Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Department of Statistics and Population Studies, University of the Western Cape, Cape Town, South Africa.
Popul Health Metr. 2020 Oct 19;18(1):28. doi: 10.1186/s12963-020-00235-y.
Monitoring abortion rates is highly relevant for demographic and public health considerations, yet its reliable estimation is fraught with uncertainty due to lack of complete national health facility service statistics and bias in self-reported survey data. In this study, we aim to test the confidante methodology for estimating abortion incidence rates in Nigeria, Cote d'Ivoire, and Rajasthan, India, and develop methods to adjust for violations of assumptions.
In population-based surveys in each setting, female respondents of reproductive age reported separately on their two closest confidantes' experience with abortion, in addition to reporting about their own experiences. We used descriptive analyses and design-based F tests to test for violations of method assumptions. Using post hoc analytical techniques, we corrected for biases in the confidante sample to improve the validity and precision of the abortion incidence estimates produced from these data.
Results indicate incomplete transmission of confidante abortion knowledge, a biased confidante sample, but reduced social desirability bias when reporting on confidantes' abortion incidences once adjust for assumption violations. The extent to which the assumptions were met differed across the three contexts. The respondent 1-year pregnancy removal rate was 18.7 (95% confidence interval (CI) 14.9-22.5) abortions per 1000 women of reproductive age in Nigeria, 18.8 (95% CI 11.8-25.8) in Cote d'Ivoire, and 7.0 (95% CI 4.6-9.5) in India. The 1-year adjusted abortion incidence rates for the first confidantes were 35.1 (95% CI 31.1-39.1) in Nigeria, 31.5 (95% CI 24.8-38.1) in Cote d'Ivoire, and 15.2 (95% CI 6.1-24.4) in Rajasthan, India. Confidante two's rates were closer to confidante one incidences than respondent incidences. The adjusted confidante one and two incidence estimates were significantly higher than respondent incidences in all three countries.
Findings suggest that the confidante approach may present an opportunity to address some abortion-related data deficiencies but require modeling approaches to correct for biases due to violations of social network-based method assumptions. The performance of these methodologies varied based on geographical and social context, indicating that performance may be better in settings where abortion is legally and socially restricted.
监测堕胎率对于人口和公共卫生考虑至关重要，但由于缺乏完整的国家卫生机构服务统计数据以及自我报告调查数据存在偏差，其可靠估计充满不确定性。在这项研究中，我们旨在测试在尼日利亚、科特迪瓦和印度拉贾斯坦邦估计堕胎发生率的知己方法，并制定方法来调整违反假设的情况。
在每个地点的基于人群的调查中，育龄女性受访者分别报告她们最近的两个知己的堕胎经历，以及她们自己的经历。我们使用描述性分析和基于设计的 F 检验来检验方法假设的违反情况。使用事后分析技术，我们纠正知己样本中的偏差，以提高从这些数据中得出的堕胎发生率估计的有效性和精度。
结果表明，知己堕胎知识的传递不完全，知己样本存在偏差，但在调整违反假设后报告知己堕胎发生率时，社会期望偏差会降低。违反假设的程度因三个背景而异。尼日利亚育龄妇女的 1 年妊娠去除率为每 1000 名妇女 18.7（95%置信区间（CI）14.9-22.5）例堕胎，科特迪瓦为 18.8（95%CI 11.8-25.8）例，印度为 7.0（95%CI 4.6-9.5）例。第一位知己的 1 年调整堕胎发生率在尼日利亚为 35.1（95%CI 31.1-39.1），在科特迪瓦为 31.5（95%CI 24.8-38.1），在印度拉贾斯坦邦为 15.2（95%CI 6.1-24.4）。第二位知己的比率更接近第一位知己的发生率，而不是受访者的发生率。在所有三个国家，调整后的知己一和二的发生率估计明显高于受访者的发生率。
研究结果表明，知己方法可能为解决一些与堕胎相关的数据不足提供了机会，但需要建模方法来纠正由于违反基于社会网络的方法假设而产生的偏差。这些方法的性能因地理位置和社会背景而异，这表明在堕胎在法律和社会上受到限制的情况下，这些方法的性能可能会更好。
