Introduction

Diabetes mellitus is a chronic metabolic disease with deadly, disabling, and costly consequences for individuals, families, communities, and countries. Although they are phenotypically distinct, diabetes subtypes (type 1, type 2, gestational, and other forms) are all defined by elevated blood glucose levels. Approximately 95 percent of diabetes cases worldwide are type 2 diabetes (previously known as adult-onset or non-insulin-dependent diabetes), which is the focus of this chapter. Type 1 diabetes (previously known as insulin-dependent diabetes) most commonly begins in childhood and adolescence. Gestational diabetes refers to elevated blood glucose levels during pregnancy among women without previous diabetes and is associated with fetal, birthing, and early childhood complications as well as higher risk of the mother developing postgestation diabetes.

The growth of diabetes and its impacts have accelerated worldwide since the end of the twentieth century (NCD-RisC 2016), likely correlated with expansion of diabetes risk factors, especially population aging and obesity. Diabetes is a multifactorial condition. Because genetic, epigenetic, lifestyle, economic, and psychosocial factors all contribute to the development of diabetes (McCarthy 2010; Stumvoll, Goldstein, and van Haeften 2005), preventing and managing the condition require action at policy, program, clinical practice, and individual levels (Hill and others 2013).

Reliable and meaningful estimates of burdens, risk factors, and effectiveness and cost-effectiveness of interventions as well as evaluations of existing policies, are limited; data are especially scarce in low- and middle-income countries (LMICs). This chapter focuses on what can and should be done to address diabetes. We present the available data regarding global burdens and trends in diabetes; review available evidence and assess the effectiveness and cost-effectiveness of interventions to prevent, detect, and control diabetes; and report summary expert opinions regarding the priority and feasibility of implementing these interventions. Assimilating evidence from countries at different income levels, we provide global perspectives on the diabetes pandemic, recommend priority interventions, and identify remaining data gaps.

Global Burden

Mortality

Worldwide, life expectancy has been rising. Even mortality rates attributable to other leading noncommunicable diseases, such as CVD, stroke, some cancers, and chronic respiratory disease, have been declining (Ali and others 2015). However, both the age-standardized mortality rate and absolute number of deaths from diabetes have been rising.

Some regional variation is evident in deaths attributable to diabetes, as shown in table 12.1. North Africa and the Middle East, Latin America and the Caribbean, and the Western Pacific regions have the highest proportions of and increases in deaths attributable to diabetes (IDF 2015). South Asia has the highest absolute number, with almost 25 percent of all diabetes deaths globally.

Table 12.1

Percentage of All Deaths and DALYs Lost Attributable to Diabetes, Globally and by Region.

However, the magnitude of deaths attributable to diabetes is probably underestimated because death certifications and mortality approximations list only one cause of death. At least half of all deaths in people with diabetes are related to CVD (Geiss, Herman, and Smith 1995; IDF 2015; Moss, Klein, and Klein 1991), and CKD contributes significantly to deaths among people with diabetes (Levitt 2008; Zimmet 2009); however, these deaths are assigned to CVD or CKD, not diabetes. The World Health Organization (WHO) estimates that deaths attributable to diabetes amounted to 2.8 percent of all deaths globally in 2010, not including deaths attributable to CVD and CKD (32.0 percent and 1.5 percent, respectively). However, diabetes contributes to at least 21 percent of coronary heart disease and 13 percent of stroke mortality worldwide (Danaei and others 2006). Incorporating these aspects, the IDF estimates that diabetes is responsible for 5 million adult deaths annually, or 8.4 percent of all deaths globally (IDF 2015; Roglic and others 2005).

