Sanhueza Antonio, Espinosa Isabel, Mújica Oscar J, da Silva Jarbas Barbosa
Organización Panamericana de la Salud Washington, DC Estados Unidos de América Organización Panamericana de la Salud; Washington, DC., Estados Unidos de América.
Rev Panam Salud Publica. 2020 Dec 18;44:e155. doi: 10.26633/RPSP.2020.155. eCollection 2020.
Present methodology for the concurrent development of quantitative targets that reflect improvement in the national average of an indicator for Sustainable Development Goal 3 (SDG3), as well as a reduction in geographic inequality.
A five-step algorithm was developed: a) calculate the national average annual percentage change (AAPC) for an SDG3 indicator; b) standardize the definition of geographic strata based on subnational distribution of the indicator in a base year; c) apply a criterion for proportional progress in the AAPC in order to project the stratum-specific indicator to the target year; d) set the national target as the weighted average of the indicator in the subnational territorial units for the target year; and e) develop inequality reduction targets by calculating absolute and relative gaps between the top and bottom strata for the target year.
The algorithm was applied to SDG indicator 3.1.1 (maternal mortality ratio, MMR), disaggregated by Guatemala's 22 departments for base year 2014 (MMR = 113/100,000 live births). By sustaining the average AAPC rate attained from 2009 to 2014 (-4.3%) and targeting its actions to territorial progress, the country would reduce its MMR to 53/100,000 by 2030 and its absolute and relative gaps by 72% and 48%, respectively.
The proposed methodology makes it possible to concurrently develop targets for the reduction of geographic inequalities in health and improvements in the national average, with explicit reference to the primacy of the principle of equity expressed in the SDGs' commitment to , whose urgency is newly important in the current post-pandemic scenario.
提出用于同步制定定量目标的方法,这些目标既要反映可持续发展目标3(SDG3)某一指标的全国平均水平的改善情况,也要反映地理不平等的减少情况。
开发了一个五步算法:a)计算SDG3指标的全国平均年度百分比变化(AAPC);b)根据基准年该指标在国家以下层面的分布情况,对地理分层的定义进行标准化;c)应用AAPC中比例进展的标准,以便将特定分层指标推算至目标年;d)将国家目标设定为目标年国家以下各领土单位该指标的加权平均值;e)通过计算目标年最高和最低分层之间的绝对差距和相对差距,制定减少不平等目标。
该算法应用于SDG指标3.1.1(孕产妇死亡率,MMR),按危地马拉22个省在2014年基准年的数据进行分解(MMR = 每10万例活产113例)。通过维持2009年至2014年达到的平均AAPC率(-4.3%),并将行动目标设定为地区进展,该国到2030年将其MMR降至每10万例53例,其绝对差距和相对差距分别缩小72%和48%。
所提出的方法能够同时制定减少健康方面地理不平等和提高全国平均水平的目标,明确提及了SDGs对公平原则首要地位的承诺,在当前疫情后情景下,其紧迫性具有新的重要意义。