Introduction

Cardiovascular, respiratory, and related chronic disorders are an increasing concern in low- and middle-income countries (LMICs). In 2010, 19 percent (408.7 million) of total disability-adjusted life years (DALYs) and 39 percent (17.0 million) of total deaths in LMICs were attributable to cardiovascular and circulatory diseases, chronic respiratory diseases, diabetes mellitus, and chronic kidney diseases combined. The burden in LMICs accounts for 85 percent and 80 percent of global cardiovascular, respiratory, and related chronic disorder DALYs and deaths, respectively (IHME 2013).

Several treatment options are available for each disease, ranging from generic pharmacologic treatments, such as aspirin for vascular disease, metformin for diabetes, and salbutamol for chronic respiratory disease, to invasive procedures, such as coronary artery bypass graft surgery for vascular disease or kidney transplant for chronic kidney disease. These invasive procedures are often costly and resource intensive, placing a large burden on a country’s health care system.

Governments face tough allocation choices for limited public resources across many competing priorities, as each country strives to achieve universal coverage of essential health care services under the Sustainable Development Goals. The large and growing burden of cardiovascular, respiratory, and related chronic disorders forces public payers to allocate, or at least consider allocating, increasing resources to these diseases and conditions. This chapter explores the difficulty of rationing health resources in LMICs. Governments and public payers may allocate resources using priority-setting policy tools such as essential medicines lists (EMLs), health benefit plans, and health technology assessment (HTA) agencies. Yet, the processes used to arrive at allocation decisions are rarely evidence based, transparent, or participatory.

Furthermore, although the focus of this chapter is on high-cost treatment, the need for a legitimate and evidence-driven priority-setting process applies to all health conditions and diseases, and preventive measures cannot be ignored; the priority-setting process is not complete without considering local evidence on the costs and benefits of both prevention and treatment.

The chapter is divided into three sections. The first section frames the topic of priority setting in health. The second section explores a case study that shows how national essential medicines lists (NEMLs) largely fail to influence prescription shares of types of insulin for which marginal cost-effectiveness has not been fully established in several LMICs. The third section examines a second case study that shows the complexity of the priority-setting process in Thailand’s decision to include dialysis in the national health insurance (NHI) plan’s benefits package.

Framing the Issues

A fundamental challenge for all health systems is allocating finite resources across the potentially unlimited demand for health services and technologies. This is a rationing problem, regardless of whether it is explicitly addressed as such, because it requires that choices be made regarding how and when services are provided, to whom, and by what mechanism across many dimensions (Ham and Robert 2003). Inevitably some demand goes unmet, which is one source of the intense pressure to provide more services and newer and more sophisticated technologies within any given resource envelope. Efforts to reduce waste, increase quality, and improve efficiency are all responses to this pressure. Expanding health care costs and spending are indications of the same forces.

Conflicts in priority-setting decisions reflect natural features of all societies, including differences in demographics and disease burden as well as cultural preferences and beliefs. In addition, there are no universal answers to the inevitable policy questions, such as the balance of support between preventive and therapeutic measures, or choices between disease control priorities. Insufficient institutional mechanisms for assessing various proffered priorities, evaluating political and economic constraints, and gathering input from citizens and stakeholders make this problem particularly acute for policy makers in LMICs.

The sheer size of the need for treatments for cardiovascular, respiratory, and related chronic disorders in LMICs forces public resources to be allocated to these conditions and ensures that these diseases will be an important concern for policy makers. Although noncommunicable diseases have traditionally been perceived as a high-income health burden, LMICs are increasingly experiencing these problems. Total DALYs and deaths in LMICs attributable to cardiovascular, respiratory, and related chronic disorders increased substantially from 16 percent of total DALYs (377.8 million) and 36 percent of total deaths (15.14 million) in 2000 to 19 percent of total DALYs and 39 percent of total deaths in 2010 (IHME 2013). Additionally, complications that arise from diabetes affect societies more broadly (van Dieren and others 2010). As cardiovascular, respiratory, and related chronic disorder needs grow, the demand for treatment increases. Consequently, the challenge of rationing becomes greater, and prevention efforts become more critical.

Although technical progress can be cost saving and reduce the relative price of health products and services, new technologies can also be costlier—although, ideally, more effective (Martins and Maisonneuve 2006). Determining the extent of coverage for an intervention requires analysis of the costs and benefits for health. Most LMICs do not incorporate cost-effectiveness evidence, even when available, in spending decisions. Without an explicit rationing mechanism, many LMICs allocate resources to expensive, novel technologies that benefit a small number of people, while not implementing low-cost, highly effective interventions that would benefit a large number of people and provide greater population health gains (Hutubessy and others 2003). In addition, politics can play a role in the process. Industry leaders, health professional associations, and patients themselves are increasingly pressuring health systems to include novel treatments.