Considerations in Lmics

Worldwide, the patterns of morbidity and mortality associated with diabetes vary because of differences in populations (differences in maternal and childhood nutrition, exposure to infectious diseases, and level of awareness and health literacy), behavior (likelihood of seeking health care), health financing (health care coverage through public payer or insurance schemes), physical access to care, health facility resources and infrastructure (ability to support high-quality and effective lifestyle interventions and self-management), provider clinical practices, mechanisms for monitoring and delivering quality care, and policies to support diabetes prevention and management. In LMICs, it is especially hard to quantify these factors given the general lack of robust population-based longitudinal data. However, significant and lofty barriers in LMICs clearly have to be overcome to manage diabetes more effectively.

Most LMICs face two significant challenges: (1) lack of coverage of routine tests and inability of patients to pay for them, leading to a high proportion of undiagnosed cases; and (2) limited access to routine care and medications such as insulin that can be life-saving, as well as a general lack of human and infrastructural resources (refrigeration for insulin, for example), leading to poor health outcomes (Beran and Yudkin 2006; Beran, Yudkin, and de Courten 2005; Sobngwi and others 2012). The issue of affordable access to insulin is especially acute for type 1 diabetes because this lifelong medicine is essential for keeping these patients alive.

There are very few, if any, data from LMICs regarding coverage or access to health services and treatments. A WHO study in four LMIC regions showed generally low availability of insulin in both the public and private health sectors, and the cost of a one-month supply of insulin was equivalent to several days of a person’s wages (Mendis and others 2007). A 47-country survey conducted by IDF Europe (n.d.) is also illustrative of major disparities in availability, accessibility, and affordability of medications to control diabetes across country-income groups. The data reflect varying levels of annual spending per capita on diabetes (from less than US$100 in the Kyrgyz Republic to US$9,000 in Switzerland) and public financing of diabetes care (from 22 percent in Georgia to 85 percent in the Netherlands). Financial or geographic barriers to accessing medications and preventive services result in suboptimal care (Beran and Yudkin 2006; Beran, Yudkin, and de Courten 2005; Zhang and others 2012) and delayed presentation, giving rise to disabling and expensive-to-treat complications.

Effectiveness of Interventions

In the past half century, well-conducted studies from across the world have created a strong base of evidence about interventions that can help prevent diabetes among high-risk individuals (Gillies and others 2007; Knowler and others 2002; Tuomilehto and others 2001). In addition, robust data from trials and epidemiological studies—conducted largely in high-income settings—have shown that proactively and intensively managing diabetes lowers the risk of cardiovascular, kidney, eye, and limb diseases and of death (Holman and others 2008; Nathan and others 2005). Interventions to address diabetes, their efficacy, the level of evidence to support these interventions, and the few studies of this nature from LMICs are cataloged in annex 12A.

Fewer data are available from LMICs themselves to aid decision makers in these contexts. Ongoing studies are evaluating the implementation of diabetes prevention in South Africa (Pengpid, Peltzer, and Skaal 2014) and Brazil (Pimentel and others 2010); other studies are piloting novel approaches to preventing diabetes (Hegde and others 2013; McDermott 2012; McDermott and others 2014), but data are as yet unavailable or the sample sizes are too small to support generalizations. However, as annex 12A shows, the estimated effects in LMICs are comparable to those in HICs. The major considerations regarding the use of HIC data for LMIC settings are possible differences in the patterns of disease and comorbidities, as well as possible differences in accessibility, availability, affordability, and implementation capacity in lower-resource settings (Basu and others 2015).

Translating Evidence Into Action

Clinical and epidemiological data have been assimilated into expert preventive and clinical guidelines that are widely accessible (ADA 2016; IDF 2013; NICE 2013), but achieving real health gains requires more than just an accessible compendium of interventions. Complex conditions like diabetes require stakeholders and resources to coalesce at policy, program, clinic, and individual levels. However, because of barriers among policy makers (competing priorities), providers (low accountability, time constraints, lack of incentive schemes), and patients (low motivation), implementation of recommended preventive and care services for diabetes is falling far short of desired goals.

