Introduction

This chapter reviews the diagnosis and treatment of cardiovascular disease in low- and middle-income countries (LMICs) with a view to improving the quality of care. In keeping with the Institute of Medicine’s definition of quality as the “degree to which health services for individuals and population increase the likelihood of desired health outcomes and are consistent with current professional knowledge” (Lohr 1990, 4), the focus is on studies of specific interventions and measurable health outcomes. Because the resources available to support health care delivery in LMICs are scarce, this chapter seeks to improve clinical quality by getting the most out of known effective interventions within the limits of available resources rather than recommending unproven interventions that require early-phase studies or substantial investment to scale up. Clinical quality can be improved anywhere and at any time and doing so need not be expensive.

Quality standards and measures contain principles that can be compared and shared across countries and local settings. However, quality care delivery in low-resource settings does not necessarily mean dissemination and implementation of a universal set of standards—especially those formulated for cardiovascular diseases in high-income countries (HICs). Standards and interventions should be dictated by context and community capacity. Adaptation to the local setting is necessary for achieving optimal clinical outcomes and patient satisfaction.

A conceptual framework guided this chapter. The authors specified four domains, cutting across two distinct phases of cardiovascular disease (acute versus chronic) and two levels of intervention (health system versus patient-provider) (table 18.1). Health system–level interventions include those directly targeting one or more of the six “building blocks of a health system” as defined by the World Health Organization (2007). Patient-provider-level interventions are focused on influencing patient or provider behavior. Acute phases of cardiovascular disorders, such as acute myocardial infarction, stroke, and limb ischemia, occur unpredictably. Good outcomes demand timely clinical responses, which require adequate and accessible facilities, functional transportation networks, providers prepared to treat cases that present at all hours, and patient awareness of when and how to seek medical attention. In contrast, chronic phases of cardiovascular disorders, such as diabetes mellitus, hypertension, and congestive heart failure, require screening for preclinical risk factors, systematic monitoring for complications, and substantial patient self-care and engagement to initiate and maintain treatment adherence. Good-quality, chronic-phase care may prevent or delay onset of acute-phase manifestations, thereby preventing or delaying disability or death.

Table 18.1

Conceptual Framework for Quality of Care for Cardiovascular Diseases.

Quality interventions are examined at the health care system and patient-provider levels. The authors populated the four domains of this two-by-two framework with potential quality improvement levers based on previous knowledge of the field and examples gleaned from other chapters in this volume. Once the framework was established, a systematic literature review was conducted to identify evidence supporting specific interventions within it. The results are accompanied by detailed narratives of clinical quality improvement efforts for cardiovascular diseases, including the story of a comprehensive community-based cardiovascular disease primary prevention program in Kenya, the experience of an acute coronary syndrome (ACS) clinical pathways intervention in China, and a spotlight on mobile health (m-health) applications around the world.

Methodology

The methodology for the systematic review, including the electronic search terms used, is detailed in annex 18A. In brief, an electronic search was conducted of the MEDLINE and EMBASE databases to capture published reports of English-language studies on cardiovascular disease care quality improvement studies carried out in LMICs from January 2000 to June 2014. The review identified 49 full text papers that reported on completed, population-based studies with clinically meaningful outcomes. These studies were selected for the review and assigned to one or more categories in the chapter framework. The chapter highlights 32 of these studies.

System-Level Interventions

Acute Phase

Timely intervention can dramatically improve the outcomes of acute cardiovascular disease, while delays may result in unnecessary death or disability. System-level factors affect the time to treatment in both the prehospital and hospital phases of an acute event. Before arriving at a hospital, patients educated about the cardinal symptoms of cardiac disease will seek care more quickly and be aware of nearby hospitals or ambulance transport to regional centers. Hours of service availability are critically important. For example, if a patient with an acute cardiovascular event arrives in the middle of the night at a hospital with revascularization services, staff must be available to provide those services. Lack of awareness, lack of acceptability, lack of affordability, and lack of availability are all common barriers that can delay treatment of acute events (see chapter 16 on surgery volume quality in volume 1, Weiser and Gawande 2015).

System-wide planning can overcome barriers to timely and appropriate care for acute cardiovascular disease. The systematic review found limited evidence of interventions to improve system-level, acute-phase care (table 18.2). Poor underlying infrastructure in low-resource settings perhaps presents daunting challenges to reorganizing complex health care delivery systems (Macharia and others 2009). Just as likely, government, nongovernmental, and private sector organizations often introduce system improvements without rigorous systematic study; therefore, the health effects of system-level changes may go unmeasured or unreported. Randomized comparison studies in low- and middle-income settings may not be conducted because of lack of research capacity, perception of causing unwanted delay in care delivery, “contamination” between intervention and control sites, and ethical concerns.

Table 18.2

Selected Studies on System-Level, Acute-Phase Quality Improvement Interventions.

Alexander and others (2013) reported on a project being launched in the rural region within Tamil Nadu, India, which plans to implement a hub-and-spoke model using existing health care resources to improve the acute ST-elevation myocardial infarction (STEMI) care delivery system. Hub hospitals are capable of delivering timely percutaneous catheter-based reperfusion therapy, while spoke hospitals are primary health care facilities with or without capacity to deliver thrombolytic reperfusion therapy. Hubs and spokes are linked by privately owned professional ambulance services. After an observation phase, the hub-and-spoke program will be implemented, and primary outcomes are expected to change in response to rates of reperfusion therapy and time to coronary reperfusion.