For LMICs, affordability is an important perspective. Although many health technologies may be cost-effective when assessed against a gross domestic product (GDP) per capita threshold (Culyer and others 2007; Johannesson and Weinstein 1993; Weinstein and Statson 1977), they may be unaffordable under a given budget constraint, forcing countries to say “no” to putatively cost-effective technologies—or resort to inequitable, implicit rationing methods. Treatments for chronic diseases can be affordable at one stage of a disease but not at another. For example, treatment at an early stage may be cost-effective to the health system, but it may become unaffordable once the disease has progressed. To that end, considering cost-effectiveness of preventive measures, such as screening, is particularly important.

In addition to cost-effectiveness, other values—including fairness, equity, human rights, respect and self-determination, and financial protection—similarly need to be factored into a decision-making framework in an evidence-based way. Although a full discussion is omitted from this chapter, Brock and Wikler (2006) address ethical issues in resource allocation and cost-effectiveness, and the World Health Organization (WHO) Consultative Group on Equity and Universal Health Coverage provides a three-part strategy that countries can use as a guideline for fair, progressive realization of universal health coverage (WHO 2014). Rights-based legal arguments, which have been used in some middle-income countries in Latin America and the Caribbean, have propelled the provision of expensive therapies without directly addressing how much should be spent, how the resources should be used, or what trade-offs might affect equity and health (Kinney and Clark 2004). However, it is important to recognize that many coverage decisions are made with no technical or social goals in mind, no underpinning analysis, and no due process of any kind; this reality is reflected in the case study that follows.

Case Study 1: Type 2 Diabetes

This case study examines how NEMLs as a priority-setting mechanism often fail to influence prescription shares of insulin analogs. This case study first discusses the burden of disease, treatment, and guidelines. Second, it discusses NEMLs as a priority-setting mechanism and analyzes prescription data to gauge the effectiveness of NEMLs as a priority-setting tool. It concludes with insights derived from the case study.

Priority-Setting Mechanism: National Essential Medicines Lists

The EML is among the earliest efforts to provide a basis for explicit priority setting in LMICs. Since 1977, the WHO has published a model list with the intent of informing purchasing decisions by national health officials (van den Ham, Bero, and Laing 2011). The medicines on the list are selected based on public health relevance, evidence on efficacy and safety, and—to some extent—comparative effectiveness so that they satisfy the priority health care needs of the population (van den Ham, Bero, and Laing 2011). The model list—updated every two years based on applications from individuals, governments, pharmaceutical companies, and medical associations—is published online. Countries often create their own versions of EMLs, with infrequent updating. As of 2011, 156 countries had adopted versions of the EML (Glassman and Chalkidou 2012).

In many countries, the adoption of an EML does not lead to the availability of all—or indeed most—of the medicines listed. Surveys undertaken in 36 countries showed that the mean availability of the 15 most frequently surveyed medicines was 38.4 percent in public sector facilities and 64.2 percent in private sector facilities (Cameron and others 2009). The disconnect between the lists, availability, and actual use is likely to be related, at least in part, to the absence of attention and support for an affordability analysis in a specific country’s public spending envelope. The WHO’s model list includes some hospital and specialist medicines, but many countries seek international advice on how to handle new, higher-cost medications, which—although cost-effective—may be beyond the resources of the health system (PAHO 2010).

The medicines for type 2 diabetes on the 18th WHO EML (updated March 2013) are the following: metformin, NPH insulin, zinc suspension insulin, neutral insulin, glibenclamide, and gliclazide (WHO 2013). Table 21.1 compares the agents on the list with those on the NEMLs of 13 selected countries: Argentina, Brazil, Colombia, the Arab Republic of Egypt, Indonesia, Mexico, Morocco, Pakistan, Peru, the Philippines, South Africa, and República Bolivariana de Venezuela, plus Turkey. The countries were selected based on the availability of IMS MIDAS² medical data. Although IMS MIDAS medical data are available for Turkey, the country does not have an NEML.³

Table 21.1

Antidiabetic Treatments on the WHO and National Essential Medicines Lists.

