

adm

issi

on ra

teSi

mpl

e de

finiti

on: P

erce

ntag

e of

unp

lann

ed a

nd u

nexp

ecte

d ho

spita

l rea

dmis

sion

s fo

r con

ditio

ns th

at a

re tr

acke

d th

roug

h th

e he

alth

sys

tem

like

acu

te m

yoca

rdia

l inf

arct

ion,

pne

umon

ia, a

sthm

a an

d di

abet

es.

Freq

uenc

y of

dat

a co

llect

ion:

Usu

ally

ana

lyze

d m

onth

ly o

r ann

ually

.

Sugg

este

d co

mpa

rato

r: A

cros

s fa

cilit

ies/

hosp

itals

.

Pote

ntia

l sou

rce

of d

ata:

Adm

inis

trat

ive

data

, cla

ims

data

, m

edic

al re

cord

s.

Benc

hmar

king

gui

delin

esN

o es

tabl

ishe

d be

nchm

ark.

Su

gges

t ben

chm

arki

ng a

gain

st p

ast

perf

orm

ance

or a

vera

ge fa

cilit

y.

Sour

ce: A

dmin

istr

ativ

e ho

spita

l dat

a, C

ount

ry X

, 201

7

This

indi

cato

r is

tric

ky to

mea

sure

acr

oss

coun

trie

s as

it is

influ

ence

d by

fact

ors

such

as

trea

tmen

t pro

toco

ls, p

rovi

der i

ncen

tives

, qua

lity

of c

are,

and

the

avai

labi

lity

of o

utpa

tient

and

com

mun

ity c

are.

For

thes

e re

ason

s, i

t is

sugg

este

d to

look

for o

utlie

r fac

ilitie

s or

pro

vide

rs w

ithin

cou

ntrie

s or

regi

ons.

If d

oing

cro

ss-c

ount

ry c

ompa

rison

, it i

s im

port

ant t

o ch

eck

how

read

mis

sion

s ar

e de

fined

– fo

r wha

t con

ditio

ns a

nd w

ithin

w

hat t

ime

perio

d. C

omm

on ti

me

perio

ds a

re 3

0-da

y an

d 90

-day

read

mis

sion

s an

d co

mm

on c

ondi

tions

incl

ude

chro

nic

obst

ruct

ive

pulm

onar

y di

seas

e, a

cute

myo

card

ial i

nfar

ctio

n, p

neum

onia

, ast

hma,

dia

bete

s, h

ip a

nd

knee

repl

acem

ents

. Ana

lysi

s is

als

o de

pend

ent o

n th

e qu

ality

of d

ata,

goo

d m

edic

al re

cord

kee

ping

, cod

ing

prac

tices

, and

repo

rtin

g co

mpl

ianc

e. T

his

indi

cato

r als

o re

quire

s un

ique

pat

ient

iden

tifier

s to

be

able

to tr

ack

patie

nts

in th

e he

alth

care

sys

tem

.

30-d

ay H

ospi

tal R

eadm

issi

on ra

te (%

), M

ay 2

017

Nat

iona

l A

vera

geH

ospi

tal 1

Hos

pita

l 2H

ospi

tal 3

Hos

pita

l 4H

ospi

tal 5

Hos

pita

l 6H

ospi

tal 7

22.3

25.1

21.2

16.6

22.4

22.1

22.3

25.4

7.3

8.2

5.1

6.6

12.3

6.9

7.3

4.4

30.0

25.0

20.0

15.0

10.0 5.0

0.0

Readmission rate (%)

Hip

repl

acem

ent

COPD

Out

puts

(man

agem

ent)

MEASURING HEALTH SYSTEM EFFICIENCY IN LOW- AND MIDDLE-INCOME COUNTRIES: A RESOURCE GUIDE 47Cesa

rean

sect

ion

(C-s

ectio

n) ra

tes

Sim

ple

defin

ition

: The

per

cent

of p

regn

ant w

omen

who

hav

e a

C-se

ctio

n in

a s

peci

fic g

eogr

aphi

cal a

rea

and

refe

renc

e pe

riod.

Freq

uenc

y of

dat

a co

llect

ion:

Adm

inis

trat

ive

data

is u

sual

ly a

naly

zed

mon

thly

, qua

rter

ly, a

nd/o

r ann

ually

. Sur

vey

data

is u

sual

ly e

very

5 y

ears

.

Sugg

este

d co

mpa

rato

r: A

cros

s co

untr

ies;

acr

oss

faci

litie

s/pr

ovid

ers;

pas

t per

form

ance

/tre

nd d

ata;

by

urba

n/ru

ral;

by p

ublic

/priv

ate

faci

lity.

Pote

ntia

l sou

rce

of d

ata:

Num

erat

or: c

linic

al re

gist

ries

for d

ata

in a

giv

en g

eogr

aphi

cal a

rea

on th

e nu

mbe

r of C

-sec

tions

per

form

ed;

estim

ates

of t

he n

umbe

r of b

irths

in

that

are

a; p

opul

atio

n- b

ased

sur

veys

fo

r sel

f-re

port

ed C

-sec

tions

onl

y.

Den

omin

ator

: all

live

birt

hs d

urin

g th

e re

fere

nce

perio

d. W

here

dat

a on

the

num

ber o

f liv

e bi

rths

are

ab

sent

, eva

luat

ors

can

calc

ulat

e to

tal

estim

ated

live

birt

hs u

sing

cen

sus

data

fo

r the

tota

l pop

ulat

ion

and

crud

e bi

rth

rate

s in

a s

peci

fied

area

.

Benc

hmar

king

gui

delin

esU

NIC

EF/W

HO

/UN

FPA

reco

mm

end

a C-

sect

ion

rate

bet

wee

n 5

and

15 p

erce

nt o

f all

birt

hs, b

ased

on

estim

ates

from

a v

arie

ty o

f sou

rces

. Ra

tes

less

than

5%

can

indi

cate

in

adeq

uate

cov

erag

e or

acc

ess

to

emer

genc

y ob

stet

ric c

are.