Community-based education initiatives can prime the public by increasing awareness of clinical signs of ACS, stroke, and heart failure and enhance acceptability of acute care solutions in the community. The Kerala Acute Coronary Syndrome Program included community-based health education programs that promoted self-detection of acute coronary disease symptoms, rapid self-referral for treatment, and timely self-administration of aspirin (Prabhakaran and others 2008). The investigators concluded that improved patient awareness contributed to reductions in time-to-thrombolysis achieved by the multicomponent intervention.

No studies were found on the impact of improved geographic and temporal coverage of acute care services, including the impact of building more hospitals within underserved areas or making revascularization more widely available.

Chronic Phase

Most studies in the system-level, chronic-phase category examined the expansion of health insurance coverage (table 18.3). Two studies evaluated the health impact of the Seguro Popular insurance that was rolled out in 2002 as part of Mexico’s national universal health insurance plan. Seguro Popular covered approximately 50 million low-income people who had no formal health insurance—often because working family members participated in the informal economy. Based on data gathered in Mexican national health and nutrition surveys, Bleich and others (2007) found that, compared with matched hypertensive adults without insurance, Seguro Popular enrollees had 1.5-fold higher odds of receiving hypertension treatment and 1.4-fold higher odds of having controlled blood pressure. A similar study of low-income diabetic patients found those with Seguro Popular insurance were more likely to receive regular blood glucose control monitoring and maintain adequate glucose control compared with their matched, uninsured counterparts (Sosa-Rubi, Galarraga, and Lopez-Ridaura 2009). In rural Nigeria, hypertensive patients living in a district where community-based health insurance was available had significantly lower systolic and diastolic blood pressures, changes not observed in the control group without insurance (Hendriks and others 2014). In rural China, hypertensive patients receiving subsidies to defer medication costs had a 9 percent absolute increase in medication adherence and significantly lower annual out-of-pocket medical costs (Yu, Zhang, and Wang 2013).

Table 18.3

Selected Studies on System-Level, Chronic-Phase Quality Improvement Interventions.

System-level quality improvement efforts can lead to measurable improvements in health status in patients with chronic cardiovascular disease. These studies also demonstrate that the health impact of system-level changes can be rigorously evaluated. Researchers can simulate randomization through natural experiments, propensity score matching, or comparison of geographic areas or facilities with and without the intervention. Stepped-wedge trials introduce interventions to couple stepwise active and systematic program implementation with evaluation (Hemming and others 2015). As in the Seguro Popular studies, repeated population-based surveys can be leveraged to measure changes in chronic cardiometabolic disease risk factors and outcomes. Quality improvement studies will be most feasible where key outcomes are part of, or added to, ongoing surveys.

Many cardiovascular disease patients remain untreated or incompletely treated with standard oral medications for secondary prevention (Yusuf and others 2011). System-level policies to improve the availability and reduce the costs of essential preventive medicines have the potential to extend effective prevention to many more of these patients. No studies were found on the impact of essential medicines designations or pharmaceutical market regulations on the quality of clinical care for cardiovascular diseases (see chapter 8 in this volume, Dugani and others 2017).

In Sub-Saharan Africa, the substantial infrastructure investment that turned the tide of the human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS) epidemic is now being leveraged for chronic noncommunicable disease management. Groups like the Kenya-based Academic Model Providing Access to Healthcare (AMPATH) have leveraged the infrastructure established for chronic care to improve hypertension control in the communities they serve (box 18.1).

Box 18.1

Systems and Individuals: The AMPATH Chronic Disease Management Experience in Kenya.

Patient-Provider-Level Interventions

Acute Phase

ACS and acute stroke care have a remarkably strong evidence base, supported by randomized controlled trials of life-saving medications and reperfusion procedures (table 18.4). Professional societies have endorsed clinical practice guidelines that propose to set international quality standards for acute care. However, these quality standards are incompletely implemented even in high-income settings (Aliprandi-Costa and others 2011; Berwanger and others 2012; Cabana and others 1999; Du and others 2014; Fox and others 2002; Hoekstra and others 2002; Pearson, Goulart-Fisher, and Lee 1995). For years, the case for initiatives to improve the quality of ACS care was based on observations of quality gaps in registry studies; only recently has evidence emerged from randomized controlled trials (Flather and others 2011; Tu and others 2009).

Table 18.4

Selected Studies of Patient-Provider-Level, Acute-Phase Quality Improvement Interventions.

Modeling studies have projected that treating ACS patients according to the recommendations of clinical guidelines is cost-effective in LMICs (Megiddo and others 2014; Wang and others 2014; see chapter 8 in this volume [Dugani and others 2017]). However, the gap between current and optimal ACS care appears to be even wider in LMIC hospitals than in HIC hospitals (Berwanger and others 2012; Du and others 2014; Wang and others 2012; Wang and others 2014; Xavier and others 2008). The Kerala Acute Care Syndrome Registry, which studied 25,748 consecutive ACS admissions in hospitals in Kerala, India, over two years, found that 41 percent of STEMI patients reached the health care facility six hours or more after symptom onset (Mohanan and others 2013). Only 41 percent and 13 percent of STEMI patients received reperfusion therapy using thrombolytics or percutaneous coronary interventions, respectively. The study also demonstrated that optimal in-hospital and discharge medical care were delivered in only 40 percent and 46 percent of admissions, respectively, with rural hospitals performing worse than urban ones (Huffman and others 2013). Patients receiving optimal in-hospital medical therapy reported a 21 percent lower rate of major adverse in-hospital cardiovascular events.

Adopting HIC guidelines for LMICs offers a great opportunity both for implementing quality improvement standards and for benchmarking significant improvements in practice and outcomes. The ACS quality improvement studies identified in the review showed some improvements in measures of clinical process, but, like studies in HICs, only equivocal clinical improvements were found.