A comparison of antidiabetic medicines on the WHO model list and on NEMLs shows that in most sampled countries, NEMLs conform closely to WHO recommendations. For human insulins, few countries include other medicines on their NEMLs. Indonesia, the Philippines, South Africa, Thailand, and República Bolivariana de Venezuela include premixed (biphasic) insulin on their NEMLs. Only Argentina and Colombia include any insulin analogs. Argentina’s NEML includes insulin aspart (a fast-acting insulin analog); Colombia’s NEML includes three fast-acting insulin analogs (insulin aspart, insulin glulisine, and insulin lispro) and two long-acting insulin analogs (insulin glargine and insulin detemir). With the exceptions of Argentina and Colombia, the NEMLs conform closely to the WHO’s recommendations.

Priority Setting in Action

This section analyzes prescription data to gauge the effectiveness of NEMLs as a priority-setting tool. It finds a high use of products that are expensive or that are not proven to be cost-effective in many countries. Figure 21.1 shows the proportion of each type of treatment out of total insulin retail prescriptions, which includes human insulins and insulin analogs (Anatomical Therapeutic Chemical Classification System 4 code A10C) for June 2013 from the IMS MIDAS medical database.

Figure 21.1

Percentage of Total Human Insulin Retail Prescriptions by Treatment by Country, 2013.

Prescription data show high use of insulin analogs in many countries, despite NEML guidance. In several countries, non-analog human insulins make up the vast majority of retail prescriptions, as in Morocco (94.5 percent), Pakistan (90.5 percent), Egypt (79.9 percent), and Peru (75.7 percent).

However, in other countries, insulin analogs make up the majority of the retail prescription market share, even though only Argentina and Colombia include insulin analogs on their NEMLs. Long-acting insulin analogs—insulin glargine and insulin detemir—have the largest share in República Bolivariana de Venezuela (76.2 percent), Brazil (59.3 percent), Mexico (51.7 percent), Colombia (48.5 percent), the Philippines (44.6 percent), and Indonesia (42.8 percent). Fast-acting insulin analogs—insulin glulisine, insulin aspart, and insulin lispro—have the largest share in Turkey (52.3 percent), South Africa (48.7 percent), and Argentina (43.6 percent).

This analysis has several limitations. First, the retail prescription market does not capture the full market and thus does not show the whole picture. However, the results are indicative of extensive use of insulin analogs in a number of countries. Second, not all type 2 diabetes patients undergo insulin therapy, so the analysis captures only part of the patient population. Examining other classes of antidiabetics would be an interesting direction for further research. Third, the data capture only the moving average target of June 2013.⁴ Extending the period may provide a different composition of prescriptions and reveal broader trends in adoption and prescription of insulin analogs. Nevertheless, the current analysis provides a snapshot of the insulin market in LMICs that was not previously available in the literature and provides a starting point for follow-on work.

The case study of insulin analogs for the treatment of type 2 diabetes shows that NEMLs do not restrict the prescribing of medicines. In some countries, an NHI formulary—which is the responsibility of health insurers—can supersede an NEML. For example, in Ghana, both an NEML and an NHI formulary exist, but the two do not contain the same drugs. Countries could benefit from synchronizing the two mechanisms to ensure a more coordinated system for priority setting.

NEMLs and NHI formularies should be synchronized for available agents as well as for new products. The insulin analog case study is one example showing that countries would benefit from reviewing both available and novel interventions. A joint report by the International Insulin Foundation and the Health Policy Analysis Centre, with the support of the International Diabetes Federation, finds that 57 percent of the Kyrgyz Republic’s insulin expenditure goes to insulin analogs. Based on their analysis, switching from an insulin analog to a human insulin could release enough resources to treat twice as many people (Abdraimova and Beran 2009).

Review of available technologies may lead to disinvestment, a process that has traditionally received little attention. Disinvestment involves withdrawing resources, either partially or entirely, from interventions—practices, procedures, pharmaceuticals, or medical devices—that are not cost-effective and do not lead to efficient resource allocation (Elshaug and others 2007). Interest in disinvestment is growing because of budget constraints in countries across all levels of development.

One of NICE’s tools for disinvestment is its “do not do” recommendations, a database of clinical practices that NICE’s independent advisory board compiles during the process of guidance development, because of evidence that the practice is not beneficial or lack of evidence to support its continued use (NICE 2012). The database includes several recommendations for type 2 diabetes.

