Article Evaluation

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Article Evaluation

Article Evaluation

Abstract

This article is designed to present the analysis of cost effectiveness for premature babies by digital photographic screening at the threat of Retinopathy of prematurity. Earlier economic studies considered the several treatments of cost effectiveness for ROP and the screening of cost effectiveness for this disease. This particular piece of writing evaluates the cost effectiveness of the screening of digital photographic which is equated with conventional IO. The article defines the tree analysis from U.K health's service perspective. The synchronization of screening and compliance with the guidelines was the serious concern in U.K in earlier decades. The department of health financed a 5 year program in the year of 1995 of researching into ROP treatment and screening in the U.K. this article evaluation defines the essence of the writing piece and the effectiveness of its authentic information.

Problem Statement or purpose

The purpose of the article is to measure up to the cost-effectiveness of substitution procedures of testing for ROP. It also includes the surviving method of the digital photographic screening by nurses and indirect ophithalmoscopy by ophthalmologists.

Literature

The study is related to the Retinopathy of prematurity which is one of the leading causes of blindness in babies born prematurely. Retinopathy of prematurity is the vasoproloferative disorder of the eye, which is connected to the administration of oxygen to preterm babies. The seriousness of ROP is distinguished by five phases of disease. The first treatment of ROP was Cryotherapy, which was the single available treatment of that particular time. The literature of the article is based on the comparison of alternative methods of cost-effectiveness for ROP “Retinopathy of Prematurity” in the U.K that also comprise on the subsisting means of screening of digital photographic by nurses and indirect ophthalmoscopy by ophthalmologists.

Methods

In this article, they used a decision tree model ...
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