Rat

es o

ver

15%

can

indi

cate

exc

essi

ve u

se o

f c-

sect

ions

resu

lting

in u

nnec

essa

ryex

posu

re to

sur

gery

and

ane

sthe

sia

risks

for w

omen

and

a w

aste

of

reso

urce

s. It

is im

port

ant t

o no

te th

at

the

refe

renc

e be

nchm

ark

is a

t the

po

pula

tion

leve

l whi

ch u

ses

all l

ive

birt

hs d

urin

g th

e re

fere

nce

perio

d as

th

e de

nom

inat

or.

As

trea

tmen

t pro

toco

ls m

ay v

ary

acro

ss c

ount

ries,

com

paris

on w

ithin

cou

ntry

is a

lso

reco

mm

ende

d. F

or w

ithin

cou

ntry

com

paris

on, a

n al

tern

ativ

e in

dica

tor i

s th

e pr

opor

tion

of fa

cilit

y de

liver

ies

that

are

C-s

ectio

ns.

Her

e, th

e so

urce

of d

ata

can

be fr

om fa

cilit

y or

cla

ims

data

. The

den

omin

ator

is li

ve b

irths

at t

he fa

cilit

y no

t all

live

birt

hs w

ithin

a c

atch

men

t are

a. T

his

indi

cato

r may

be

sign

ifica

ntly

hig

her t

han

c-se

ctio

n ra

tes

at th

e po

pula

tion

leve

l bec

ause

it d

oes

not a

djus

t for

hig

her r

isk

preg

nanc

ies

and

refe

rral

s. W

hile

spe

cify

ing

an a

ppro

pria

te ra

nge

of ta

rget

per

cent

ages

with

in a

faci

lity

is im

prac

tical

, it m

ight

stil

l be

inte

rest

ing

to

benc

hmar

k ag

ains

t ave

rage

faci

litie

s/pr

ovid

ers

of s

imila

r typ

e to

look

for o

utlie

rs. T

he R

obso

n Sc

ale

has

bee

n ap

plie

d in

som

e se

ttin

gs to

hel

p m

onito

r the

rate

s of

c-s

ectio

n am

ongs

t sub

grou

ps o

f pre

gnan

t wom

en

(e.g

. hig

h ris

k ve

rsus

low

risk

) how

ever

this

will

like

ly re

quire

acc

ess

to p

atie

nt h

isto

ry a

nd m

edic

al re

cord

s.

(Lin

k to

Rob

son

Scal

e:ht

tps:

//ap

ps.w

ho.in

t/iri

s/bi

tstr

eam

/han

dle/

1066

5/25

9512

/978

9241

5131

97-e

ng.p

df;js

essi

onid

=A04

8ECA

988C

32D

049A

D25

B2E2

D07

E68A

?seq

uenc

e=1)

Caes

area

n se

ctio

n ra

tes,

201

5 (o

r nea

rest

yea

r)

Turkey

Portugal

United Kingdom

Estonia

Korea

Slovak Rep

ublic

Spain

Norway

Italy

OECD33

France

Finland

Mexico

United States

Canada

Sweden

Hungary

Ireland

Denmark

Iceland

Australia

Luxembourg

Belgium

Chile

Germany

Czech

Republic

Israel

Poland

Austria

Latvia

Netherl

ands

Switzerl

and

New Zea

land

Slovenia

53.1

46.8

46.0

38.0

37.2

36.2

35.3

34.0

32.9

32.3

32.2

30.2

30.2

30.1

28.7

27.927.8

26.0

26.3

25.4

24.5

21.1

21.0

20.8

20.8

20.8

18.7

17.3

16.216.1

16.0

15.9

15.5

26.2

60 50 40 30 20 10 0

Per 1

00 li

ve b

irths

Out

puts

(man

agem

ent)


Yello

w fe

ver

(5 d

ose)

vac

cine

9900

7119

4835

4364

3848

2975

Vic

ryl H

S-37

mm

1

Hal

oper

idol

tab

1mg

Stri

p (b

lood

ch

oles

tero

l)

Mag

nesi

um s

ulfa

te

past

e 0%

(100

mg)

Chol

era

vacc

ine

(2 d

ose

vial

)

Pote

ntia

l Sou

rce

Faci

lity

base

d su

rvey

s, a

dmin

istr

ativ

e da

ta, S

ervi

ce P

rovi

sion

Ass

essm

ents

. If

ava

ilabl

e, L

ogis

tics

Man

agem

ent

and

Info

rmat

ion

Syst

em (L

MIS

) or

inve

ntor

y m

anag

emen

t sy

stem

Benc

hmar

king

gui

delin

esId

eally

zer

o. S

ugge

st b

ench

mar

king

ag

ains

t pa

st p

erfo

rman

ce o

r av

erag

e fa

cilit

y

Loss

due

to

expi

red

drug

s in

Hos

pita

l A, T

op 6

item

s in

USD

, 201

7

Sour

ce: I

nven

tory

man

agem

ent s

yste

m, C

ount

ry X

. 201

7

mig

ht in

clud

e th

e or

der t

urna

roun

d tim

e (i.

e. t

he n

umbe

r of d

ays

betw

een

whe

n th

e or

der w

as re

ceiv

ed a

nd d

eliv

ered

), th

e fr

eque

ncy

of o

rder

s an

d th

e pr

escr

iptio

n ra

te o

r util

izat

ion

of c

omm

only

exp

ired

prod

ucts

.

