Periodic Reporting for period 3 - E4I (Improving health services to prevent heart attacks and strokes: Evidence for interventions (E4I) in large middle-income countries)
Período documentado: 2023-06-01 hasta 2024-11-30
Largely due to the ageing of their populations and changing lifestyles, middle-income countries (MICs) are facing a rapidly increasing burden of heart attacks and strokes. Most of these cardio- and cerebrovascular disease (CCVD) events are preventable through successful treatment of three major risk factors: diabetes, dyslipidaemia, and hypertension. Yet despite the existence of inexpensive and effective medications, only a small minority of adults with these risk factors in MICs successfully transition through the care continuum from screening to effective treatment. There is currently little to no evidence from these settings on what health services interventions are most effective in reducing the loss of patients along the CCVD risk factor care continuum. Focussing on the four most populous MICs – which jointly account for 43% of the world’s population – E4I thus aims to i) determine at which of the main steps in the care continuum – screening, linkage to care, and retention in care – the greatest loss of patients occurs; ii) establish which health services interventions have been most effective in reducing the loss of patients at each of these three care steps; and iii) ascertain the causal effect of reducing the loss of patients along the care continuum on individuals’ health and economic outcomes. To do so, E4I will use novel causal inference techniques from different academic disciplines on large population-based cross-sectional and cohort datasets with jointly over seven million participants, challenging the frequently-held beliefs in public health that only randomised trials can provide causal effect estimates and that cohort data’s principal value is the study of disease aetiology. By generating urgently needed knowledge on how to more effectively deliver proven treatments for a major public health problem in MICs, E4I will decisively advance public health research and has the potential to have an important impact on population health globally.
We have focused our activities on Aim 1 of E4I, which tries to determine at which of the three main steps in the care continuum – screening, linkage to care, and retention in care – the greatest loss of patients occurs. As such, we have worked on detailed literature reviews and analyses to generate care cascade estimates for screening, diagnosis, treatment, and control of diabetes and hypertension. We have gained interesting insights. In the case of hypertension, we found that over a 5- to 9-year period, many individuals diagnosed with hypertension stop treatment and most of them lose blood pressure control. There was also considerable stasis at early care stages: two of three undiagnosed individuals remained undiagnosed, and three of four untreated individuals remained untreated over the period. We also found that cross-sectional continuums could provide a very different estimate of the benefits of improving diagnosis and treatment when compared with longitudinal estimates that track individuals over time. In Indonesia, for example, 68% of individuals that were diagnosed in 2007 reported treatment and 20% had a controlled blood pressure. This gave the impression that improving diagnosis was the primary bottleneck to blood pressure control. However, when following individuals longitudinally, just 34% of those that became diagnosed between 2007 and 2014 initiated treatment and just 4% achieved control, revealing that the cross-sectional continuum substantially overvalued the potential benefits of diagnosis alone. These longitudinal perspectives highlight that achievement of blood pressure control is seldom sustained over time and that policies solely aimed at improving diagnosis or initiating treatment may not lead to large improvements in control, because those who are diagnosed are unlikely to start treatment and those who start treatment tend to discontinue treatment over time. This work has not yet been published but we are actively working on generating scientific manuscripts for publication from this work.
Developing targeted, evidence-informed policies to manage diabetes, hypertension and dyslipidemia first requires identifying the greatest gaps in care. Prior studies have used the care continuum—the series of sequential steps from diagnosis to treatment and control of the condition—for assessing gaps in management. The major limitation of these studies is that they were based on cross-sectional data and thus failed to capture critical dynamic elements of chronic disease management, such as how individuals arrived at a specific continuum stage or whether they move forward or backward from that stage over time. For example, individuals who are diagnosed but untreated either could have failed to progress through the continuum by not initiating treatment or could have moved backward through the continuum by stopping treatment after having initiated it previously. Identifying which pathways are more pronounced is critical for formulating effective policy because the reasons for a failure to initiate treatment are likely different from the reasons for discontinuing treatment. Cross-sectional continuums may give the impression that individuals who are treated and achieved control of their condition require no additional attention and that policies should focus on progressing individuals from other continuum stages. However, reaching a state of treatment and control is not permanent, and individuals may fail to maintain control over time due to physiological changes from aging, shifting lifestyle patterns, or because they are inconsistent with or stop treatment. Because they are based on comparisons of different individuals at one time point rather than on the same individuals over time, cross-sectional continuums may also give a false impression of the proportion of individuals that would initiate treatment and reach control if diagnosed. Before our project, there were few detailed longitudinal studies describing individuals’ movement through the care continuum over time in middle-income countries.