Table

Despite efforts to promote disinvestment, it is difficult to know the extent to which “do not do” lists are implemented given that there is no mandate to adopt the recommendations. A challenge for the United Kingdom’s National Health Service is the lack of data on usage beyond the primary care level as well as indication-specific precision (Garner and Littlejohns 2011). Drug utilization studies are critical. In addition, independent information interventions directed at clinicians and patients can reinforce messages of what to do and what not to do. Without these data and interventions, the health system cannot fully determine variations in care and the potential savings from disinvestment.

Case Study Insights

Despite policy makers’ attempts to use the NEML as a mechanism to promote cost-effective treatments, insulin analogs make up the majority of retail prescriptions and are purchased in significant quantities by public payers in some countries, with Colombia as a clear example (see also box 21.1). A number of lessons can be drawn from the case study.

Box 21.1

Analog Insulin Pricing and Sales in Colombia.

Affirming the Role of Cost-Effectiveness Analyses in the Priority-Setting Process

The WHO model list is composed of treatments based on public health relevance, efficacy, safety, and comparative cost-effectiveness, yet the type 2 diabetes treatments on the NEMLs of many countries differ from the model list. In Morocco, the NEML does not include non-analog human insulins, which have been proven cost-effective; the NEMLs of Argentina and Colombia include insulin analogs, which have not been proven to be cost-effective for broad use, at least at the prices currently obtained by different purchasers.

Several reasons could contribute to the discrepancy, such as lack of awareness of the WHO model list or failure to update the NEML to reflect the best available evidence. However, another contributing factor could be that the model list does not reflect country-level cost-effectiveness analyses, and thus cannot be reconciled with the country’s public spending envelope. When governments seek to set priorities for the use of limited health resources, including updating an NEML, a global or regional reference is crucial but is only a starting point.

Comparing Priority-Setting Mechanisms and Processes in Similar Countries

Based on the analysis of the 13 NEMLs in this case study, NEMLs differ from each other as well as from the WHO model list. In addition, a comparison of human insulin prescriptions in retail markets shows vastly different compositions across countries, at least of those captured in the IMS MIDAS database. Non-analog insulins make up more than 90 percent of human insulin prescriptions in Morocco and fewer than 10 percent of human insulin prescriptions in Turkey, despite similar epidemiological profiles with respect to population characteristics and diabetes prevalence.

Each country can learn from the priority-setting mechanisms and processes of other countries with similar characteristics, such as region, development status, burden of disease, or health system. In addition to the WHO model list, other countries’ NEMLs and processes could serve as good benchmarks when selecting treatments to include on an NEML or in a health benefits package. Data on actual use are helpful to understanding how prescribing levels of various treatments differ between similar countries. NEMLs are easy to obtain online, but obtaining data on actual use is more difficult. It may be costly to obtain use data from a third party; comparing data captured internally by governments requires a large amount of coordination among countries. These data need to be more readily available to enable countries to compare their own priority-setting mechanisms and processes with those in similar countries.

Staying Up to Date on the Market Authorization Process

The case study shows that the composition of human insulin treatment prescriptions is vastly different across countries. Since discrepancies occur between NEMLs and actual prescribing, the prescribing differences between countries are not simply the result of differences in medications listed but can be driven by the entry of new products as part of each country’s market authorization process. Once a new product comes to market, the pressure to publicly subsidize it increases.

It is important to be aware of market authorization processes, not only for a single treatment but for the treatment class as a whole. Understanding the market authorization process for classes of drugs in neighboring or similar countries can be useful for managing the pressures on and anticipating the changes to prescribing patterns.

Communicating Priority-Setting Processes and Decisions

In many countries, the actual priority-setting process—for example, exactly how or why a drug is included or excluded from an NEML—is not clear to the public.

Public awareness of the decision-making process and dissemination of the supporting evidence compels a payer or listing entity or the drug regulation entity to remain accountable for its decisions. Accountability mechanisms, such as the appointment of an independent, multidisciplinary committee or the establishment of an appeals process, are discussed in the next section (WHO 2011). These mechanisms reduce the influence of marketing pressures on priority-setting decisions; even if such pressures do have an impact, the mechanisms allow regulators to subsequently manage and minimize the risk of poor prescribing decisions.

The processes for selecting which drugs are on each country’s NEML are not clear, especially when they deviate from the WHO model list. For example, Morocco’s NEML does not include any non-analog human insulins. Colombia’s NEML includes three fast-acting and two long-acting insulin analogs, but Argentina’s NEML only includes one insulin analog. Several countries’ NEMLs include acarbose, an alpha-glucosidase inhibitor, and glipizide, a sulfonylurea, neither of which are on the WHO model list.