Expi

red

drug

s P

ropo

rtio

n of

pro

duct

s pa

st t

heir

use-

by d

ate

in s

tock

ove

r th

e to

tal a

mou

nt o

f pro

duct

in s

tock

. Can

als

o be

mea

sure

d in

mon

etar

y va

lue

if th

e qu

anti

ty o

f di

scar

ded

expi

red

drug

is r

ecor

ded

alon

g w

ith

the

mar

ket

pric

e of

the

dru

g.

Freq

uenc

y of

dat

a co

llect

ion:

Adm

inis

trat

ive

data

cou

ld b

e co

llect

ed m

onth

ly, y

earl

y. F

acili

ty s

urve

ys a

re u

sual

ly a

d ho

c w

hen

fund

ing

is a

vaila

ble.

Inve

ntor

y or

logi

stic

s m

anag

emen

t sy

stem

s ca

n pr

oduc

e da

ily/r

eal-

tim

e re

port

s.

Sugg

este

d co

mpa

rato

r: A

cros

s fa

cilit

ies,

pha

rmac

ies,

med

ical

sto

rage

cen

ters

.

Pote

ntia

l sou

rce

of d

ata:

Faci

lity

base

d su

rvey

s, a

dmin

istr

ativ

eda

ta, S

ervi

ce P

rovi

sion

Ass

essm

ents

.If

avai

labl

e, L

ogis

tics

Man

agem

ent

and

Info

rmat

ion

Syst

em (L

MIS

) or

inve

ntor

y m

anag

emen

t sys

tem

.

Be

nchm

arki

ng g

uide

lines

Idea

lly z

ero.

Sug

gest

ben

chm

arki

ngag

ains

t pa

st p

erfo

rman

ce o

rav

erag

e fa

cilit

y.

Out

puts

(man

agem

ent)


180

days

120

days

10-6

9 da

ys

6 da

ys60-8

3 da

ys 40 d

ays80

day

s55

day

s

051015202530354045

Faci

lity

1 Faci

lity

2Faci

lity

3

Faci

lity

4Faci

lity

5 Faci

lity

6Faci

lity

7 Faci

lity

8Faci

lity

9 Faci

lity

10Faci

lity

11 Faci

lity

12Faci

lity

13 Faci

lity

14Faci

lity

15 Faci

lity

16

6-55

day

s 4-40

day

s

Pote

ntia

l sou

rce

of d

ata:

Faci

lity

base

d su

rvey

s, a

dmin

istr

ativ

e da

ta, S

ervi

ce P

rovi

sion

Ass

essm

ents

. If

ava

ilabl

e, L

ogis

tics

Man

agem

ent

and

Info

rmat

ion

Syst

em (L

MIS

) or

inve

ntor

y m

anag

emen

t sy

stem

.

Benc

hmar

king

gui

delin

esId

eally

zer

o. S

ugge

st b

ench

mar

king

ag

ains

t pa

st p

erfo

rman

ce o

r av

erag

e fa

cilit

y.

Stoc

k-ou

t ra

te (%

) and

num

ber o

f day

s it

ems

wer

e no

t av

aila

ble,

ac

ross

faci

litie

s, 2

017

Sour

ce: I

nven

tory

man

agem

ent s

yste

m, C

ount

ry X

. 201

7

Just

like

exp

ired

prod

ucts

, dru

g st

ocko

uts

coul

d al

so b

e a

refle

ctio

n of

wea

knes

ses

thro

ugho

ut t

he s

uppl

y ch

ain.

Lac

k of

fund

s an

d/or

com

plic

ated

and

leng

thy

proc

edur

es t

o ac

cess

fund

s an

d or

der d

rugs

cou

ld b

e an

issu

e. H

ere

too,

in

dica

tors

suc

h as

the

pro

port

ion

of e

xpire

d or

ruin

ed d

rugs

, the

ord

er t

urna

roun

d tim

e, t

he fr

eque

ncy

of o

rder

s, a

nd t

he p

resc

riptio

n ra

te o

r util

izat

ion

of s

tock

ed-o

ut it

ems

mig

ht h

elp

faci

litie

s/ph

arm

acie

s/ce

ntra

l med

ical

sto

res

bett

er

fore

cast

and

man

age

drug

inve

ntor

ies.

Dru

g st

ocko

uts

Sim

ple

defin

itio

n: N

umbe

r or

per

cent

of

serv

ice

deliv

ery

unit

s th

at e

xper

ienc

e a

stoc

kout

of a

par

ticu

lar

prod

uct

that

sho

uld

be in

sto

ck o

ver

a de

fined

per

iod

of t

ime.

It c

ould

als

o be

defi

ned

as n

umbe

r, or

pe

rcen

t of

pro

duct

s th

at a

re u

nava

ilabl

e at

a p

arti

cula

r se

rvic

e de

liver

y un

it.

Freq

uenc

y of

dat

a co

llect

ion:

Adm

inis

trat

ive

data

cou

ld b

e co

llect

ed m

onth

ly, y

earl

y. F

acili

ty s

urve

ys a

re u

sual

ly a

d ho

c w

hen

fund

ing

is a

vaila

ble.

Inve

ntor

y or

logi

stic

s m

anag

emen

t sy

stem

s ca

n pr

oduc

e da

ily/r

eal-

tim

e re

port

s.

Sugg

este

d co

mpa

rato

r: A

cros

s fa

cilit

ies,

pha

rmac

ies,

med

ical

sto

rage

cen

ters

.

Out

puts

(man

agem

ent)

MEASURING HEALTH SYSTEM EFFICIENCY IN LOW- AND MIDDLE-INCOME COUNTRIES: A RESOURCE GUIDE50 Pote

ntia

l sou

rce

of d

ata:

Clai

ms

and

prem

ium

col

lect

ion

data

.

Benc

hmar

king

gui

delin

esTh

e “e

xpec

ted”

cla

ims

ratio

sho

uld

be 1

00%

min

us a

mar

gin

for

adm

inis

trat

ive

expe

nses

and

a

reas

onab

le le

vel o

f cla

im re

serv

es.

