ABC | Volume 111, Nº1, July 2018

Original Article Stevens et al The Economic Burden of Heart Conditions in Brazil Arq Bras Cardiol. 2018; 111(1):29-36 Table 5 – Base case result SC TM STS Total costs (reais) 5 832 55 930 49 870 Total QALYs 3.99 5.89 5.61 Net monetary benefit 103 306 104 994 103 382 Incremental costs (reais) 50 098 44 038 Incremental QALYs 1.89 1.61 Incremental cost (reais) per QALY 26 437 27 281 Incremental net monetary benefit 1 688 77 QALY: quality-adjusted life-year; SC: standard care; TM: telemedicine; STS: structured telephone support. Multivariate sensitivity analysis An alternative multivariate scenario analysis was carried out where the costs of TM and STS were varied as well as the health state utilities. In this scenario, the costs of the interventions were increased by 20% and the health state utilities for health states for the strategies were assumed to be the same as those for SC. The results of this scenario analysis are presented in Table 6, which shows that the ICER increases from 26,437 to 41,123 reais/QALY for TM vs SC, and increases from 27,281 to 40,309 reais/QALY for STC vs SC. Assuming aWTP threshold of 27,328- 81,984 reais/QALY as above, the cost-effectiveness analysis suggests that TM and STS may be cost-effective treatment options for the management of patients with HF. Discussion Our analysis provides the inaugural estimate on the cost of the four conditions across Brazil. By analysing four conditions concurrently in a common framework, we were able to identify the total impact and the impacts of the conditions relative to each other. We have identified that, while MI has significant acute care costs, it does not have as significant informal care costs as HF or HTN. Conversely, HF, while not having as significant acute care costs as MI, has significant productivity losses. While HTN has a low health cost per person, it has a significant total cost due to the large number of people with the condition. Our analysis demonstrates that these conditions can have a large productivity and wellbeing impact beyond their health system costs, which is an important finding from a societal perspective. If policymakers focus only on health costs of a condition, or the relative cost of care per person, they may miss the broader impact that these conditions have across the economy, and the true cost once other fiscal impacts are taken into account. While the study has focused on using administrative datasets for health costs, as they are more likely to be reflective of cost allocation by payers, the datasets themselves may not reflect real costs for each condition. For example, the coding and reporting of conditions is subject to clinicians’ individual judgement in nominating the underlying cause, active condition, or chronic condition as the primary condition, and this choice can change the reporting of attributable impacts. A systematic review and meta-analysis of administrative databases for HF identified that datasets do not capture a quarter of cases, 27 while a systematic review of electronic medical data for AF identified that there was a disproportionate focus on inpatient data and additional research incorporating outpatient codes, and electrocardiogram data are required to correctly identify the presentations of AF. 28 Therefore, while the costs reported are reflective of current clinical judgement and administrative reporting, the cost allocation attributable to each condition can continue to be improved. In attributing the relative severity of conditions, their treatment and the impact on related conditions should be considered. Treatment of one of these conditions could alleviate the future development of another costed condition, and the detailed relationships between conditions are still being established. For example, while HTN is understood to be a common risk factor for heart conditions, there is a growing body of evidence that suggests AF is associated with MI. 29 Therefore, addressing AF could alleviate future cases of MI and the corresponding cost attributed to MI. The primary limitation in this study was comprehensive data availability. There are three key assumptions in the methodology that had to be made and could impact the results, which the reader should keep in mind. First, our health cost estimates are driven by reported hospital statistics for each of the conditions. This is likely to be more appropriate for conditions that have significant acute care management (e.g. MI), but it may under-represent the true cost of conditions that have a greater emphasis on primary care or pharmaceutical management, such as HTN. Second, common to all productivity estimates using a human capital approach, the unemployment rate for Brazil may or may not be sufficiently low to incur a permanent productivity loss. A loss in productivity due to heart conditions from a societal perspective will only equate to a loss in productivity to the economy under the condition that the economy is at the non-accelerating inflation rate of unemployment, so any reduction in hours worked due to illness cannot be replaced in the longer term by employing or increasing hours of other substitute workers. Thirdly, although TM and STS were found to provide beneficial effects in reducing all-cause mortality for recently discharged HF patients, in the original study, 24 these results were statistically inconclusive. While this uncertainty around estimates was assessed in the sensitivity analysis, these strategies will need to be re-examined as new evidence emerges. 34