Most countries lack explicit decision-making mechanisms of any kind, but progress has been made. Policy makers in LMICs are increasingly adopting policy instruments that explicitly define, limit, control, or guarantee the particular health technologies, interventions, and benefits that are to be funded and sometimes provided by the government. One approach to explicit priority setting has been to establish HTA entities to assess new and current medical technologies.

Case Study 2: Dialysis in Thailand

This case study explores Thailand’s decision to include dialysis in the benefits package of an NHI plan. First, it discusses the burden of disease, treatment, and health coverage. Second, it discusses HTA agencies as priority-setting mechanisms. Then it examines Thailand’s decision to include dialysis using a peritoneal dialysis (PD)–first policy in the Universal Coverage Scheme’s (UCS) (box 21.2) benefits package. It concludes with insights from the case study.

Box 21.2

Health Coverage in Thailand.

Case Study Insights

Thailand is a leader in the universal health coverage movement as evidenced by its early success in reforming the country’s health financing system to provide nearly every citizen with health insurance. This health system is supported by a sophisticated HTA agency that sets priorities through an explicit and evidence-driven process. This case study explores the process of including dialysis in the UCS benefits package and some of the considerations involved (a parallel example on South Africa is provided in box 21.3). A few lessons can be drawn from this example.

Box 21.3

Dialysis in South Africa.

Acknowledging the Importance of Equity- and Ethics-Related Commitments

Cost-effectiveness and value for money are often key concerns when considering particular interventions to be included or excluded from a benefits package. The decision to include PD in the UCS benefits package was deliberate, based on results from economic evaluations but also considering equity- and ethics-related factors.

The equity-based argument for inclusion of dialysis compared the relative coverage between the UCS and the other two schemes, given that all are supported by public funds. In addition, the UCS aims to reduce catastrophic expenditures on health for the Thai poor and near-poor. For those who needed dialysis, having to pay large out-of-pocket sums meant that the UCS did not deliver on its promise of financial protection. This case study shows that HTA goes beyond the numbers-based evidence provided by economic analysis by including evidence that involves equity, ethical, social, and legal implications.

Including Input from All Key Stakeholders

The case study shows that the priority-setting process affects many different parties—including policy makers in the Ministry of Public Health, academics, providers in hospitals and health clinics, community health workers, professional associations, and patients. In Thailand, the HTA through HITAP in theory provides an avenue for all stakeholders to play a role in policy change, and this was true for decisions around dialysis.

Through accountability mechanisms such as the appointment of an independent, multidisciplinary committee or establishing an appeals process, the public can take ownership of policy decisions. A transparent priority-setting process via information sharing can limit conflicts between interests (Tantivess and others 2013). For example, physicians recommended coverage of hemodialysis rather than PD based on favorable medical evidence. However, through information sharing—especially of the cost-effectiveness data—and an inclusive process, providers were convinced to accept the decision for PD as first-line therapy.

Incorporating Disease Prevention Measures in the Priority-Setting Process

The burden of noncommunicable disease—specifically cardiovascular, respiratory, and related chronic disorders—will continue to increase in the absence of serious efforts to control risk factors. And because interventions for noncommunicable disease can be expensive and perhaps required for a lifetime, publicly financing these treatments can place a serious burden on a country’s economy.

An important component of the UCS is health promotion, and the Thai government continues to invest in such programs. For the early detection and prevention of diabetes and hypertension, the Ministry of Public Health in 2011 launched a US$76 million program of screening measures in 5,500 communities. Still, some in the NHSO consider the health promotion funds to be insufficient and hope to increase efforts to promote healthier lifestyles and prevent noncommunicable diseases overall, not only kidney disease (Treerutkuarkul 2010).

Interventions that target disease prevention cannot be left out of the priority-setting process. When policy makers invest in an expensive curative intervention, it is also important to consider the opportunity cost of investing in preventive interventions. In some cases, treatment alone can be cost-effective but not when coupled with screening.

Using Priority-Setting to Strengthen Overall Health System Capacity

Including an intervention in the benefits package is not just about gathering the evidence and making a decision. For the government to be able to deliver on promises, other parts of the health system must adjust to accommodate new policies. In Thailand, the NHSO encouraged the establishment of clinics that could provide PD in public facilities, particularly first-level hospitals, and partnered with private facilities when it realized that the capacity of public facilities was insufficient (Tantivess and others 2013).

Since 2008, the infrastructure and human resources to accommodate the inclusion of dialysis in the UCS benefits package have been developed. The number of PD clinics increased from 23 to 160 between 2008 and 2012, and the number of nurses trained to care for dialysis patients increased from 56 to 423 during that same period (Tantivess and others 2013). Finding the resources to build the capacity for the provision of dialysis has enabled many to benefit from dialysis coverage under the UCS.