Berwanger and others (2012) randomized large urban hospitals in Brazil into those offering a multifaceted quality improvement program with educational material, reminders, algorithms, and training visits and those offering usual care. The intervention group had 2.64 higher odds of receiving evidence-based ACS therapy within the first 24 hours following symptom onset. There were no changes, however, in 30-day mortality or in-hospital cardiovascular events. Du and others (2014) randomized large urban Chinese hospitals to implement a U.S.-guidelines-based ACS pathway, along with periodic clinical performance audits and feedback throughout the intervention period (figure 18.1). Hospitals in the intervention arm showed higher rates of discharge for recommended therapies, but no difference in other indicators, including reperfusion in STEMI cases within 12 hours of symptom onset, door-to-needle time, door-to-balloon time, or high-risk patients undergoing angiography. As in Berwanger and others (2012), there were no significant differences in mortality or cardiovascular events. Prabhakaran and others (2008) enrolled 34 hospitals in the Kerala region of India to serve as their own controls in a pre- and postintervention design. After the multifaceted quality improvement intervention, there was a significant median reduction in time-to-thrombolysis of 54 minutes—from 193 to 139 minutes—and a significant increase in the use of evidence-based medications.

Figure 18.1

Clinical Pathways for Acute Coronary Syndrome in Hospitals with and without Catheterization Facilities in the Phase 2 CPACS-2.

In sum, selected studies of quality improvement programs for ACS and stroke care found improvements in some measures of clinical process, but not in clinical outcomes—similar to the pattern commonly found in programs in HICs. Even regarding surrogate measures of process, quality improvement studies yielded variable results. It may be that success depends on the support of health care providers and administrators and tailoring to the specific context of the participating health care system (that is, the availability of treatments and financial protection for patients). Lessons learned from these programs may be helpful for the design of future patient-provider-level studies on cardiovascular disease (box 18.2). Despite their limitations, these ambitious studies demonstrated that complex quality improvement programs can be implemented in the hospital setting in middle-income countries. No studies were found measuring the impact of physician education on diagnostic accuracy or clinical decision making related to acute cardiovascular disorders.

Box 18.2

Acute Care Quality Improvement in Middle-Income Countries: Lessons from the CPACS Study in China.

Chronic Phase

Adherence to life-saving medications and lifestyle changes is suboptimal worldwide, regardless of country income level (Yusuf and others 2011). Since the overwhelming majority of chronic-phase cardiovascular disease patients live in LMICs, where health care resources are limited, optimizing low-cost primary and secondary prevention interventions is critical. Numerous studies have been conducted on a variety of interventions to improve the quality of chronic-phase cardiovascular diseases in LMICs (table 18.5). Because patients are ideally prescribed several standard daily oral medications for primary or secondary prevention of cardiovascular disease, achievement of medication adherence, sometimes lifelong, is a key challenge for quality health care worldwide (figure 18.2). Most of the interventions reviewed were related to chronic medication adherence, specifically the use of fixed-dose combination pills, health care delivery supported by mobile communication technology, and task-shifting.

Table 18.5

Selected Studies of Chronic-Phase, Patient-Provider-Level Interventions.

Figure 18.2

Number of Drugs Taken for Coronary Heart Disease and Stroke by Individuals in the PURE Study, by Country Income Level, 2003–09.

Combination Pills

Many patients with cardiovascular disease are prescribed multiple daily medications. As the number of medications increases, the probability that patients will take all of the prescribed pills declines. For this reason, combining multiple medications into a single pill can improve adherence. Combined low doses of multiple medications in place of higher doses of a single medication also should lower the frequency of side effects.

Thom and others (2013) randomized persons at high risk of cardiovascular disease (CVD) living in India (one of four countries studied) to receive combination pills or the usual multiple-pill therapy. After a mean follow-up of about 15 months, participants taking the combination pills had 25 percent higher absolute adherence and small but significant reductions in both systolic blood pressure and low-density lipoprotein compared with participants randomized to receive multiple-pill treatment. Yusuf and others (2009) also found small but significant improvements among Indian subjects at risk for CVD when randomized to receive a combination pill containing multiple blood pressure medications, statins, and aspirin. A follow-up study by Yusuf and others (2012) showed that high-dose combination pills improved blood pressure and lipid control in high-risk Indian subjects compared with low-dose ones with similar rates of tolerability. Zou and others (2014) found that starting high-risk rural Chinese participants on combination pills achieved an 11 percent higher absolute adherence rate.

These trials show that combination pills can improve medication adherence and improve risk factor control in high-risk CVD patients. For this reason, efforts to approve and manufacture combination medications are underway (FDA 2014).

Mobile Communication Technology

Mobile technologies such as cell phones are becoming increasingly available in LMICs and are playing an important role in health promotion (box 18.3). Twelve studies were identified on the role of m-health in the prevention and treatment of diabetes, hypertension, heart failure, and coronary artery disease. Although not every study demonstrated a significant improvement in clinical care quality, these studies suggest that m-health via text messages and phone calls can be a useful tool for managing chronic cardiovascular conditions in LMICs.

Box 18.3

Mobile Health: Harnessing the Communication Revolution in LMICs.

Task-Shifting

Task-shifting refers to the rational redistribution of tasks among health care teams, often from a few highly trained health providers to a larger contingent of providers with less training (see chapter 17 in this volume, Joshi and others 2017; WHO 2008). Six studies were identified evaluating task-shifting for improving patient adherence to prescribed medications. Some studies coupled task-shifting with increased access to affordable or free medications (Erhun, Agbani, and Bolaji 2005; Kengne and others 2009), or family-based home health education and supplemental training of general practitioners (Jafar and others 2009).