For a

priv

ate

insu

ranc

e co

mpa

ny,

a m

argi

n fo

r pro

fit w

ould

als

o be

su

btra

cted

. Fo

r mat

ure

natio

nal h

ealt

h in

sura

nce

syst

ems,

one

wou

ld e

xpec

t th

e ra

tio

to b

e ab

out

90%

.

Clai

ms

rati

o (%

) by

mem

bers

hip

grou

p, 2

014-

2017

Sour

ce: N

atio

nal h

ealth

insu

ranc

e ag

ency

cla

ims

and

prem

ium

dat

abas

e (2

019)

If t

he c

laim

s ra

tio is

regu

larly

ove

r 100

% o

ver a

n ex

tend

ed n

umbe

r of y

ears

it m

eans

eith

er t

he p

rem

ium

s ar

e no

t be

ing

prop

erly

cal

cula

ted

usin

g so

und

actu

aria

l prin

cipa

ls a

nd/o

r the

re a

re is

sues

on

the

clai

ms

paym

ent

and

adm

inis

trat

ive

expe

nse

side

. If c

ompa

ring

acro

ss in

sura

nce

sche

mes

, it

is im

port

ant

to a

ccou

nt fo

r diff

eren

ces

in p

rem

ium

s, m

embe

rshi

p ty

pes,

cov

erag

e ra

tes,

and

cos

t co

ntai

nmen

t fe

atur

es.

Percentage (%)

Poor

and

nea

r po

orPr

ivat

e fo

rmal

Info

rmal

sec

tor

Reti

red/

pens

ione

rs

2014

2015

2016

2017

Clai

ms

ratio

Sim

ple

defin

itio

n: A

rat

io c

alcu

late

d as

acc

rued

cla

ims

divi

ded

by a

ccru

ed p

rem

ium

s. T

his

is s

omet

imes

als

o re

ferr

ed t

o as

the

loss

rat

io.

Freq

uenc

y of

dat

a co

llect

ion:

Mon

thly

, qua

rter

ly, a

nnua

lly.

Sugg

este

d co

mpa

rato

r: B

y m

embe

rshi

p ty

pe, a

gain

st p

ast

perf

orm

ance

. If

mor

e th

an o

ne s

chem

e by

insu

ranc

e sc

hem

e.

600

500

400

300

200

100 0

Out

puts

(man

agem

ent)


0%20%

40%

60%

80%

100%

120%

Gen

eral

Pub

licSe

rvic

esDe

fenc

ePu

blic

Ord

eran

d Sa

fety

Econ

omic

Affai

rsEn

viro

nmen

tal

Prot

ec�o

nHo

usin

g an

dCo

mm

unity

Amen

i�es

Heal

thTo

urism

and

Crea�v

eEc

onom

y

Relig

ion

Educ

a�on

Socia

lPr

otec�o

n

Adju

sted

bud

get

Orig

inal

bud

get

Pote

ntia

l sou

rce

of d

ata:

No

know

n gl

obal

com

para

tor

data

base

. Fin

anci

al M

anag

emen

t In

form

atio

n Sy

stem

s (F

MIS

) or

Publ

ic E

xpen

ditu

re a

nd F

inan

cial

A

ccou

ntab

ility

Fra

mew

orks

(PEF

A)

give

exe

cutio

n ra

tes

for g

over

nmen

t as

a w

hole

and

by

sect

or. P

ublic

Ex

pend

iture

Rev

iew

s (P

ERs)

may

als

o ha

ve th

is in

form

atio

n al

thou

gh th

e m

etho

dolo

gy a

nd fr

eque

ncy

are

less

co

nsis

tent

.Th

e W

orld

Ban

k’s

BOO

ST in

itiat

ive

(dep

loye

d in

~40

coun

trie

s gl

obal

ly)

prov

ides

bud

get a

nd e

xpen

ditu

re

data

at a

mor

e gr

anul

ar le

vel b

y go

vern

men

t lev

el (c

entr

al o

r loc

al),

adm

inis

trat

ive

unit

(min

istr

y,

depa

rtm

ent,

agen

cy, u

nive

rsity

, ho

spita

l or s

choo

l), e

cono

mic

serv

ices

, cap

ital e

xpen

ses)

, fun

ctio

nal

sour

ce (b

udge

t, do

mes

tic o

r for

eign

bo

rrow

ing)

.ht

tps:

//pef

a.or

g/;

http

://gl

obal

prac

tice

s.w

orld

bank

.org

/go

vern

ance

/Pag

es/S

iteP

ages

/PER

%20

aspx

;ht

tp://

sear

ch.w

orld

bank

.org

/per

Man

y of

the

reas

ons

for p

oor B

ERs

are

gene

rally

out

side

the

cont

rol o

f the

MO

H (e

.g. w

eak

capa

city

to fo

reca

st re

venu

es, d

elay

s in

the

rele

ase

of fu

nds,

in-y

ear b

udge

t adj

ustm

ents

, or r

igid

ities

on

how

fund

s ca

n be

spe

nt a

cros

s pr

ogra

ms)

. For

asse

ssm

ent o

f exe

cutio

n ra

tes

is a

lso

extr

emel

y he

lpfu

l. Fo

r ins

tanc

e, M

inis

trie

s th

at a

re v

ery

hum

an re

sour

ce in

tens

e (li

ke e

duca

tion

and

heal

th) a

re li

kely

to h

ave

a be

tter

exe

cutio

n ra

te th

an o

ther

s be

caus

e w

ages

are

qua

si s

tatu

tory

. For

this

re

ason

, it m

ight

mak

e m

ore

sens

e to

com

pare

the

BER

of w

ages

and

sal

arie

s, n

on-w

age

recu

rren

t exp

endi

ture

s, a

nd in

vest

men

t exp

endi

ture

s in

the

sect

or s

epar

atel

y.