It is not yet clear whether the inclusion of dialysis has had a population health impact, such as improved life expectancy, on the UCS population. As in the previous case study, the Thai dialysis example shows that review of current interventions included in a benefits package, not just new technologies, is crucial in the priority-setting process.

Conclusions

This chapter brings to light the challenges facing evidence-based resource allocation for health, especially to meet the increasing demand for the treatment of cardiovascular, respiratory, and related chronic disorders. Policy makers in LMICs must weigh prevention, affordability, and ethical considerations in addition to cost-effectiveness when deciding on whom and for what the government will spend. Interventions, both preventive and curative, can be cost-effective, depending on the context, such as disease progression. The priority-setting process can be greatly influenced by political considerations.

The first case study examines prescribing data for human insulin for type 2 diabetes, and reports that, despite being available, an NEML may not have an impact on what treatments physicians actually prescribe and patients actually use. The second case study examines Thailand’s decision to include dialysis in the UCS benefits package. Thailand has a sophisticated, explicit priority-setting mechanism—HITAP, its HTA agency—yet, the NHSO still has to make difficult coverage decisions.

Both examples show that countries can benefit from reviewing available interventions in addition to new ones. Crucially, the examples show the importance of institutional capacity in carrying out the process of explicit priority setting to guide technology adoption decisions. Explicit priority setting uses a transparent, deliberative process led by an independent, multidisciplinary committee that considers evidence—as well as other factors, such as inclusiveness—to drive decisions.

A better priority-setting system can provide a fair and transparent mechanism for managing the politics of resource allocation, connect evidence-based decisions to budgets, and create permanent institutional channels for considering resource allocation choices over time.

Acknowledgment

The authors thank Kalipso Chalkidou and Yot Teerawattananon for their contributions and acknowledge the contributions of Sergio Marquez, senior advisor of the Ministry of Health, on medicine pricing policies.

Notes

World Bank Income Classifications as of July 2014 are as follows, based on estimates of gross national income (GNI) per capita for 2013:

• Low-income countries (LICs) = US$1,045 or less
• Middle-income countries (MICs) are subdivided:

  1. lower-middle-income = US$1,046–US$4,125
  2. upper-middle-income (UMICs) = US$4,126–US$12,745
• High-income countries (HICs) = US$12,746 or more.

1

People with type 1 diabetes have a total lack of insulin due to immune system response, while people with type 2 diabetes do not have enough insulin or are insulin resistant.

2

IMS MIDAS medical data show exactly what is being prescribed for a disease or therapy area and is standardized internationally.

3

Many countries, including Turkey, are moving to health insurance formularies that sometimes coexist with and sometimes supersede EMLs, which become defunct and disappear.

4

Average of May, June, and July 2013.

5

Hemodialysis uses an artificial kidney outside the body to filter blood; peritoneal dialysis uses the lining of the abdominal cavity to filter blood.

