Program of Sexual and Reproductive Health, National Institute for Medical Research, Mwanza Centre, Mwanza, Tanzania.
Department of Biostatistics, Epidemiology and Behavioral Sciences, School of Public Health, Catholic University of Health, and Allied Sciences, Mwanza, Tanzania.
PLoS One. 2023 Sep 7;18(9):e0289942. doi: 10.1371/journal.pone.0289942. eCollection 2023.
Many studies analyze sexual and reproductive event data using descriptive life tables. Survival analysis has better power to estimate factors associated with age at first sex (AFS), but proportional hazards models may not be right model to use. This study used accelerated failure time (AFT) models, restricted Mean Survival time model (RMST) models, with semi and non-parametric methods to assess age at first sex (AFS), factors associated with AFS, and verify underlying assumptions for each analysis.
Self-reported sexual debut data was used from respondents 15-24 years in eight cross-sectional surveys between 1994-2016, and from adolescents' survey in an observational community study (2019-2020) in northwest Tanzania. Median AFS was estimated in each survey using non-parametric and parametric models. Cox regression, AFT parametric models (exponential, gamma, generalized gamma, Gompertz, Weibull, log-normal and log-logistic), and RMST were used to estimate and identify factors associated with AFS. The models were compared using Akaike information criterion (AIC) and Bayesian information criterion (BIC), where lower values represent a better model fit.
The results showed that in every survey, the Cox regression model had higher AIC and BIC compared to the other models. Overall, AFT had the best fit in every survey round. The estimated median AFS using the parametric and non-parametric methods were close. In the adolescent survey, log-logistic AFT showed that females and those attending secondary and higher education level had a longer time to first sex (Time ratio (TR) = 1.03; 95% CI: 1.01-1.06, TR = 1.05; 95% CI: 1.02-1.08, respectively) compared to males and those who reported not being in school. Cell phone ownership (TR = 0.94, 95% CI: 0.91-0.96), alcohol consumption (TR = 0.88; 95% CI: 0.84-0.93), and employed adolescents (TR = 0.95, 95% CI: 0.92-0.98) shortened time to first sex.
The AFT model is better than Cox PH model in estimating AFS among the young population.
许多研究使用描述性生命表分析性和生殖事件数据。生存分析在估计与首次性行为年龄（AFS）相关的因素方面具有更好的能力，但比例风险模型可能不是正确的模型。本研究使用加速失效时间（AFT）模型、限制平均生存时间模型（RMST）模型、半参数和非参数方法评估首次性行为年龄（AFS）、与 AFS 相关的因素，并验证每种分析的潜在假设。
本研究使用了来自坦桑尼亚西北部 1994 年至 2016 年 8 项横断面调查中 15-24 岁的受访者以及 2019 年至 2020 年观察性社区研究中青少年调查的数据，来估计与首次性行为年龄（AFS）相关的因素。在每次调查中，均使用非参数和参数模型估计中位数 AFS。Cox 回归、AFT 参数模型（指数、伽马、广义伽马、戈珀兹、威布尔、对数正态和对数逻辑）和 RMST 用于估计和识别与 AFS 相关的因素。使用赤池信息量准则（AIC）和贝叶斯信息量准则（BIC）比较模型，其中较小的值表示更好的模型拟合度。
结果表明，在每个调查中，Cox 回归模型的 AIC 和 BIC 均高于其他模型。总体而言，在每个调查轮次中，AFT 均具有最佳的拟合度。使用参数和非参数方法估计的中位数 AFS 相近。在青少年调查中，log-logistic AFT 显示女性和接受中等及以上教育程度的青少年首次性行为时间更长（时间比（TR）=1.03；95%CI：1.01-1.06，TR=1.05；95%CI：1.02-1.08），而男性和未上学的青少年首次性行为时间更短。手机拥有率（TR=0.94，95%CI：0.91-0.96）、饮酒（TR=0.88；95%CI：0.84-0.93）和就业青少年（TR=0.95，95%CI：0.92-0.98）会缩短首次性行为的时间。
在估计年轻人群的 AFS 方面，AFT 模型优于 Cox PH 模型。
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