Economic Evaluations of Diabetes Interventions

To identify and compare the value of different diabetes-focused interventions—that is, the resource inputs required to achieve benefit—we reviewed and synthesized the available literature regarding cost-effectiveness of interventions to detect, prevent, and manage diabetes and its complications. The methodology is described in annex 12B; median and individual study incremental cost-effectiveness ratios (ICER) are provided in annex 12B (table 12B.1) and annex 12C (tables 12C.1–12C.4), respectively.

Valuing Interventions

Interpreting the value of investing in different health interventions in a specific context is challenging, especially when the data regarding cost-effectiveness were collected in different settings. The cost to implement interventions differs markedly across settings, as does the purchasing power of individuals and societies. In addition, the effectiveness of interventions may differ slightly in different settings, although there is no reason to believe that biological differences would be striking. Keeping these factors in mind, we compiled and calculated median ceiling ratios, that is, thresholds (of value) for decision makers to benchmark whether the median cost-effectiveness estimates reported fall into very cost-effective or cost saving, cost-effective, or not cost-effective ranges for their respective regions (annex 12D and annex table 12D.1) and even for country-income groups (low income, middle income, and high income) given the heterogeneity within regions. In addition, we used a regional cost index derived from health care input cost data compiled by the WHO to calibrate estimated cost-effectiveness for different LMIC regions (table 12.2). Although imperfect, this approach makes it possible to categorize interventions based on their value for LMIC decision makers; however, an important caveat is that these calibrations are linear and would be more accurate if fixed and variable costs, marginal gains, and diminishing returns were accounted for.

Table 12.2

Converted Incremental Cost-Effectiveness Ratios for Interventions to Detect, Prevent, and Manage Diabetes by LMIC Region, 2012 U.S. Dollars.

Based on benchmarking of ICERs of different diabetes interventions against ceiling thresholds in low-income countries of different regions worldwide (table 12.2 and annex 12D, table 12D.1), blood pressure control and diabetes self-management education offered the greatest value, and together with screening for dysglycemia and primary prevention interventions, were considered cost-effective or very cost-effective across all regions. Interventions offering the least value were intensive glycemic control, lipid control, and screening for diabetic retinopathy and CKD.

Priority Interventions to Address Diabetes

To collate stakeholder perspectives regarding appropriateness and priority ranking of interventions for diabetes prevention and management, we performed a systematic two-round Delphi process with a panel of leading experts representing diverse geographic regions characterized by varying levels of financial and human resource constraints. The methodology used is described in detail in annex 12E.

In round 1, respondents were asked to rank an adapted list of interventions identified by Narayan and others (2006) by priority and implementation feasibility in LMICs. They were also asked to rate four components of feasibility:

• Reach. Ability to reach the target population
• Technical complexity. Level of medical technologies or expertise needed to implement an intervention
• Capital intensity. Amount of capital resources required for an intervention
• Cultural acceptability. Appropriateness of an intervention based on social norms or religious beliefs in the respondent’s geographic region.

Table 12.3 presents the average rankings across experts for both priority and feasibility, and for the four feasibility components.

Table 12.3

Results of Delphi Surveys: Round 1 Expert Opinions Regarding Intervention Priority and Implementation Feasibility. Average ranking

With regard to average feasibility, the respondents ranked blood pressure control (3.67), preconception care among women with diabetes (3.42), screening for gestational diabetes (3.23), smoking cessation (3.21), and comprehensive foot care (3.19) as the most feasible interventions because they have the greatest reach, lowest technical complexity, and lowest resource needs and are likely to be the most culturally acceptable.

However, scores for feasibility diverged from scores for priority. Notably, only one of the six highest-priority interventions (preconception care among women with diabetes) was also considered highly feasible. Lifestyle interventions to prevent diabetes were considered the least feasible (2.29).

In round 2, respondents were asked to list up to 15 innovative or novel strategies that would facilitate diabetes prevention and control in LMICs and to rank them according to their priority and feasibility. The resulting list of 106 strategies was organized into 15 intervention categories; table 12.4 presents how many times each strategy was mentioned.