References

1. Abdraimova A, Beran D. 2009. “Rapid Assessment Protocol for Insulin Access in Kyrgyzstan.” Policy Research Document 66, Health Policy Analysis Center, Bishkek, Kyrgyz Republic.
2. Beran D, Yudkin J S. 2010. “Looking beyond the Issue of Access to Insulin: What Is Needed for Proper Diabetes Care in Resource Poor Settings.” Diabetes Research and Clinical Practice 88 (3): 217–21. [PubMed: 20447710]
3. British Medical Association and the Royal Pharmaceutical Society of Great Britain. 2011. British National Formulary 61. London: BMJ Publishing Group and Pharmaceutical Press.
4. Brock D W, Wikler D. 2006. “Ethical Issues in Resource Allocation, Research, and New Product Development.” In Disease Control Priorities in Developing Countries, second edition, edited by Jamison D T, Breman J G, Measham A R, Alleyne G, Claeson M, Evans D B, Jha P, Mills A, Musgrove P, editors. 259–70. Washington, DC: World Bank. [PubMed: 21250319]
5. Cameron A, Ewen M, Ross-Degnan D, Ball D, Laing R. 2009. “Medicine Prices, Availability, and Affordability in 36 Developing and Middle-Income Countries: A Secondary Analysis.” The Lancet 373 (9659): 240–49. [PubMed: 19042012]
6. Cohen D, Carter P. 2010. “How Small Changes Led to Big Profits for Insulin Manufacturers.” BMJ (341): c7139. [PubMed: 21159773]
7. Culyer A J, McCabe C, Briggs A, Claxton K, Buxton M., and others. 2007. “Searching for a Threshold, Not Setting One: The Role of the National Institute for Health and Clinical Excellence.” Journal of Health Services Research and Policy 12 (1): 56–58. [PubMed: 17244400]
8. Elshaug A G, Hiller J E, Tunis S R, Moss J R. 2007. “Challenges in Australian Policy Processes for Disinvestment from Existing, Ineffective Health Care Practices.” Australia and New Zealand Health Policy 4 (23): 1–8. [PMC free article: PMC2174492] [PubMed: 17973993]
9. European Medicines Agency. 2014. “Human Medicines: Tresiba (insulin degludec).” European Medicines Agency, London. http://www​.ema.europa​.eu/ema/index.jsp?curl=pages​/medicines/human​/medicines/002498/human_med_001609​.jsp&mid​=WC0b01ac058001d124.
10. Fink S. 2010. “Life and Death Choices as South Africans Ration Dialysis Care.” ProPublica, New York.
11. Garner S, Littlejohns P. 2011. “Do NICE’s Recommendations for Disinvestment Add Up?” BMJ 343 349–51.
12. Glassman A, Chalkidou K. 2012. “Priority-Setting in Health: Building Institutions for Smarter Public Spending.” Report of the Priority-Setting Institutions for Global Health Working Group, Center for Global Development, Washington, DC.
13. Ham C, Robert G. 2003. Reasonable Rationing: International Experience of Priority Setting in Health Care. Philadelphia, PA: Open University Press.
14. Hutubessy R, Baltussen R, Tan-Torres Edejer T, Evans D. 2003. “Generalized Cost-Effectiveness Analysis: An Aid to Decision Making in Health.” In Making Choices in Health: WHO Guide to Cost-Effectiveness Analysis, edited by Tan-Torres T, Edejer R, Baltussen T, Adam R, Hutubessy A Acharya, editors. , and others,277–88. Geneva: World Health Organization.
15. IHME (Institute for Health Metrics and Evaluation). 2013. “Global Burden of Diseases, Injuries, and Risk Factors Study 2010.” University of Washington, Institute for Health Metrics and Evaluation, Seattle, WA. http://www​.healthdata.org/gbd.
16. IMS Health. 2013. IMS MIDAS Medical Data.
17. Japan Pharmaceuticals and Medical Devices Agency. 2013. “New Drugs Approved in FY 2012.” Japan Pharmaceuticals and Medical Devices Agency, Tokyo. http://www​.pmda.go.jp​/english/service/pdf​/list/NewdrugsFY2012.pdf.
18. Johannesson M, Weinstein M C. 1993. “On the Decision Rules of Cost-Effectiveness Analysis.” Journal of Health Economics 12 (4): 459–67. [PubMed: 10131756]
19. Karagiannis T, Paschos P, Paletas K, Matthews D R, Tsapas A. 2012. “Dipeptidyl Peptidase-4 Inhibitors for Treatment of Type 2 Diabetes Mellitus in the Clinical Setting: Systematic Review and Meta-Analysis.” BMJ 344: e1369. [PubMed: 22411919]
20. Kinney E, Clark B. 2004. “Provisions for Health and Health Care in the Constitutions of the Countries of the World.” Cornell International Law Journal 37 (2): 285–355.
21. Martins J, Maisonneuve C. 2006. “The Drivers of Public Expenditure on Health and Long-Term Care: An Integrated Approach.” Economic Studies 43, Organisation for Economic Co-operation and Development, Paris.
22. MIMS (Monthly Index of Medical Specialties). 2014. MIMS Drug Information. https://www​.mims.com/.
