Comparison of survival analysis approaches to modelling age at first sex among youth in Kisesa Tanzania.

Autor: Materu J; 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., Konje ET; Department of Biostatistics, Epidemiology and Behavioral Sciences, School of Public Health, Catholic University of Health, and Allied Sciences, Mwanza, Tanzania., Urassa M; Program of Sexual and Reproductive Health, National Institute for Medical Research, Mwanza Centre, Mwanza, Tanzania., Marston M; Department of Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom., Boerma T; Institute for Global Public Health, University of Manitoba, Manitoba, Canada., Todd J; Program of Sexual and Reproductive Health, National Institute for Medical Research, Mwanza Centre, Mwanza, Tanzania.; Department of Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Sep 07; Vol. 18 (9), pp. e0289942. Date of Electronic Publication: 2023 Sep 07 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0289942
Abstrakt: Background: 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.
Methods: 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.
Results: 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.
Conclusion: The AFT model is better than Cox PH model in estimating AFS among the young population.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Materu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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