A=a

ggre

gate

exp

endi

ture

out

turn

w

as b

etw

een

95%

and

105

% o

f th

e ap

prov

ed a

ggre

gate

bud

gete

d ex

pend

iture

in a

t le

ast

two

of t

he

last

thr

ee y

ears

.B=

aggr

egat

e ex

pend

iture

out

turn

w

as b

etw

een

90%

and

110

% o

f th

e ap

prov

ed a

ggre

gate

bud

gete

d ex

pend

iture

in a

t le

ast

two

of t

he

last

thr

ee y

ears

.C=

aggr

egat

e ex

pend

iture

out

turn

w

as b

etw

een

85%

and

115

% o

f th

e ap

prov

ed a

ggre

gate

bud

gete

d ex

pend

iture

in a

t le

ast

two

of t

he

last

thr

ee y

ears

.D

=per

form

ance

is le

ss t

han

requ

ired

for a

C s

core

.

Sour

ce: P

EFA

Var

ious

Yea

rsBudg

et E

xecu

tion

Rat

es, b

y se

ctor

, Ind

ones

ia, 2

016,

%Be

nchm

arki

ng g

uide

lines

Whi

le t

here

are

no

esta

blis

hed

benc

hmar

ks t

o as

sess

hea

lth

sect

or

budg

et e

xecu

tion

rate

s, t

here

ar

e PE

FA s

corin

g gu

idel

ines

for

aggr

egat

e ex

pend

iture

out

turn

whi

ch

coul

d be

app

lied

to a

sses

s th

e he

alth

se

ctor

bud

get

relia

bilit

y.

Budg

et e

xecu

tion

rate

s (B

ERs)

by

agg

rega

te e

xpen

ditu

re o

uttu

rn).

Freq

uenc

y of

dat

a co

llect

ion:

revi

ews

are

less

freq

uent

.

Sugg

este

d co

mpa

rato

r: A

cros

s co

untr

ies;

Oth

er s

ecto

rs/l

ine

min

istr

ies;

by

expe

ndit

ure

cate

gory

(e.g

. cap

ital

vs

recu

rren

t); t

rend

dat

a.

Out

puts

(man

agem

ent)

MEASURING HEALTH SYSTEM EFFICIENCY IN LOW- AND MIDDLE-INCOME COUNTRIES: A RESOURCE GUIDE52 Life

exp

ecta

ncy

at b

irth

Pote

ntia

l sou

rce

of d

ata:

Vita

l reg

istr

y da

ta. H

ouse

hold

su

rvey

s su

ch a

s th

e D

emog

raph

ic

and

Hea

lth

Surv

ey (D

HS)

, th

e U

NIC

EF M

ultip

le In

dica

tor

Clus

ter S

urve

y (M

ICS)

.ht

tps:

//dhs

prog

ram

.com

http

s://m

ics.

unic

ef.o

rg

Benc

hmar

king

gui

delin

esN

o es

tabl

ishe

d be

nchm

ark

the

high

er t

he b

ette

r. Su

gges

t al

so b

ench

mar

king

aga

inst

pa

st p

erfo

rman

ce o

r rel

evan

t re

gion

al/in

com

e co

untr

y co

mpa

rato

rs/a

vera

ges.

Life

exp

ecta

ncy

(in y

ears

), 20

17

Sour

ce: W

orld

Ban

k (2

019)

. Wor

ld D

evel

opm

ent I

ndic

ator

s. (h

ttps

://da

taba

nk.w

orld

bank

.org

/sou

rce/

wor

ld-d

evel

opm

ent-

indi

cato

rs)

Ave

rage

num

ber

of y

ears

tha

t a

new

born

is e

xpec

ted

to li

ve if

cur

rent

mor

talit

y ra

tes

cont

inue

to

appl

y.

Freq

uenc

y of

dat

a co

llect

ion:

Vit

al r

egis

try

data

sho

uld

be a

nnua

l. Su

rvey

dat

a us

ually

eve

ry 5

yea

rs.

Sugg

este

d co

mpa

rato

r:

The

rela

tive

str

aigh

tfor

war

d an

d st

anda

rdiz

ed m

easu

rabi

lity

of t

hese

indi

cato

rs a

llow

s fo

r co

mpa

rabi

lity

acro

ss c

ount

ries.

It

may

als

o be

of

inte

rest

to

polic

y m

aker

s to

com

pare

by

soci

oeco

nom

ic s

tatu

s (e

.g. w

ealt

h qu

inti

le, l

evel

of e

duca

tion

, gen

der)

.

As

a hi

gh le

vel o

utco

me

indi

cato

r, it

will

be

impo

rtan

t to

choo

se re

leva

nt h

ealth

sys

tem

inpu

t, o

utpu

t, a

nd p

roce

ss m

easu

res

in d

evel

opin

g a

resu

lts c

hain

nar

rativ

e.

Out

com

es (h

ealt

h st

atus

)

77

75

72

7070

68

66

63

Mal

dive

s

Sri L

anka

Bang

lade

sh

Nep

al

Bhut

an

Indi

a

Paki

stan

Afgh

anis

tan

Life

exp

ecta

ncy

in S

outh

Asia

, 201

7


ntia

l sou

rce

of d

ata:

Vita

l reg

istr

y da

ta. H

ouse

hold

su

rvey

s su

ch a

s t

he D

emog

raph

ic

and

Hea

lth

Surv

ey (D

HS)

, th

e U

NIC

EF M

ultip

le In

dica

tor C

lust

er

Surv

ey (M

ICS)

.ht

tps:

//dhs

prog

ram

.com

http

s://m

ics.

unic

ef.o

rg

Benc

hmar

king

gui

delin

esN

o es

tabl

ishe

d be

nchm

ark,

the

low

er

the

bett

er. S

ugge

st b

ench

mar

king

ag

ains

t pa

st p

erfo

rman

ce o

r re

leva

nt re

gion

al/in

com

e co

untr

y co

mpa

rato

rs/a

vera

ges.