23. Ministerio de Salud y Protección Social Colombia. 2014. “Sistema de Informacion de Medicamentos.” Bogota.
24. Moosa M R, Kidd M. 2006. “The Dangers of Rationing Dialysis Treatment: The Dilemma Facing a Developing Country.” Kidney International 70 (6): 1107–14. [PubMed: 16883316]
25. NICE (National Institute for Health and Care Excellence). 2009. “Type 2 Diabetes: Newer Agents for Blood Glucose Control in Type 2 Diabetes.” Short Clinical Guideline 87, NICE, London. http://www​.nice.org.uk​/nicemedia/pdf/cg87niceguideline.pdf. [PubMed: 22220323]
26. NICE (National Institute for Health and Care Excellence). 2012. “NICE ‘Do Not Do’ Recommendations.” NICE, London. http://www​.nice.org.uk​/usingguidance/donotdorecommendations​/index.jsp.
27. Novo Nordisk. 2013. “Novo Nordisk Receives Complete Response Letter in the US for Tresiba® and Ryzodeg®.” Bagsværd, Denmark. http://www​.novonordisk​.com/include/asp/exe_news_attachment​.asp?sAttachmentGUID​=83700060-0ce3-4577-a35a-f3e57801637d.
28. PAHO (Pan American Health Organization). 2010. “Access to High-Cost Medicines in the Americas: Situation, Challenges and Perspectives.” Technical Series 1—Essential Medicine, Access, and Innovation. PAHO, Washington, DC.
29. Pinelli N R, Cha R, Brown M B, Jaber L A. 2008. “Addition of Thiazolidinedione or Exenatide to Oral Agents in Type 2 Diabetes: A Meta-Analysis.” Annals of Pharmacotherapy 42 (11): 1541–51. [PubMed: 18957626]
30. Praditpornsilpa K, Lekhyananda S, Premasathian N, Kingwatanakul P, Lumpaopong A., and others. 2011. “Prevalence Trend of Renal Replacement Therapy in Thailand: Impact of Health Economics Policy.” Journal of the Medical Association of Thailand 94 (Suppl 4): S1–6. [PubMed: 22043559]
31. Qaseem A, Humphrey L L, Sweet D E, Starkey M, Shekelle P. 2012. “Oral Pharmacologic Treatment of Type 2 Diabetes Mellitus: A Clinical Practice Guideline from the American College of Physicians.” Annals of Internal Medicine 156 (3): 218–31. [PubMed: 22312141]
32. Renal Services Task Team. 2010. “Guideline: Priority Setting Approach in the Selection of Patients in the Public Sector with End-Stage Kidney Failure for Renal Replacement Treatment in the Western Cape Province.” Cape Town, South Africa.
33. Rojas L B A, Gomes M B. 2013. “Metformin: An Old but Still the Best Treatment for Type 2 Diabetes.” Diabetology and Metabolic Syndrome 5 (1): 6. [PMC free article: PMC3607889] [PubMed: 23415113]
34. Tantivess S, Werayingyong P, Chuengsaman P, Teerawattananon Y. 2013. “Universal Coverage of Renal Dialysis in Thailand: Promise, Progress, and Prospects.” BMJ 346: f462. [PubMed: 23369775]
35. Teerawattananon Y, Mugford M, Tangcharoensathien V. 2007. “Economic Evaluation of Palliative Management versus Peritoneal Dialysis and Hemodialysis for End-Stage Renal Disease: Evidence for Coverage Decisions in Thailand.” Value Health 10 (1): 61–72. [PubMed: 17261117]
36. Treerutkuarkul A. 2010. “Thailand: Health Care for All, at a Price.” Bulletin of the World Health Organization 88 (2): 84–85. [PMC free article: PMC2814485] [PubMed: 20428360]
37. Truven Health Analytics. 2014. Red Book Online. http://redbook​.solutions​.aap.org/redbook.aspx.
38. van den Ham R, Bero L, Laing R. 2011. “Selection of Essential Medicines.” In World Medicines Situation Report, third edition. Geneva: World Health Organization.
39. van Dieren S, Beulens J, van der Schouw Y, Grobbee D, Neal B. 2010. “The Global Burden of Diabetes and Its Complications: An Emerging Pandemic.” European Journal of Preventive Cardiology 17 (Suppl 1): S3–8. [PubMed: 20489418]
40. Weinstein M C, Statson W B. 1977. “Foundations of Cost-Effectiveness Analysis for Health and Medical Practices.” New England Journal of Medicine 296 (13): 716–21. [PubMed: 402576]
41. WHO (World Health Organization). 2011. Marketing Authorization of Pharmaceutical Products with Special Reference to Multisource (Generic) Products: A Manual for National Medicines Regulatory Authorities (NMRAs). Geneva: WHO.
42. WHO (World Health Organization). 2013. “WHO Model List of Essential Medicines, 18th List.” WHO, Geneva. http://apps​.who.int/iris​/bitstream/10665/93142/1/EML_18_eng​.pdf.
43. WHO (World Health Organization). 2014 Making Fair Choices on the Path to Universal Health Coverage: Final Report of the WHO Consultative Group on Equity and Universal Health Coverage. Geneva: WHO. [PMC free article: PMC4047814] [PubMed: 24940009]