Handan Wand1, Tarylee Reddy2, Reshmi Dassaye3, Jothi Moodley3, Sarita Naidoo3, Gita Ramjee4. 1. Kirby Institute, University of New South Wales, Kensington 2052, New South Wales, Australia. Electronic address: hwand@kirby.unsw.edu.au. 2. Biostatistics Unit, South African Medical Research Council, Durban, Kwazulu-Natal, South Africa. Electronic address: Tarylee.Reddy@mrc.ac.za. 3. South African Medical Research Council, HIV Prevention Research Unit, Durban, Kwazulu-Natal, South Africa. 4. South African Medical Research Council, HIV Prevention Research Unit, Durban, Kwazulu-Natal, South Africa; Aurum Global Department: Health Research, Durban, Kwazulu-Natal, South Africa.
Abstract
OBJECTIVE: Despite all efforts, high pregnancy rates are often reported in HIV biomedical intervention trials conducted in African countries. We therefore aimed to develop a pregnancy risk scoring algorithm for targeted recruitment and screening strategies among a cohort of women in South Africa. METHODS: The study population was ~ 10,000 women who enrolled in one of the six biomedical intervention trials conducted in KwaZulu Natal, South Africa. Cox regression models were used to create a pregnancy risk scoring algorithm which was internally validated using standard statistical measures. RESULTS: Five factors were identified as significant predictors of pregnancy incidence:<25 years old, not using injectable contraceptives, parity (<3), being single/not cohabiting and having ≥ 2 sexual partners in the past three months. Women with total scores of 21-24, 25-35 and 36+ were classified as being at "moderate", "high", "severe" risk of pregnancy. Sensitivity of the development and validation models were reasonably high (sensitivity 76% and 74% respectively). CONCLUSION: Our risk scoring algorithm can identify and alert researchers to women who need additional non-routine pregnancy assessment and counselling, with statistically acceptable accuracy and robustness.
OBJECTIVE: Despite all efforts, high pregnancy rates are often reported in HIV biomedical intervention trials conducted in African countries. We therefore aimed to develop a pregnancy risk scoring algorithm for targeted recruitment and screening strategies among a cohort of women in South Africa. METHODS: The study population was ~ 10,000 women who enrolled in one of the six biomedical intervention trials conducted in KwaZulu Natal, South Africa. Cox regression models were used to create a pregnancy risk scoring algorithm which was internally validated using standard statistical measures. RESULTS: Five factors were identified as significant predictors of pregnancy incidence:<25 years old, not using injectable contraceptives, parity (<3), being single/not cohabiting and having ≥ 2 sexual partners in the past three months. Women with total scores of 21-24, 25-35 and 36+ were classified as being at "moderate", "high", "severe" risk of pregnancy. Sensitivity of the development and validation models were reasonably high (sensitivity 76% and 74% respectively). CONCLUSION: Our risk scoring algorithm can identify and alert researchers to women who need additional non-routine pregnancy assessment and counselling, with statistically acceptable accuracy and robustness.
Authors: Stewart E Reid; James Y Dai; Jing Wang; Bupe N Sichalwe; Godspower Akpomiemie; Frances M Cowan; Sinead Delany-Moretlwe; Jared M Baeten; James P Hughes; Anna Wald; Connie Celum Journal: J Acquir Immune Defic Syndr Date: 2010-04 Impact factor: 3.731
Authors: Nelly R Mugo; Ting Hong; Connie Celum; Deborah Donnell; Elizabeth A Bukusi; Grace John-Stewart; Jonathan Wangisi; Edwin Were; Renee Heffron; Lynn T Matthews; Susan Morrison; Kenneth Ngure; Jared M Baeten Journal: JAMA Date: 2014 Jul 23-30 Impact factor: 56.272
Authors: Nancy S Padian; Ariane van der Straten; Gita Ramjee; Tsungai Chipato; Guy de Bruyn; Kelly Blanchard; Stephen Shiboski; Elizabeth T Montgomery; Heidi Fancher; Helen Cheng; Michael Rosenblum; Mark van der Laan; Nicholas Jewell; James McIntyre Journal: Lancet Date: 2007-07-21 Impact factor: 79.321
Authors: Robert Pool; Catherine M Montgomery; Neetha S Morar; Oliver Mweemba; Agnes Ssali; Mitzy Gafos; Shelley Lees; Jonathan Stadler; Angela Crook; Andrew Nunn; Richard Hayes; Sheena McCormack Journal: PLoS One Date: 2010-07-21 Impact factor: 3.240
Authors: Frank Tanser; Victoria Hosegood; Till Bärnighausen; Kobus Herbst; Makandwe Nyirenda; William Muhwava; Colin Newell; Johannes Viljoen; Tinofa Mutevedzi; Marie-Louise Newell Journal: Int J Epidemiol Date: 2007-11-12 Impact factor: 7.196