Fottrell Edward, Högberg Ulf, Ronsmans Carine, Osrin David, Azad Kishwar, Nair Nirmala, Meda Nicolas, Ganaba Rasmane, Goufodji Sourou, Byass Peter, Filippi Veronique
UCL Institute for Global Health, University College London, 30 Guilford Street, London WC1N 1EH, United Kingdom.
Emerg Themes Epidemiol. 2014 Mar 13;11(1):3. doi: 10.1186/1742-7622-11-3.
Maternal morbidity is more common than maternal death, and population-based estimates of the burden of maternal morbidity could provide important indicators for monitoring trends, priority setting and evaluating the health impact of interventions. Methods based on lay reporting of obstetric events have been shown to lack specificity and there is a need for new approaches to measure the population burden of maternal morbidity. A computer-based probabilistic tool was developed to estimate the likelihood of maternal morbidity and its causes based on self-reported symptoms and pregnancy/delivery experiences. Development involved the use of training datasets of signs, symptoms and causes of morbidity from 1734 facility-based deliveries in Benin and Burkina Faso, as well as expert review. Preliminary evaluation of the method compared the burden of maternal morbidity and specific causes from the probabilistic tool with clinical classifications of 489 recently-delivered women from Benin, Bangladesh and India.
Using training datasets, it was possible to create a probabilistic tool that handled uncertainty of women's self reports of pregnancy and delivery experiences in a unique way to estimate population-level burdens of maternal morbidity and specific causes that compared well with clinical classifications of the same data. When applied to test datasets, the method overestimated the burden of morbidity compared with clinical review, although possible conceptual and methodological reasons for this were identified.
The probabilistic method shows promise and may offer opportunities for standardised measurement of maternal morbidity that allows for the uncertainty of women's self-reported symptoms in retrospective interviews. However, important discrepancies with clinical classifications were observed and the method requires further development, refinement and evaluation in a range of settings.
孕产妇发病比孕产妇死亡更为常见，基于人群的孕产妇发病负担估计可为监测趋势、确定优先事项以及评估干预措施的健康影响提供重要指标。已证明基于产科事件的外行报告方法缺乏特异性，因此需要新的方法来衡量孕产妇发病的人群负担。开发了一种基于计算机的概率工具，用于根据自我报告的症状以及妊娠/分娩经历来估计孕产妇发病的可能性及其原因。开发过程涉及使用来自贝宁和布基纳法索1734例机构分娩的发病体征、症状和原因的训练数据集，以及专家评审。该方法的初步评估将概率工具得出的孕产妇发病负担和具体原因与来自贝宁、孟加拉国和印度的489名近期分娩妇女的临床分类进行了比较。
利用训练数据集，可以创建一种概率工具，该工具以独特方式处理妇女对妊娠和分娩经历的自我报告的不确定性，以估计人群层面的孕产妇发病负担和具体原因，与相同数据的临床分类相比效果良好。当应用于测试数据集时，与临床评审相比，该方法高估了发病负担，不过已确定了可能的概念和方法学原因。
概率方法显示出前景，可能为孕产妇发病的标准化测量提供机会，这种测量考虑到回顾性访谈中妇女自我报告症状的不确定性。然而，观察到与临床分类存在重要差异，该方法需要在一系列环境中进一步开发、完善和评估。
