Statistics

Analysis of the article by Nuijten on the CostEffectiveness of a Specific Treatment for Children

The second part considers globally accepted methods of health economics strategies that provide alternatives in analyzing and evaluating similar problems and concludes with a proposal that could be considered an alternative approach to the modeling, analysis, and presentation of the results appropriate for this case.

Nuijten et al. (2007) studied the cost-effectiveness of using Palivizumab as a preventative treatment against the severe respiratory syncytial virus (RSV) in children with bronchopulmonary dysplasia (BPD) and congenital heart disease (CHD).

Given the medical research finding that Palivizumab could reduce the incidence of future hospitalization of children with BPD and CHD, the research question that needed answering was whether it was more economical for treatment to be administered to children at risk of RSV infection at an earlier age, instead of withholding treatment until some 20% of children get infected at a later age, eventually ending up in the hospital because of severe infection, and some of whom do not survive.

The study, therefore, aimed to determine whether an ounce of prevention is, indeed, more economical than a pound of cure. Using a decision tree model, the authors adopted the perspective of the National Health Service (NHS) of the UK, used data from published literature, Palivizumab clinical trials, and official UK price/tariffs lists, and availed of national population statistics to develop an econometric model to investigate the issue.

The authors also adopted a societal perspective scenario analysis taking into account the lost productivity resulting from RSV-related mortality, administrative costs of the treatment, hospital care for RSV infections, and the cost of asthma treatment.

The model used was a decision tree, a series of nodes each with two branches and extending out to six levels (see Figure 1 in Nuijten et al., 2007, p. 57). The costs and benefits at each branch and level are calculated based on probabilities and data gathered from clinical trials.

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