Five task-shifting studies targeted hypertensive patients. Kengne and others (2009) carried out a large trial of hypertensive participants enrolled in a nurse-led clinic in Cameroon. Erhun, Agbani, and Bolaji (2005) evaluated the role of pharmacist-led clinics for patients with hypertension in Nigeria. Adeyemo and others (2013) randomized Nigerian participants with hypertension to clinic-based care with home visits or to clinic-based care only. Jafar and others (2009) conducted a cluster, randomized controlled trial of two interventions—home health education provided by health aides and training of general practitioners—in a population of Pakistani patients with hypertension. Regardless of the approach, intensified team-based care led to improved hypertension control.

Nesari and others (2010), the single study on diabetes, showed that having nurses call patients regularly to reinforce lifestyle changes and adjust medication doses led to a significant decrease in hemoglobin A1c. The intervention group increased adherence to lifestyle changes and glucose monitoring.

Guideline Implementation or Provider Education

Health care provider education and implementation of guidelines have the potential to standardize, improve, and sustain quality of care for cardiovascular and other conditions in LMICs. Studies of the impact of physician education and guideline dissemination yielded mixed results. Qureshi and others (2007) found that physician education through workshops and guideline dissemination led to significant improvements in patient care. Anchala and others (2015) revealed that providing physicians with a clinical decision support system for undertaking guideline-based hypertension management led to significant reductions in systolic blood pressure. However, Reutens and others (2012) and Steyn and others (2013) showed conflicting results and highlighted that guideline dissemination alone did not lead to actual implementation. Imposing guidelines without first gaining buy-in from providers may be a recipe for failure. Allocating time for education and feedback and strategically inserting guideline information into the flow of clinical practice may increase the chance that guidelines are actually implemented.

Conclusions

This chapter surveys the evidence on quality improvement in cardiovascular disease care at the system and patient-provider levels. An impressive amount of research on quality improvement has been carried out in LMICs—although not all approaches reviewed were consistently effective (figures 18.3 and 18.4). The innovative approaches taken by these programs demonstrate that it is not simply a matter of adapting HIC programs to LMICs: innovations to improve the quality of clinical care may originate precisely in low-resource environments. For example, the concept of shifting health care tasks to lay health workers originated in LMICs as a means to address the limited supply of medical doctors. As the AMPATH experience demonstrates (box 18.1), implementing a comprehensive approach to quality improvement, at both the system and patient-provider levels, is feasible in LMICs.

Figure 18.3

Examples of Acute-Phase Cardiovascular Disease Quality Improvement Interventions Identified in the DCP3 Systematic Review.

Figure 18.4

Examples of Chronic-Phase Cardiovascular Disease Quality Improvement Interventions Identified in the DCP3 Systematic Review.

The majority of studies in this review focused on chronic cardiovascular disease and chronic risk factors such as hypertension and diabetes. At the system level, expanded health insurance coverage was found to improve the control of hypertension and diabetes. These powerful findings likely stem from improved access to care and financial protection from out-of-pocket health expenditures. Pharmaceutical supply regulation, drug price regulation, and essential medication designations are all potentially powerful system-level interventions, but their impact on cardiovascular disorders has yet to be studied.

At the patient-provider level, increased intensity of care—however delivered or by whom—was consistently found to improve chronic disease or risk factor outcomes. Intensification involved a team-based approach that included extra health care provider input, such as shifting tasks to pharmacists, dieticians, or nurses; phone counseling; smartphone-based reminders; or home visits. There were no head-to-head comparative effectiveness studies between these approaches, and multiple approaches often were combined (for example, implementing both task-shifting and patient education), so no one approach stands out as better than the others. Care intensification inevitably requires up-front investment, but this investment may be offset by improved downstream health outcomes for cardiovascular disease. A modeling study by Gaziano and others (2014) projected that, despite the added costs of hiring community health workers to manage hypertension in South Africa, increased intensity of care may offset this investment by averting expensive hospital admissions and chronic disease complications.

The studies reviewed for this chapter were often limited in ways that require cautious interpretation of their results. Because of the diversity of interventions and conditions, effect sizes could not be summarized in a meta-analysis. First, it is possible that the studies were published because of their positive results, and, because of the heterogeneity of interventions and targets, it was not possible to evaluate evidence of publication bias. Second, most studies were very short term (less than 12 months), and sustaining intervention effects may be difficult in real clinical settings. As in HICs, most investigators studied clinical process measures and did not report on hard clinical outcomes, which may lead to gaming the system (via an inappropriately strong focus on reaching surrogate targets to the neglect of measures that improve meaningful outcomes) and other unintended consequences when these interventions are introduced into routine practice.

Although all of the studies measured some change in the quality of care, and some reported on the number of provider contacts and specified the technology or medications used, none reported on the costs or cost-effectiveness of these interventions. When resources are limited, the call to improve or restructure existing services may be tempered by the perception that implementation will be costly and not worth the effort—or at least not as attractive as an alternative policy with more immediate returns on investment. Cost and quality-of-life measurement and cost-effectiveness analyses can be important guides in assessing the net benefits of quality improvement programs in limited-resource contexts. Modeling studies can extend the results of short-term interventions and surrogate clinical measures by simulating a range of likely downstream disease outcomes. At the very least, future studies need to report on intervention inputs as measured by “units”—including the number of providers, contacts between patients and providers, medications, and education classes and teachers—so that clinics and health organizations can “cost out” interventions when seeking the best ones for their settings. Collecting data elements common to implementation research, such as acceptability, sustainability, local context, and affordability, will help ensure that both positive and negative studies will guide implementation and future research.

The majority of cardiovascular disease patients now live in LMICs, and demographic trends virtually guarantee that the number and proportion will grow in coming decades. To ensure that each of these patients receives long-term treatment and control, it is essential to draw on promising research on clinical quality improvement and make the most of the resources directly at hand.

Annex

The annex to this chapter is as follows. It is available at http://www.dcp-3.org/CVRD.

• Annex 18A. Systematic Review Methods and Complete Search Results

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 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.

Funding: AEM was supported by a grant from the U.S. National Heart, Lung, and Blood Institute career development award (K08 HL089675-01). Rajesh Vedanthan was supported by a grant from the Fogarty International Center of the U.S. National Institutes of Health (K01 TW 009218-05). Panniyammakal Jeemon is supported by a Wellcome Trust, Department of Biotechnology India Alliance clinical and public health intermediate career fellowship.

Disclaimer: None of the authors have conflicts to declare. The views expressed in this article are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1. Adeyemo A, Tayo B O, Luke A, Ogedegbe O, Durazo-Arvizu R., and others. 2013. “The Nigerian Antihypertensive Adherence Trial.” Journal of Hypertension 31 (1): 201–7. [PMC free article: PMC3530610] [PubMed: 23137954]
2. Alexander T, Victor S M, Mullasari A S, Veerasekar G, Subramaniam K., and others. 2013. “Protocol for a Prospective, Controlled Study of Assertive and Timely Reperfusion for Patients with ST-Segment Elevation Myocardial Infarction in Tamil Nadu: The TN-STEMI Programme.” BMJ Open 3 (12): e003850. [PMC free article: PMC3855601] [PubMed: 24302505]
3. Aliprandi-Costa B, Ranasinghe I, Chow V, Kapila S, Juergens C., and others. 2011. “Management and Outcomes of Patients with Acute Coronary Syndromes in Australia and New Zealand, 2000–2007.” Medical Journal of Australia 195 (3): 116–21. [PubMed: 21806528]
4. AMPATH (Academic Model Providing Access to Healthcare). 2015. “Our Model.” AMPATH. http://www​.ampathkenya.org/our-model.
5. Anchala R, Kaptoge S, Pant H, Di Angelantonio E, Franco O H., and others. 2015. “Evaluation of Effectiveness and Cost-Effectiveness of a Clinical Decision Support System in Managing Hypertension in Resource-Constrained Primary Health Care Settings: Results from a Cluster Randomized Trial.” Journal of the American Heart Association 4 (1): e001213. [PMC free article: PMC4330052] [PubMed: 25559011]
6. Andrade M V, Maia A C, Cardoso C S, Alkmim M B, Ribeiro A L. 2011. “Cost-Benefit of the Telecardiology Service in the State of Minas Gerais: Minas Telecardio Project.” Arquivos Brasileiros de Cardiologia 97 (4): 307–16. [PubMed: 21808852]
7. Berwanger O, Guimarães H P, Laranjeira L N, Cavalcanti A B, Kodama A A., and others. 2012. “Effect of a Multifaceted Intervention on Use of Evidence-Based Therapies in Patients with Acute Coronary Syndromes in Brazil.” Journal of the American Medical Association 307 (19): 2041–49. [PubMed: 22665103]
8. Bleich S N, Cutler D M, Adams A S, Lozano R, Murray C J. 2007. “Impact of Insurance and Supply of Health Professionals on Coverage of Treatment for Hypertension in Mexico: Population-Based Study.” BMJ 335 (7625): 875. [PMC free article: PMC2043407] [PubMed: 17954519]
9. Bloomfield G S, Kimaiyo S, Carter E J, Binanay C, Corey G R., and others. 2011. “Chronic Noncommunicable Cardiovascular and Pulmonary Disease in Sub-Saharan Africa: An Academic Model for Countering the Epidemic.” American Heart Journal 161 (5): 842–47. [PMC free article: PMC3093664] [PubMed: 21570512]
10. Cabana M D, Rand C S, Powe N R, Wu A W, Wilson M H., and others. 1999. “Why Don’t Physicians Follow Clinical Practice Guidelines? A Framework for Improvement.” Journal of the American Medical Association 282 (15): 1458–65. [PubMed: 10535437]
11. Du X, Gao R, Turnbull F, Wu Y, Rong Y., and others. 2014. “Hospital Quality Improvement Initiative for Patients with Acute Coronary Syndromes in China: A Cluster Randomized, Controlled Trial.” Circulation, Cardiovascular Quality, and Outcomes 7 (2): 217–26. [PubMed: 24619325]
12. Dugani S B, Moran A E, Bonow R O, Gaziano T A. 2017. “Ischemic Heart Disease: Cost-Effective Acute Management and Secondary Prevention.” In Disease Control Priorities, Cardiovascular, Respiratory, and Related Disorders (third edition): Volume 5, edited by Prabhakaran D, Anand S, Gaziano T A, Mbanya J C, Wu Y, Nugent R, editors. Washington, DC: World Bank.
13. Einterz R M, Kimaiyo S, Mengech H N, Khwa-Otsyula B O, Esamai F., and others. 2007. “Responding to the HIV Pandemic: The Power of an Academic Medical Partnership.” Academic Medicine 82 (8): 812–18. [PubMed: 17762264]
14. Erhun W O, Agbani E O, Bolaji E E. 2005. “Positive Benefits of a Pharmacist-Managed Hypertension Clinic in Nigeria.” Public Health 119 (9): 792–98. [PubMed: 15990127]
15. Etyang A O, Munge K, Bunyasi E W, Matata L, Ndila C., and others. 2014. “Burden of Disease in Adults Admitted to Hospital in a Rural Region of Coastal Kenya: An Analysis of Data from Linked Clinical and Demographic Surveillance Systems.” The Lancet Global Health 2: e216–24. [PMC free article: PMC3986034] [PubMed: 24782954]
16. Ferrante D, Varini S, Macchia A, Soifer S, Badra R., and others. 2010. “Long-Term Results after a Telephone Intervention in Chronic Heart Failure: DIAL (Randomized Trial of Phone Intervention in Chronic Heart Failure) Follow-Up.” Journal of the American College of Cardiology 56 (5): 372–78. [PubMed: 20650358]
17. Flather M D, Babalis D, Booth J, Bardaji A, Machecourt J., and others. 2011. “Cluster-Randomized Trial to Evaluate the Effects of a Quality Improvement Program on Management of Non–ST-Elevation Acute Coronary Syndromes: The European Quality Improvement Programme for Acute Coronary Syndromes (EQUIP-ACS).” American Heart Journal 162 (4): 700–7.e1. [PubMed: 21982663]
18. Fox K A, Goodman S G, Klein W, Brieger D, Steg P G., and others. 2002. “Management of Acute Coronary Syndromes: Variations in Practice and Outcome; Findings from the Global Registry of Acute Coronary Events (GRACE).” European Heart Journal 23 1177–89. [PubMed: 12127920]
19. Gaziano T A, Bertram M, Tollman S M, Hofman K J. 2014. “Hypertension Education and Adherence in South Africa: A Cost-Effectiveness Analysis of Community Health Workers.” BMC Public Health 14 (240). [PMC free article: PMC3973979] [PubMed: 24606986]
20. Gaziano T A, Reddy K S, Paccaud F, Horton S, Chaturvedi V. 2006. “Cardiovascular Disease.” 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. 645–62. Washington, DC: World Bank and Oxford University Press. [PubMed: 21250309]
21. GESICA Investigators. 2005. “Randomised Trial of Telephone Intervention in Chronic Heart Failure: DIAL Trial.” BMJ 331 (7514): 425. [PMC free article: PMC1188105] [PubMed: 16061499]
22. Goodarzi M, Ebrahimzadeh I, Rabi A, Saedipoor B, Jafarabadi M A. 2012. “Impact of Distance Education via Mobile Phone Text Messaging on Knowledge, Attitude, Practice, and Self-Efficacy of Patients with Type 2 Diabetes Mellitus in Iran.” Journal of Diabetes and Metabolic Disorders 11 (10). [PMC free article: PMC3598175] [PubMed: 23497632]
23. Hemming K, Haines T P, Chilton P J, Girling A J, Lilford R J. 2015. “The Stepped-Wedge Cluster Randomised Trial: Rationale, Design, Analysis, and Reporting.” BMJ 350: h391. [PubMed: 25662947]
24. Hendriks M E, Wit F W, Akande T M, Kramer B, Osagbemi G K., and others. 2014. “Effect of Health Insurance and Facility Quality Improvement on Blood Pressure in Adults with Hypertension in Nigeria: A Population-Based Study.” JAMA Internal Medicine 174 (4): 555–63. [PubMed: 24534947]
25. Hoekstra J W, Pollack C V Jr, Roe M T, Peterson E D, Brindis R., and others. 2002. “Improving the Care of Patients with Non-ST-Elevation Acute Coronary Syndromes in the Emergency Department: The CRUSADE Initiative.” Academic Emergency Medicine: Official Journal of the Society for Academic Emergency Medicine 9 (11): 1146–55. [PubMed: 12414463]
26. Huffman M D, Prabhakaran D, Abraham A K, Krishnan M N, Nambiar A C., and others. 2013. “Optimal In-Hospital and Discharge Medical Therapy in Acute Coronary Syndromes in Kerala: Results from the Kerala Acute Coronary Syndrome Registry.” Circulation, Cardiovascular Quality, and Outcomes 6 (4): 436–43. [PMC free article: PMC3830530] [PubMed: 23800985]
27. ITU (International Telecommunication Union). 2013. The World in 2014: ICT Facts and Figures. Geneva: ITU.
28. Jafar T H, Hatcher J, Poulter N, Islam M, Hashmi S., and others. 2009. “Community-Based Interventions to Promote Blood Pressure Control in a Developing Country: A Cluster Randomized Trial.” Annals of Internal Medicine 151 (9): 593–601. [PubMed: 19884620]
29. Joshi R, Kengne A P, Hersch F, Weber M B, McGuire H, Patel A. 2017. “Innovations in Community-Based Health Care for Cardiometabolic and Respiratory Diseases.” In Disease Control Priorities (third edition): Volume 5, Cardiovascular, Respiratory, and Related Disorders, edited by Prabhakaran D, Anand S, Gaziano T A, Mbanya J C, Wu Y, Nugent R, editors. Washington, DC: World Bank.
30. Kahn J G, Yang J S, Kahn J S. 2010. “‘Mobile’ Health Needs and Opportunities in Developing Countries.” Health Affairs 29 (2): 252–58. [PubMed: 20348069]
31. Kayima J, Wanyenze R K, Katamba A, Leontsini E, Nuwaha F. 2013. “Hypertension Awareness, Treatment, and Control in Africa: A Systematic Review.” BMC Cardiovascular Disorders 13 (54). [PMC free article: PMC3750220] [PubMed: 23915151]
