PDF(Pubmed)
Abstract：
Recently developed cost and affordability of healthy diet (CoAHD) metrics have quickly become mainstream food security indicators. However, published research on the sensitivity of estimation methods is limited. This paper focuses on two important innovations in CoAHD measurement at the global level. First, we develop a demographic scaling factor to adjust healthy diet costs for cross-country differences in age structures, since younger populations generally require fewer calories than older populations. Second, we improve the way in which household expenditure available for purchasing food (\"food budgets\") are derived. In addition, we explore sensitivity of global CoAHD estimates to potential problems with the representativeness and food product coverage of global food price data and vary assumptions for activity levels that shape energy expenditure requirements. We apply these explorations to the EAT-Lancet reference diet in 137 countries using price data from 2017. Relative to the conventional methods, we find that demographic scaling and improved food budget derivation substantially reduces the estimated population who cannot afford a healthy diet, from 3.02 to 2.13 billion. Adjustments for low product coverage can lead to modest reductions for specific regions and food groups, while higher physical activity assumptions increase the share of people who cannot afford a healthy diet, though perhaps implausibly so. Methods clearly matter in CoAHD estimation, and more accurate and timelier CoAHD estimates have substantial scope to improve policy analysis, design and targeting.
摘要：
最近开发的健康饮食（CoAHD）指标的成本和可负担性已迅速成为主流食品安全指标。然而,已发表的关于估计方法敏感性的研究是有限的。本文重点研讨了CoAHD丈量在全球层面上的两个重要立异点。首先,我们开发了一个人口缩放因子来调整健康饮食成本，以适应年龄结构的跨国差异，因为年轻人群通常比老年人需要更少的卡路里。第二,我们改善了可用于购买食物的家庭支出(“食物预算”)的来源方式。此外,我们探讨了全球CoAHD估计值对全球食品价格数据的代表性和食品覆盖率的潜在问题的敏感性,以及对影响能源支出需求的活动水平的不同假设.我们使用2017年的价格数据将这些探索应用于137个国家的EAT-Lancet参考饮食。相对于传统方法,我们发现人口规模和改善的食物预算推导大大减少了无法负担健康饮食的估计人口，从3.02亿到21.3亿。对低产品覆盖率的调整可能会导致特定地区和食品集团的适度减少，而更高的体力活动假设增加了负担不起健康饮食的人的比例，虽然也许令人难以置信。方法在CoAHD估计中显然很重要，更准确、更及时的CoAHD估计有很大的空间来改善政策分析，设计和定位。
