No abstract

The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription.

No abstract

This body of evidence suggests that unconditional cash transfers (UCTs) may not impact a summary measure of health service use in children and adults in LMICs. However, UCTs probably or may improve some health outcomes (i.e. the likelihood of having had any illness, the likelihood of having been food secure, and the level of dietary diversity), one social determinant of health (i.e. the likelihood of attending school), and healthcare expenditure. The evidence on the relative effectiveness of UCTs and CCTs remains very uncertain.

Unconditional cash transfers for reducing poverty and vulnerabilities: effect on use of health services and health outcomes in low-and middle-income countries (Protocol)

About 3ie The International Initiative for Impact Evaluation (3ie) promotes evidence-informed, equitable, inclusive and sustainable development. We support the generation and effective use of high-quality evidence to inform decision-making and improve the lives of people living in poverty in low-and middle-income countries. We provide guidance and support to produce, synthesise and quality assure evidence of what works, for whom, how, why and at what cost. 3ie impact evaluations 3ie-supported impact evaluations assess the difference a development intervention has made to social and economic outcomes. 3ie is committed to funding rigorous evaluations that include a theory-based design and that use the most appropriate mix of methods to capture outcomes and are useful in complex development contexts. About this report 3ie accepted the final version of the report, Rebuilding the social compact: urban service delivery and property taxes in Pakistan, as partial fulfilment of requirements under grant DPW1.1005 awarded through Development Priorities Window 1. The report is technically sound and 3ie is making it available to the public in this final report version as it was received. No further work has been done. The 3ie technical quality assurance team for this report comprises Francis Rathinam, Neeta Goel, Kanika Jha Kingra and Deeksha Ahuja, an anonymous external impact evaluation design expert reviewer and an anonymous external sector expert reviewer, with overall technical supervision by Marie Gaarder. The 3ie editorial production team for this report comprises Anushruti Ganguly and Akarsh Gupta.

This paper investigates the relationship of features of in-vitro fertilization (IVF) embryos and the associated oocyte and follicle to the outcome of transfer. It differs from previous studies in including a range of features (n = 53) and in using class probability tree analysis. This is a non-parametric multivariate method which expresses relationships as simple rules of features characterizing the 'take home baby' and 'no take home baby' (negative pregnancy test) classes of embryo batches. Data were analysed retrospectively. Fifty-three (embryo, oocyte and follicular) features for each of the three embryos in the transferred batch were collected for 200 IVF patients. Each batch of three embryos was described by a representative value for each feature. The relationship between features and outcome of transfer was analysed. Only four of the 53 features were identified as predictive. However, an appropriate combination of these four (embryo grade, cell number, follicle size and follicular fluid volume) achieved satisfactory predictivities while offering a simplified and more quantitative basis than regularly used criteria. The key component of the composite embryo grading allotted by embryologists turned out to be cell number. The existing predictive use of follicular size was corroborated and an independent additional predictive contribution of follicular fluid volume was found. The study also suggests that the 49 remaining features have little additional predictive value.

No abstract