32. Kengne A P, Awah P K, Fezeu L L, Sobngwi E, Mbanya J C. 2009. “Primary Health Care for Hypertension by Nurses in Rural and Urban Sub-Saharan Africa.” Journal of Clinical Hypertension 11 (10): 564–72. [PMC free article: PMC8673012] [PubMed: 19817937]
33. Khonsari S, Subramanian P, Chinna K, Latif L A, Ling L W., and others. 2014. “Effect of a Reminder System Using an Automated Short Message Service on Medication Adherence Following Acute Coronary Syndrome.” European Journal of Cardiovascular Nursing 14 (2): 170–79. doi:10.1177/1474515114521910. [PubMed: 24491349]
34. Kinfu Y, Dal M R, Poz H Mercer, Evans D B. 2009. “The Health Worker Shortage in Africa: Are Enough Physicians and Nurses Being Trained?” Bulletin of the World Health Organization 87 (3): 225–30. [PMC free article: PMC2654639] [PubMed: 19377719]
35. Lohr K N., ed. 1990. Medicare: A Strategy for Quality Assurance. Vol. 1. Washington, DC: National Academies Press. [PubMed: 25144047]
36. Macharia W M, Njeru E K, Muli-Musiime F, Nantulya V. 2009. “Severe Road Traffic Injuries in Kenya, Quality of Care and Access.” African Health Sciences 9 (2): 118–24. [PMC free article: PMC2707053] [PubMed: 19652745]
37. Manji I, Lukas S, Vedanthan R, Jakait B, Pastakia S. 2012. “Community-Based Approaches to Reduce Medication Stock Outs in Western Kenya.” Paper prepared for the Science of Eliminating Health Disparities Summit, Washington, DC, December 17–19.
38. Megiddo I, Chatterjee S, Nandi A, Laxminarayan R. 2014. “Cost-Effectiveness of Treatment and Secondary Prevention of Acute Myocardial Infarction in India: A Modeling Study.” Global Heart 9 (4): 391–98 e3. [PubMed: 25592792]
39. Mohanan P P, Mathew R, Harikrishnan S, Krishnan M N, Zachariah G., and others. 2013. “Presentation, Management, and Outcomes of 25,748 Acute Coronary Syndrome Admissions in Kerala, India: Results from the Kerala ACS Registry.” European Heart Journal 34 (2): 121–92. [PMC free article: PMC3538274] [PubMed: 22961945]
40. Nazzal N C, Campos T P, Corbalan H R, Lanas Z F, Bartolucci J J., and others. 2008. “The Impact of Chilean Health Reform in the Management and Mortality of ST Elevation Myocardial Infarction (STEMI) in Chilean Hospitals.” Revista Medica de Chile 136 (10): 1231–39. [PubMed: 19194618]
41. Nesari M, Zakerimoghadam M, Rajab A, Bassampour S, Faghihzadeh S. 2010. “Effect of Telephone Follow-Up on Adherence to a Diabetes Therapeutic Regimen.” Japan Journal of Nursing Science 7 (2): 121–28. [PubMed: 21092015]
42. Pearson S D, Goulart-Fisher D, Lee T H. 1995. “Critical Pathways as a Strategy for Improving Care: Problems and Potential.” Annals of Internal Medicine 123 (12): 941–48. [PubMed: 7486490]
43. Peng B, Ni J, Anderson C S, Zhu Y, Wang Y., and others. 2014. “Implementation of a Structured Guideline-Based Program for the Secondary Prevention of Ischemic Stroke in China.” Stroke 45 (2): 515–19. [PubMed: 24385269]
44. Piette J D, Datwani H, Gaudioso S, Foster S M, Westphal J., and others. 2012. “Hypertension Management Using Mobile Technology and Home Blood Pressure Monitoring: Results of a Randomized Trial in Two Low/Middle-Income Countries.” Telemedicine Journal and e-Health 18 (8): 613–20. [PMC free article: PMC4361160] [PubMed: 23061642]
45. Prabhakaran D, Jeemon P, Mohanan P P, Govindan U, Geevar Z., and others. 2008. “Management of Acute Coronary Syndromes in Secondary Care Settings in Kerala: Impact of a Quality Improvement Programme.” National Medical Journal of India 21 (3): 107–11. [PubMed: 19004139]
46. Qureshi N N, Hatcher J, Chaturvedi N, Jafar T H. 2007. “Effect of General Practitioner Education on Adherence to Antihypertensive Drugs: Cluster Randomised Controlled Trial.” BMJ 335 (7628): 1030. [PMC free article: PMC2078673] [PubMed: 17991935]
47. Ramachandran A, Snehalatha C, Ram J, Selvam S, Simon M., and others. 2013. “Effectiveness of Mobile Phone Messaging in Prevention of Type 2 Diabetes by Lifestyle Modification in Men in India: A Prospective, Parallel-Group, Randomised Controlled Trial.” The Lancet Diabetes and Endocrinology 1 (3): 191–98. [PubMed: 24622367]
48. Ramanath K V, Balaji D B S S, Nagakishore C H, Mahesh S Kumar, Bhanuprakash M. 2012. “A Study on Impact of Clinical Pharmacist Interventions on Medication Adherence and Quality of Life in Rural Hypertensive Patients.” Journal of Young Pharmacists 4 (2): 95–100. [PMC free article: PMC3385224] [PubMed: 22754261]
49. Reutens A T, Hutchinson R, Van Binh T, Cockram C, Deerochanawong C., and others. 2012. “The GIANT Study, a Cluster-Randomised Controlled Trial of Efficacy of Education of Doctors about Type 2 Diabetes Mellitus Management Guidelines in Primary Care Practice.” Diabetes Research and Clinical Practice 98 (1): 38–45. [PubMed: 22784926]