Sus

tain

able

D

evel

opm

ent

Goa

l tar

gets

aim

to

redu

ce u

nder

5 m

orta

lity

to le

ss t

han

25 p

er 1

000

live

birt

hs b

y 20

30.

Und

er 5

-mor

talit

y ra

te (p

er 1

,000

), 20

17

010

2030

4050

6070

8090

100

110

120

130

Sub-

Saha

ran

Afr

ica

Mau

ritiu

s

Mal

dive

s

Cuba

Japa

n

Isra

el

Slov

enia

Cana

da Uni

ted

Stat

es

Turk

men

ista

n

Djib

outi

Lao

PDR

Hai

ti

Paki

stan

Som

alia

East

Asi

a &

Pac

i�c

Nor

th A

mer

ica

Sout

h A

sia

Lati

n A

mer

ica

&

Cari

bbea

n

Mid

dle

East

&

Nor

th A

fric

a

Euro

pe &

Ce

ntra

l Asi

a

Und

er-fi

ve m

orta

lity

Sim

ple

defin

itio

n: U

nder

-five

mor

talit

y (U

5M) r

ate

is t

he p

roba

bilit

y th

at a

chi

ld w

ill d

ie b

efor

e re

achi

ng a

ge fi

ve.

Freq

uenc

y of

dat

a co

llect

ion:

Vit

al r

egis

try

data

sho

uld

be a

nnua

l. Su

rvey

dat

a us

ually

eve

ry 5

yea

rs.

Sugg

este

d co

mpa

rato

r: T

he r

elat

ive

stra

ight

forw

ard

and

stan

dard

ized

mea

sura

bilit

y of

the

se in

dica

tors

allo

ws

for

com

para

bilit

y ac

ross

cou

ntrie

s. It

may

als

o be

of

inte

rest

to

polic

y m

aker

s to

com

pare

by

regi

on (e

.g. p

rovi

nce,

urb

an/r

ural

), or

soc

ioec

onom

ic s

tatu

s (e

.g. w

ealt

h qu

inti

le, l

evel

of e

duca

tion

, gen

der)

.

Not

e: B

est a

nd w

orst

cou

ntrie

s by

regi

onSo

urce

: Wor

ld B

ank

(201

9). W

orld

Dev

elop

men

t Ind

icat

ors

http

s://d

atab

ank.

wor

ldba

nk.o

rg/s

ourc

e/w

orld

-dev

elop

men

t-in

dica

tors

Neo

nata

l (le

ss th

an 2

8 da

ys) a

nd in

fant

(les

s th

an 1

yea

r) m

orta

lity

rate

s ar

e im

port

ant p

arts

of t

he o

vera

ll un

der-

5 m

orta

lity

rate

. Dis

aggr

egat

ed a

naly

ses

can

help

iden

tify

vuln

erab

le p

opul

atio

ns fo

r bet

ter t

arge

ting.

As

mos

t U5M

dea

ths

are

prev

enta

ble,

it w

ill b

e im

port

ant t

o ch

oose

rele

vant

inpu

t, o

utpu

t and

pro

cess

indi

cato

rs in

dev

elop

ing

resu

lts c

hain

nar

rativ

e. F

or e

xam

ple,

num

ber o

f nur

ses,

pos

tnat

al c

over

age,

fully

-im

mun

ized

chi

ldre

n, re

leva

nt c

hild

-hea

lth s

peci

fic re

adin

ess

mea

sure

s (S

ALA

(htt

ps://

ww

w.w

ho.in

t/he

alth

info

/sys

tem

s/sa

ra_i

ntro

duct

ion/

en/)

or S

DI (

http

s://w

ww

.sdi

ndic

ator

s.or

g), a

nd o

ther

s su

ch a

s di

agno

stic

acc

urac

y of

com

mon

chi

ldho

od il

lnes

ses

like

diar

rhea

or p

neum

onia

, and

adh

eren

ce to

clin

ical

gu

idel

ines

.

Out

com

es (h

ealt

h st

atus

)

MEASURING HEALTH SYSTEM EFFICIENCY IN LOW- AND MIDDLE-INCOME COUNTRIES: A RESOURCE GUIDE54 Pote

ntia

l sou

rce

of d

ata:

Vita

l reg

istr

y da

ta, h

ealt

h se

rvic

es

reco

rds,

cen

sus.

Hou

seho

ld s

urve

ys

such

as

the

Dem

ogra

phic

and

Hea

lth

Surv

ey (D

HS)

, the

UN

ICEF

Mul

tiple

In

dica

tor C

lust

er S

urve

y (M

ICS)

.ht

tps:

//dhs

prog

ram

.com

http

s://m

ics.

unic

ef.o

rg

Benc

hmar

king

gui

delin

esN

o es

tabl

ishe

d be

nchm

ark,

th

e lo

wer

the

bet

ter.

Sugg

est

benc

hmar

king

aga

inst

pas

t pe

rfor

man

ce o

r rel

evan

t re

gion

al/

inco

me

coun

try

com

para

tors

/av

erag

es. S

usta

inab

le D

evel

opm

ent

Goa

l tar

gets

aim

to

redu

ce m

ater

nal

mor

talit

y to

less

tha

n 70

per

10

0,00

0 liv

e bi

rths

by

2030

.

