Isanaka Sheila, Hedt-Gauthier Bethany L, Grais Rebecca F, Allen Ben G S
Department of Research, Epicentre, 8 rue Saint Sabin, 75011, Paris, France.
Departments of Nutrition and Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, USA.
Popul Health Metr. 2018 Jul 3;16(1):11. doi: 10.1186/s12963-018-0167-3.
Many health programs can assess coverage using standardized cluster survey methods, but estimating the coverage of nutrition programs presents a special challenge due to low disease prevalence. Used since 2012, the Semi-Quantitative Evaluation of Access and Coverage (SQUEAC) employs both qualitative and quantitative methods to identify key barriers to access and estimate coverage of therapeutic feeding programs. While the tool has been increasingly used in programs, the validity of certain methodological elements has been the subject of debate.
We conducted a study comparing a SQUEAC conjugate Bayesian analysis to a two-stage cluster survey estimating the coverage of a therapeutic feeding program in Niger in 2016.
We found that the coverage estimate from the conjugate Bayesian analysis was sensitive to the prior estimation. With the exception of prior estimates produced by an external support team, all prior estimates resulted in a conflict with the likelihood result, excluding interpretation of the final coverage estimate. Allowing for increased uncertainty around the prior estimate did not materially affect conclusions.
SQUEAC is a demanding analytical method requiring both qualitative and quantitative data collection and synthesis to identify program barriers and estimate coverage. If the necessary technical capacity is not available to objectively specify an accurate prior for a conjugate Bayesian analysis, alternatives, such as a two-stage cluster survey or a larger likelihood survey, may be considered to ensure valid coverage estimation.
NCT03280082 . Retrospectively registered on September 12, 2017.
许多卫生项目可以使用标准化整群调查方法评估覆盖率,但由于疾病患病率较低,估计营养项目的覆盖率面临特殊挑战。自2012年起使用的获取与覆盖率半定量评估法(SQUEAC)采用定性和定量方法来确定获取的关键障碍并估计治疗性喂养项目的覆盖率。虽然该工具在项目中使用得越来越多,但某些方法要素的有效性一直是争论的焦点。
我们开展了一项研究,将SQUEAC共轭贝叶斯分析与2016年在尼日尔进行的估计治疗性喂养项目覆盖率的两阶段整群调查进行比较。
我们发现共轭贝叶斯分析得出的覆盖率估计对先验估计很敏感。除了外部支持团队得出的先验估计外,所有先验估计都与似然结果相冲突,无法对最终覆盖率估计进行解读。在先验估计周围增加不确定性对结论没有实质性影响。
SQUEAC是一种要求较高的分析方法,需要定性和定量数据收集与综合来确定项目障碍并估计覆盖率。如果没有客观指定共轭贝叶斯分析准确先验的必要技术能力,可以考虑采用替代方法,如两阶段整群调查或更大规模的似然调查,以确保有效的覆盖率估计。
NCT编号03280082。于2017年9月12日追溯注册。