50. Rotheram-Borus M J, Tomlinson M, Gwegwe M, Comulada W S, Kaufman N., and others. 2012. “Diabetes Buddies: Peer Support through a Mobile Phone Buddy System.” Diabetes Educator 38 (3): 357–65. [PMC free article: PMC4059372] [PubMed: 22546740]
51. Shetty A S, Chamukuttan S, Nanditha A, Raj R K, Ramachandran A. 2011. “Reinforcement of Adherence to Prescription Recommendations in Asian Indian Diabetes Patients Using Short Message Service (SMS): A Pilot Study.” Journal of the Association of Physicians of India 59 (November): 711–14. [PubMed: 22616337]
52. Sosa-Rubi S G, Galarraga O, Lopez-Ridaura R. 2009. “Diabetes Treatment and Control: The Effect of Public Health Insurance for the Poor in Mexico.” Bulletin of the World Health Organization 87 (7): 512–19. [PMC free article: PMC2704037] [PubMed: 19649365]
53. Steyn K, Lombard C, Gwebushe N, Fourie J M, Everett-Murphy K., and others. 2013. “Implementation of National Guidelines, Incorporated within Structured Diabetes and Hypertension Records at Primary Level Care in Cape Town, South Africa: A Randomised Controlled Trial.” Global Health Action 6: 20796. [PMC free article: PMC3784670] [PubMed: 24070181]
54. Thom S, Poulter N, Field J, Patel A, Prabhakaran D., and others. 2013. “Effects of a Fixed-Dose Combination Strategy on Adherence and Risk Factors in Patients with or at High Risk of CVD.” Journal of the American Medical Association 310 (9): 918–29. [PubMed: 24002278]
55. Tu J V, Donovan L R, Lee D S, Wang J T, Austin P C., and others. 2009. “Effectiveness of Public Report Cards for Improving the Quality of Cardiac Care: The EFFECT Study; a Randomized Trial.” Journal of the American Medical Association 302 (21): 2330–37. [PubMed: 19923205]
56. U.S. FDA (United States Food and Drug Administration). 2014. “Briefing Document: Cardiovascular and Renal Drugs Advisory Committee Meeting.” U.S. FDA, Washington, DC.
57. Wang M, Moran A E, Liu J, Coxson P G, Heidenreich P A., and others. 2014. “Cost-Effectiveness of Optimal Use of Acute Myocardial Infarction Treatments and Impact on Coronary Heart Disease Mortality in China.” Circulation, Cardiovascular Quality, and Outcomes 7 (1): 78–85. [PMC free article: PMC4191653] [PubMed: 24425706]
58. Wang N, Zhao D, Liu J, Yu C, Wang W., and others. 2012. “Impact of Heart Failure on In-Hospital Outcomes of Acute Coronary Syndrome Patients in China: Results from the Bridging the Gap on CHD Secondary Prevention in China (BRIG) Project.” International Journal of Cardiology 160 (1): 15–19. [PubMed: 21453979]
59. Weiser T, Gawande A. 2015. “Excess Surgical Mortality: Strategies for Improving Quality of Care.” In Disease Control Priorities (third edition): Volume 1, Essential Surgery, edited by Debas H T, Gawande A, Jamison D T, Kruk M E, Mock C N, editors. Washington, DC: World Bank.
60. WHO (World Health Organization). 2007. Everybody’s Business: Strengthening Health Systems to Improve Health Outcomes. WHO’s Framework for Action. Geneva: WHO.
61. WHO (World Health Organization). 2008. Task Shifting: Global Recommendations and Guidelines. Geneva: WHO.
62. WHO (World Health Organization). 2013. Global Health Workforce Statistics, the 2013 Update. Geneva: WHO.
63. Xavier D, Pais P, Devereaux P J, Xie C, Prabhakaran D., and others. 2008. “Treatment and Outcomes of Acute Coronary Syndromes in India (CREATE): A Prospective Analysis of Registry Data.” The Lancet 371 (9622): 1435–42. [PubMed: 18440425]
64. Yu B, Zhang X, Wang G. 2013. “Full Coverage for Hypertension Drugs in Rural Communities in China.” American Journal of Managed Care 19 (1): e22–29. [PMC free article: PMC4538950] [PubMed: 23379776]
65. Yusuf S, Islam S, Chow C K, Rangarajan S, Dagenais G., and others. 2011. “Use of Secondary Prevention Drugs for Cardiovascular Disease in the Community in High-Income, Middle-Income, and Low-Income Countries (the PURE Study): A Prospective Epidemiological Survey.” The Lancet 378 (9798): 1231–43. [PubMed: 21872920]
66. Yusuf S, Pais P, Afzal R, Xavier D, Teo K., and others. 2009. “Effects of a Polypill (Polycap) on Risk Factors in Middle-Aged Individuals without Cardiovascular Disease (TIPS): A Phase II, Double-Blind, Randomised Trial.” The Lancet 373 (9672): 1341–51. [PubMed: 19339045]
67. Yusuf S, Pais P, Sigamani A, Zavier D, Afzal R., and others. 2012. “Comparison of Risk Factor Reduction and Tolerability of a Full-Dose Polypill (with Potassium) Versus Low-Dose Polypill (Polycap) in Individuals at High Risk of Cardiovascular Diseases: The Second Indian Polycap Study (TIPS-2) Investigators.” Circulation, Cardiovascular Quality, and Outcomes 5 (4): 463–71. [PubMed: 22787067]
68. Zou G, Wei X, Gong W, Yin J, Walley J., and others. 2014. “Evaluation of a Systematic Cardiovascular Disease Risk Reduction Strategy in Primary Health Care: An Exploratory Study from Zhejiang, China.” Journal of Public Health 37 (2): 241–50. [PubMed: 24696086]