Mat

erna

l mor

talit

y pe

r 100

,000

live

birt

hs (1

990-

2016

)

Sour

ce: W

orld

Ban

k (2

019)

. Wor

ld D

evel

opm

ent I

ndic

ator

s. (h

ttps

://da

taba

nk.w

orld

bank

.org

/sou

rce/

wor

ld-d

evel

opm

ent-

indi

cato

rs)

As

mos

t mat

erna

l dea

ths

are

prev

enta

ble,

it w

ill b

e im

port

ant t

o ch

oose

rele

vant

inpu

t, o

utpu

t and

pro

cess

indi

cato

rs in

dev

elop

ing

the

resu

lts c

hain

nar

rativ

e. F

or e

xam

ple,

num

ber o

f mid

wiv

es, a

nten

atal

car

e co

vera

ge, a

cces

s to

ski

lled

care

dur

ing

http

s://w

ww

.who

.int/

heal

thin

fo/s

yste

ms/

sara

_int

rodu

ctio

n/en

/) or

SD

I (ht

tps:

//ww

w.s

dind

icat

ors.

org)

, and

oth

ers

such

as

diag

nost

ic a

ccur

acy,

adh

eren

ce to

clin

ical

gu

idel

ines

. Mat

erna

l dea

th a

udits

are

als

o an

impo

rtan

t add

ition

al s

ourc

e of

info

rmat

ion

to e

xpla

in c

ause

s of

mat

erna

l mor

talit

y.

Rate per 100,000 live births

The

annu

al n

umbe

r of

mat

erna

l dea

ths

from

any

cau

se r

elat

ed t

o or

agg

rava

ted

by p

regn

ancy

or

its

man

agem

ent,

rel

ativ

e to

the

tot

al n

umbe

r of

live

birt

hs. T

he in

dica

tor

is r

epor

ted

as d

eath

s pe

r 10

0,00

0 liv

e bi

rths

.

Freq

uenc

y of

dat

a co

llect

ion:

Vit

al r

egis

try

data

sho

uld

be a

nnua

l. Su

rvey

dat

a oc

curs

usu

ally

eve

ry 5

yea

rs.

Sugg

este

d co

mpa

rato

r: T

he s

tand

ardi

zed

mea

sura

bilit

y of

the

se in

dica

tors

allo

ws

for c

ompa

rabi

lity

acro

ss c

ount

ries.

It m

ay a

lso

be o

f int

eres

t to

pol

icy

mak

ers

to c

ompa

re b

y re

gion

(e.g

. pro

vinc

e, u

rban

/rur

al),

or s

ocio

econ

omic

sta

tus

(e.g

. wea

lth

quin

tile

, lev

el o

f edu

cati

on, g

ende

r).

Mat

erna

l mor

talit

y ra

teO

utco

mes

(hea

lth

stat

us)


Sim

ple

defin

itio

n: T

he b

urde

n of

TB

dise

ase

can

be m

easu

red

in t

erm

s of

: i) I

ncid

ence

– t

he n

umbe

r of

new

and

rel

apse

cas

es o

f TB

aris

ing

in a

giv

en t

ime

perio

d, u

sual

ly 1

yea

r; ii

) Pre

vale

nce

– th

e nu

mbe

r of

ca

ses

of T

B at

a g

iven

poi

nt in

tim

e; a

nd ii

i) M

orta

lity

– th

e nu

mbe

r of

dea

ths

caus

ed b

y TB

in a

giv

en t

ime

perio

d, u

sual

ly 1

yea

r.

Freq

uenc

y of

dat

a co

llect

ion:

TB

noti

ficat

ions

/sur

veill

ance

dat

a ar

e id

eally

cap

ture

d in

rea

l-ti

me

and

rev

iew

ed w

eekl

y or

mon

thly

. Pre

vale

nce

surv

eys

are

at le

ast

5 ye

ars

apar

t.

Sugg

este

d co

mpa

rato

r: A

cros

s co

untr

ies,

by

regi

on, h

igh

risk

popu

lati

ons

(e.g

. peo

ple

livin

g w

ith

HIV

, inj

ecti

ng d

rug

user

s, p

rison

s, n

ursi

ng h

omes

, hea

lthc

are

wor

kers

).

Indi

a

Phili

ppin

esPa

kist

an

Indo

nesi

a

Chin

aSo

uth

Afric

aN

iger

ia

DR C

ongo

Bang

lade

sh

UR T

anza

Mya

nmar

Moz

ambi

que

Keny

a

Ethi

opia

Viet

Na

Thai

land

DPR

Kor

Ango

la

Russ

ian

Fede

rati

onBr

azil

TB b

urde

n

Pote

ntia

l sou

rce

of d

ata:

The

WH

O p

rodu

ces

a ye

arly

Glo

bal

TB re

port

. The

re a

re a

lso

i) TB

pr

eval

ence

sur

veys

– a

met

hod

used

if

notifi

catio

n da

ta fr

om ro

utin

e su

rvei

llanc

e ar

e t

houg

ht t

o be

in

accu

rate

or i

ncom

plet

e an

d w

hen

ther

e is

an

estim

ated

rela

tivel

y hi

gh p

reva

lenc

e of

TB

(mor

e th

an

100

per 1

00,0

00 p

opul

atio

n) a

s th

ey a

re e

xpen

sive

to

cond

uct;

ii)

TB n

otifi

catio

ns –

a m

etho

d us

ed

by 1

44 c

ount

ries

with

low

leve

ls

of u

nder

repo

rtin

g (g

ener

ally

hig

h in

com

e an

d so

me

sele

cted

mid

dle-

inco

me

coun

trie

s); a

nd ii

i) N

atio

nal

inve

ntor

y st

udie

s –

thes

e m

easu

re

the

leve

l of u

nder

repo

rtin

g of

de

tect

ed T

B ca

ses

(met

hod

used

in 6

co

untr

ies)

; and

iv) C

ase

notifi

catio

n da

ta c

ombi

ned

with

exp

ert

opin

ion

abou

t ca

se-d

etec

tion

gaps

– t

his

met

hod

is t

he le

ast

pref

erre

d an

d it

is re

lied

upon

onl

y if

one

of t

he

othe

r thr

ee m

etho

ds c

anno

t be

use

d (c

urre

ntly

use

d in

43

coun

trie

s).