Table 12.4

Number of Mentions for Priority or Feasibility of Innovative Intervention Strategies.

Knowledge Gaps

The effectiveness of interventions to prevent and control diabetes is undisputed. With two exceptions—screening for both prediabetes and diabetes and preventive interventions among persons with iIFGs—all of these interventions have been tested in well-designed, large RCTs. There is no reason to believe that effectiveness will be greatly different across different countries. Some variation in effectiveness is likely, perhaps because of supply-side differences in reach and implementation or because of demand-side differences in yield, adoption, adherence, and maintenance.

Although the above arguments justify the use of HIC data for LMIC contexts, there is no substitute for local, country-specific data because diabetes prevention and management interventions require sustained behavioral changes, and local social, environmental, cultural, political, and economic conditions affect successful implementation of and adherence to these interventions. Data from LMICs are far overshadowed by data from HICs. Most LMICs still do not have representative surveys that can identify the population-wide prevalence of diabetes, prediabetes, and subtypes of diabetes or the proportion of people with diabetes who are undiagnosed. There are almost no longitudinal data for understanding the natural history of diabetes and whether different ethnic, genotypic, or phenotypic variants of prediabetes and diabetes progress differently. There are also very few, if any, data regarding childhood- or adolescent-onset type 2 diabetes and interventions to address it, even though it appears to be a growing problem. More local observational and experimental data from LMICs are necessary.

In addition, there are key knowledge gaps regarding supply-side aspects of how to implement diabetes detection, prevention, and treatment in LMICs. The understanding of how well-tested diabetes prevention and treatment programs need to be adapted to optimize effectiveness in local LMIC circumstances, or indeed, to address comorbidities (such as diabetes and HIV/AIDS) is very cursory (see chapter 16 in this volume, Magee and others 2017). These aspects, as well as a better understanding of existing infrastructure, resources, and competencies, can help shape how diabetes prevention and treatment interventions will be delivered, by whom, and where. In addition, each country’s health care financing varies considerably, and decision makers in payer institutions (ministries, insurance companies, employers) will want to know what up-front, fixed, and variable resource investments are required; what return on investment is possible; and over what time horizon. Indeed, trials of low-cost diagnostics and technologies for detection, prevention, and management hold much promise for lowering supply-side costs, but data are extremely scarce. These factors are important considerations and are relevant to integrating diabetes prevention and management services into routine systems.

There are also large gaps in the availability of qualitative data from LMICs (Hennink and others 2016). In particular, data that inform intervention adaptation, but also marketing, are crucial for generating demand for diabetes services. Intervention programs, tools, and facilities need to be coupled with efforts to optimize adoption, adherence to, and maintenance of the intervention. Generating demand requires effective messaging, communicating the opportunities through appropriate avenues, and using culturally sensitive wording and approaches. Communication needs to convince decision makers that these interventions fit within a context of supportive policies. Diabetes detection, prevention, and control could be facilitated or hindered at employer, insurance, or regulatory levels. For example, employers that support employee efforts to manage their disease risk may encourage better control and be rewarded through employee loyalty and performance.

In comparison with our understanding of individual-focused behavioral interventions, the evidence on societal-level nutrition, agricultural, physical activity, health financing, regulatory, and built-environment policies is weak in design and inconsistent across all countries (Roberto and others 2015). Any policy or program should be accompanied by robust evaluation with an appropriate control group and repeated assessments of exposures and outcomes (Soumerai, Starr, and Majumdar 2015). Evaluations of natural experiments (interventions not introduced or manipulated by researchers) using rigorous methods may provide efficient opportunities to guide future policy (Ackermann and others 2015). Indeed, we also do not know the comparative effectiveness and cost-effectiveness of individual versus population-level interventions.

