MObile Technology for Improved Family Planning: update to randomised controlled trial protocol.

BACKGROUND: This update outlines changes to the MObile Technology for Improved Family Planning study statistical analysis plan and plans for long-term follow-up. These changes result from obtaining additional funding and the decision to restrict the primary analysis to participants with available follow-up data. The changes were agreed prior to finalising the statistical analysis plan and sealing the dataset. METHODS/DESIGN: The primary analysis will now be restricted to subjects with data on the primary outcome at 4-month follow-up. The extreme-case scenario, where all those lost to follow-up are counted as non-adherent, will be used in a sensitivity analysis. In addition to the secondary outcomes outlined in the protocol, we will assess the effect of the intervention on long-acting contraception (implant, intra-uterine device and permanent methods).To assess the long-term effect of the intervention, we plan to conduct additional 12-month follow-up by telephone self-report for all the primary and secondary outcomes used at 4 months. All participants provided informed consent for this additional follow-up when recruited to the trial. Outcome measures and analysis at 12 months will be similar to those at the 4-month follow-up. The primary outcomes of the trial will be the use of an effective modern contraceptive method at 4 months and at 12 months post-abortion. Secondary outcomes will include long-acting contraception use, self-reported pregnancy, repeat abortion and contraception use over the 12-month post-abortion period. DISCUSSION: Restricting the primary analysis to those with follow-up data is the standard approach for trial analysis and will facilitate comparison with other trials of interventions designed to increase contraception uptake or use. Undertaking 12-month trial follow-up will allow us to evaluate the long-term effect of the intervention. TRIAL REGISTRATION: ClinicalTrials.gov NCT01823861

A Life Insurance Deterrent to Risky Behavior in Africa

The spread of HIV and AIDS and risky sexual behavior continues to be a problem in Sub-Saharan African countries despite government measures to educate people on the risk and severity of the disease and measures to promote safe sex practices such as making condoms readily available at reduced or no cost. We examine whether people decide to engage in risky sexual behavior due to low income and low life expectancy. Sub-Saharan Africa is characterized by conditions that significantly reduce life expectancy such as unsanitary conditions prevalent in poverty stricken areas, inaccessibility to health care, and dangerous working conditions such as those in very poor mining regions. Moreover, since income per capita in these countries is very low, the opportunity cost associated with dying from AIDS and foregoing future consumption is very low. We examine how a government provided life insurance benefit may be an effective means of deterring risky sexual behavior. To evaluate this policy prescription we develop a life-cycle model with personal and family consumption and endogenous probability of survival. In the model, agents can receive life insurance benefits if their death is not the result of AIDS. We demonstrate that excessive risky behavior does result from low life expectancy and low levels of income and illustrate the conditions for which the life insurance benefit can replicate the effects of higher income and life expectancy, deterring risky sexual behavior and reducing the spread of HIV/AIDS.AIDS; life-cycle; life expectancy; sub-Saharan Africa

Statistical models of economic burden : a case study in medicine

Purpose: The main aim of this article is to use statistical methods for the estimation of the economic burden and the survival rate of deeply premature babies. Design/Methodology/Approach: The results of a survey of 2.222 children with a birth weight of 501-1500 grams and a gestational age of 23-37 weeks were used as input data. Cox’s proportional hazards model was used as a survival tool. Findings: The results of Cox survival regression model showed a series of statistically significant predictors of survivability (p<0.05) for three age cohorts: neonatal, postnatal and pediatric (until 2 years). One of the statistically significantly predictors of survivability of premature infants with very low birth weight (VLBW) and extremely low birth weight (ELBW) in every age cohort is the volume of primary resuscitation measure and the length of stay in the neonatal pathology unit (NPU). Practical Implications: The results permitted to assess the amount of nursing care measures, the duration of care in a neonatal pathology unit, the rehabilitation of children with VLBW and ELBW in the long run. The assessment will ultimately help to estimate the overall economic burden associated with maintaining health and quality of life of premature babies. Originality/Value: The scientific contribution of the study consists in the use of an integrated approach to the problem of estimating the economic burden of nursing very premature babies, taking into account their survival and subsequent disability risks in the neonatal, postnatal, and pediatric periods.peer-reviewe

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival