Mukabutera Assumpta, Thomson Dana R, Hedt-Gauthier Bethany L, Atwood Sidney, Basinga Paulin, Nyirazinyoye Laetitia, Savage Kevin P, Habimana Marcellin, Murray Megan
School of Public Health, University of Rwanda College of Medicine and Health Sciences, Kigali, Rwanda.
Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA.
Trop Med Int Health. 2017 Dec;22(12):1505-1513. doi: 10.1111/tmi.12995. Epub 2017 Nov 9.
Public health interventions are often implemented at large scale, and their evaluation seems to be difficult because they are usually multiple and their pathways to effect are complex and subject to modification by contextual factors. We assessed whether controlling for rainfall-related variables altered estimates of the efficacy of a health programme in rural Rwanda and have a quantifiable effect on an intervention evaluation outcomes.
We conducted a retrospective quasi-experimental study using previously collected cross-sectional data from the 2005 and 2010 Rwanda Demographic and Health Surveys (DHS), 2010 DHS oversampled data, monthly rainfall data collected from meteorological stations over the same period, and modelled output of long-term rainfall averages, soil moisture, and rain water run-off. Difference-in-difference models were used.
Rainfall factors confounded the PIH intervention impact evaluation. When we adjusted our estimates of programme effect by controlling for a variety of rainfall variables, several effectiveness estimates changed by 10% or more. The analyses that did not adjust for rainfall-related variables underestimated the intervention effect on the prevalence of ARI by 14.3%, fever by 52.4% and stunting by 10.2%. Conversely, the unadjusted analysis overestimated the intervention's effect on diarrhoea by 56.5% and wasting by 80%.
Rainfall-related patterns have a quantifiable effect on programme evaluation results and highlighted the importance and complexity of controlling for contextual factors in quasi-experimental design evaluations.
公共卫生干预措施通常是大规模实施的，其评估似乎很困难，因为这些措施往往多种多样，其作用途径复杂，且会受到背景因素的影响而发生变化。我们评估了控制与降雨相关的变量是否会改变卢旺达农村一项卫生项目效果的估计值，并对干预评估结果产生可量化的影响。
我们进行了一项回顾性准实验研究，使用了2005年和2010年卢旺达人口与健康调查（DHS）之前收集的横断面数据、2010年DHS超抽样数据、同期从气象站收集的月降雨量数据，以及长期降雨平均值、土壤湿度和雨水径流的模型输出数据。使用了差分模型。
降雨因素混淆了健康伙伴组织（PIH）干预措施的影响评估。当我们通过控制各种降雨变量来调整项目效果的估计值时，几项有效性估计值变化了10%或更多。未对与降雨相关的变量进行调整的分析低估了干预措施对急性呼吸道感染患病率的影响14.3%，对发烧的影响52.4%，对发育迟缓的影响10.2%。相反，未调整的分析高估了干预措施对腹泻的影响56.5%，对消瘦的影响80%。
与降雨相关的模式对项目评估结果有可量化的影响，并突出了在准实验设计评估中控制背景因素的重要性和复杂性。