Recommendations

To recommend priority interventions, we identified interventions for which the data about clinical effectiveness, topical importance, feasibility, and cost-effectiveness were most convergent. In other words, interventions that were, on aggregate, considered effective, important, feasible, and cost-effective were given highest priority. The following interventions to detect, prevent, and manage diabetes should be prioritized in LMICs:

• Blood pressure control among people with diabetes is considered a high priority, most feasible, and very cost-effective.
• Care management as well as aspects of care (self-management education) to support better risk factor control and preventive practices are considered effective and cost-effective and may be delivered by community health workers in practice.
• Lifestyle interventions to prevent diabetes among high-risk individuals are highly effective and cost-effective, but implementing them is challenging, which may reflect our limited understanding of the processes, personnel, financing, and infrastructure needed to deliver and sustain acceptable diabetes prevention services in various LMIC contexts. Yet lifestyle interventions may offer the greatest long-term possibility to slow the growth of diabetes worldwide and warrant further investigation regarding best practices.
• Good glycemic control has important microvascular benefits and may be achievable, but it comes at a fairly high cost. Lipid-lowering medications to prevent CVD events and mortality are also highly efficacious and quite achievable, but they are not universally cost-effective unless cheaper generic versions become widely available. Smoking cessation and foot care are considered important and feasible and have demonstrated value (Li and others 2010).

With respect to the priority actions and approaches to achieving these goals, we recommend pursuing the following supply- and demand-side interventions (in no specific order):

• Targeted (two-step) screening to identify people with prediabetes and diabetes (users of supply-side interventions) is cost-effective and should be a priority, and it should be espoused in guidelines and policy. This step should be complemented by efforts to ensure that the health system has adequate capacity to address the needs identified through screening.
• Purchasers of health care (governments, insurers, employers) should facilitate physical and financial access to essential medicines to treat diabetes and vascular risk factors in diabetes. These medicines should include at least insulin and two classes of oral glucose-lowering medications, at least two classes of blood-pressure-lowering medications, a statin, and possibly a selective serotonin reuptake inhibitor. Access to laboratory testing for periodic monitoring would complement the effectiveness of medications.
• Employers and providers should offer and deliver prevention (health promotion) and care (self-management support) services in clinical and nonclinical settings (workplaces and communities) through trained nonclinical staff (community health workers, lifestyle coaches). Although building capacity and creating infrastructure are expensive up-front costs, health payers, whether government or private, should consider the longer-term benefits and possibilities for financing operational costs sustainably through other means (user fees).
• Researchers should give high priority to addressing important knowledge gaps, especially regarding implementation and scalability.

Conclusions

The global burden of diabetes is colossal, especially among those least equipped to pay for treatment of end-stage disease. There is general consensus—combining evidence from published sources and expert opinions—that interventions to identify risk and to prevent and manage diabetes are effective, important, and provide value in most settings. There are large gaps in our understanding of how to facilitate implementation, engagement, and sustained success, especially in diverse settings. Governments could consider supporting research to address data gaps with regard to distribution and natural history of disease, implementation sciences, and cost-lowering technologies; building capacity; strengthening infrastructure; and covering up-front costs to catalyze socially valued programs.

Given the pluralistic and evolving needs and priorities in different countries and health care systems—many of which are experiencing changes in disease prevalence, capacity, and financing—ongoing research and evaluation of health system and societal interventions are essential to guide policy makers, donors, communities, and care providers.

Annexes

The annexes to this chapter are as follows. They are available at http://www.dcp-3.org/CVRD.

• Annex 12A. Effectiveness and Quality of Evidence Regarding Diabetes Screening, Prevention, and Treatment Interventions
• Annex 12B. Methodology for Cost-Effectiveness Analyses
• Annex 12C. Studies Reporting Cost-Effectiveness of Diabetes Screening, Prevention, and Treatment Interventions
• Annex 12D. Valuing Interventions—Regional Ceiling Ratios for Benchmarking
• Annex 12E. Gathering and Analyzing Stakeholder Perspectives to Prioritize Interventions

Note

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

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