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Improving Evidence for Implementation of Guideline-Based Care in Low- and Middle-Income Countries.

Coronary heart disease is the number one cause of death and disability in India and around the world.1 Evidence-based approaches for prevention and treatment of coronary heart disease are widely available but are not used optimally, particularly in lowand middleincome countries.2,3 In India, like in many countries, there are major gaps between actual and ideal care, and quality of care is substantially worse for patients with low socioeconomic status.4 Studies of quality improvement in Brazil,5 China,6 and India7 have been associated with improvements in acute coronary syndrome care5-7 and in clinical outcomes.7 However, there is an important need to further develop approaches to improve implementation of evidence-based care in these countries. Certain principles apply to implementing guidelines in any health care setting, as summarized by reviews of the evidence of what has been shown to be effective.8 Effective strategies have been multifaceted, as there are numerous aspects to implementation such that any one element, such as physician reminders or a care pathway, is unlikely to make a large difference. Audit and feedback are important, and data collection is essential: if implementation strategies and outcomes are not measured, they cannot be improved. Targeted interactive education such as with outreach visits by experts from outside the practice setting is important. The interventions must also be customized to account for the barriers of the local health care setting. Understanding the gaps and barriers is a requirement for developing effective interventions in any health care system. In India, barriers to optimal care of acute coronary syndromes vary by region. These include financial barriers for patients and their families, transportation limitations in both rural (lack of transportation modes) and urban (heavy traffic) settings, lack of sophisticated emergency medical services, and inadequate integrated information systems to track patients through systems of care. Solutions have included use of cell phones for efficient transfer of information, including to patients and their families, and use of nonphysician health care workers to educate patients and their families, especially after discharge from the hospital, when use of effective treatments begins to wane.9 Progressively more rigorous approaches have been developed for generation of evidence regarding what works for implementing guidelines. Most quality improvement programs have used a pre/post design, in which performance and outcomes are measured over time as an intervention to improve care is implemented. Although this use of a “historical control” group can be compelling, secular trends in both the patient population and treatments may result in the incorrect conclusion of cause and effect of the intervention. Patients can be randomized to interventions that are targeted at the individual patient. However, when the intervention is at the level of the health care system, randomization at the level of the clinic or hospital is needed. This can be done as a cluster randomized trial, in which either the effect can be compared directly between the groups after a period of intervention5 or the change over time can be compared between the intervention and control groups.10 Cluster randomized designs have a specific set of limitations: recruitment of sites that are all focused on optimizing care may limit the ability to detect differences; enrolling selected patients because of requirements for informed consent may result in a lower-risk and more intensely treated population (by excluding patients with comorbidities and cognitive impairment); and the knowledge of treatment assignment may influence which patients are recruited (with a desire of sites assigned to the intervention to select well-treated patients). Moreover, the likelihood of sites being assigned to the control group may discourage participation because of the perception that those sites would not benefit from being in the study. Thus, another study design is the stepped-wedge design, in which all sites start in the control group, then sites are randomly assigned to times (or in steps) of switching to the intervention group, with statistical modeling to control for change over time and clustering effects. This is the design used in the ACS QUIK study by Huffman and colleagues reported in this issue of JAMA.11 In this study, a program to improve clinical outcomes and process measures was evaluated in 63 hospitals across the southern state of Kerala in India. In total, 21 374 patients with acute myocardial infarction were studied, and the primary outcome, a composite of all-cause mortality, reinfarction, stroke, or major bleeding, was assessed at 30 days. The intervention was carefully constructed to be state of the art: it was multifaceted and locally adapted, drawing from what has been effective in other studies, with audit and feedback, and with targeted education and external assistance in data review. During 5 predefined steps over the study period, the investigators randomly selected hospitals to move in a 1-way crossover from the control to the intervention group. The hospitals provided either usual care (control group; n = 10 066 patients [step 0: n=2915; step 1: n=2649; step 2: n=2251; step 3: n=1422; step 4; n=829; step 5: n=0]) or care based on use of a quality improvement tool kit (intervention group; n = 11 308 patients [step 0: n=0; step 1: n=662; step 2: n=1265; step 3: n=2432; step 4: n=3214; step 5: n=3735]) that involved audit Related article page 567 Opinion