Benc

hmar

king

gui

delin

esSD

G 3

incl

udes

a t

arge

t to

end

th

e gl

obal

TB

epid

emic

by

2030

, w

ith T

B in

cide

nce

(new

and

rela

pse

case

s pe

r 100

,000

pop

ulat

ion

per

year

) defi

ned

as t

he in

dica

tor f

or

mea

sure

men

t of

pro

gres

s. T

he 2

030

targ

ets

set

in t

he E

nd T

B St

rate

gy a

re

a 90

% re

duct

ion

in T

B de

aths

and

an

80%

redu

ctio

n in

TB

inci

denc

e,

com

pare

d w

ith le

vels

in 2

015.

TB b

urde

n: 2

0 hi

ghes

t co

untr

ies,

Inci

denc

e nu

mbe

r in

thou

sand

s, 2

017

Sour

ce: W

HO

(201

8). G

loba

l Tub

ercu

losi

s Re

port

201

8. h

ttps

://w

ww

.who

.int/

tb/p

ublic

atio

ns/g

loba

l_re

port

/en

The

ultim

ate

goal

is t

o di

rect

ly m

easu

re T

B bu

rden

from

TB

notifi

catio

ns in

all

coun

trie

s. N

otifi

catio

ns a

re c

onsi

dere

d a

good

pro

xy o

f inc

iden

ce in

cou

ntrie

s th

at h

ave

high

-per

form

ance

sur

veill

ance

sys

tem

s an

d go

od a

cces

s to

qua

lity

care

th

at re

sult

in fe

w u

ndia

gnos

ed c

ases

. As

the

surv

eilla

nce

syst

em im

prov

es a

nd t

he n

umbe

r of c

ases

dec

reas

es, c

ount

ries

no lo

nger

und

erta

ke e

xpen

sive

TB

surv

eys

that

wou

ld re

quire

larg

er a

nd la

rger

sam

ple

size

s. T

o as

sess

pro

gres

s m

ade

due

to a

pro

gram

or i

nter

vent

ion

it m

ay b

e m

ore

info

rmat

ive

to a

lso

look

at

indi

cato

rs a

long

the

ent

ire T

B ca

scad

e in

clud

ing

othe

r TB

indi

cato

rs s

uch

as T

B ca

se d

etec

tion,

TB

notifi

catio

n, T

B tr

eatm

ent

succ

ess

rate

s, T

B-se

rvic

e sp

ecifi

c re

adin

ess

mea

sure

s (S

ARA

htt

ps://

ww

w.w

ho.in

t/he

alth

info

/sys

tem

s/sa

ra_r

epor

ts/e

n/ a

nd S

DI h

ttps

://w

ww

.sdi

ndic

ator

s.or

g/in

dica

tors

) and

dia

gnos

tic a

ccur

acy

and

adhe

renc

e to

clin

ical

gui

delin

es.

51-1

00

101-

150

151-

200

201-

500

501-

1000

>100

0

Out

com

es (h

ealt

h st

atus

)

Per

cent

age

of h

ouse

hold

s w

ho s

pend

mor

e th

an 1

0% o

f the

ir to

tal h

ouse

hold

con

sum

ptio

n on

hea

lth

expe

ndit

ures

.

Freq

uenc

y of

dat

a co

llect

ion:

Usu

ally

eve

ry 5

yea

rs h

owev

er s

ome

regi

ons/

coun

trie

s co

llect

dat

a on

an

annu

al b

asis

. The

Hea

lth

Equi

ty a

nd F

inan

cial

Pro

tect

ion

Indi

cato

rs d

atab

ase

will

be

upda

ted

annu

ally

.

Sugg

este

d co

mpa

rato

r: A

cros

s co

untr

ies.

Som

e su

rvey

s al

so p

rodu

ce r

epre

sent

ativ

e da

ta a

t th

e su

bnat

iona

l lev

el a

llow

ing

wit

hin

coun

try

com

paris

ons.

It m

ay a

lso

be o

f in

tere

st t

o po

licy

mak

ers

to c

ompa

re b

y so

cioe

cono

mic

sta

tus

(e.g

. wea

lth

quin

tile

or

inco

me

grou

ps).

Cata

stro

phic

hea

lth e

xpen

ditu

re

Pote

ntia

l sou

rce

of d

ata:

Hou

seho

ld s

urve

ys. M

ore

rece

ntly

the

W

orld

Ban

k ha

s st

arte

d to

pro

duce

re

gula

r est

imat

es t

hrou

gh it

s H

ealt

h Eq

uity

and

Fin

anci

al P

rote

ctio

n da

taba

se (H

EFPI

).ht

tp://

data

topi

cs.w

orld

bank

.org

/hea

lth-

Benc

hmar

king

gui

delin

esN

o es

tabl

ishe

d be

nchm

ark,

the

low

er

the

bett

er. S

ugge

st b

ench

mar

king

ag

ains

t pa

st p

erfo

rman

ce o

r re

leva

nt re

gion

al/in

com

e co

untr

y co

mpa

rato

rs/a

vera

ges.

Inci

denc

e of

cat

astr

ophi

c he

alth

exp

endi

ture

, % o

f pop

ulat

ion

(Lat

est

year

, 10

Perc

ent

thre

shol

d)

prot

ectio

n it

mus

t be

inte

rpre

ted

with

cau

tion

as it

can

not

dis

tran

spor

tatio

n, t

ime

cost

s, in

form

al p

aym

ents

, etc

.

Not

e: G

rey

= no

dat

aSo

urce

: WB

(201

9) H

EFPI

dat

abas

e

0-3

3-6

6-10

10-1

515

-45

The World Bank’s support to the Joint Learning Network for UHC is made possible with financial contributions from the following partners:

www.jointlearningnetwork.org

measuring health system efficiency in low- and middle

Documents