Page 60 - DISINVESTMENT AND IMPLEMENTATION OF VISION SCREENING TESTS BASED ON THEIR EFFECTIVENESS
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CHAPTER 3
METHODS
We developed a micro-simulation model, programmed in MATLAB R2008b (Math- Works), to assess modification of the Dutch eye screening programme. Monte Carlo simulations with various incidence and sensitivity combinations were used to approximate the observational data. The model simulates the path followed by a subject during consecutive screens within the Dutch amblyopia screening programme, which consists of seven exams between age 0 and 60 months. VA is measured from 36 months onwards. A child can attend or not attend a screen test. If the child attends screening, the result can be positive or negative. Positive cases can comply with the referral for diagnostic follow-up. Screen positive cases who have the disease are the true positives (tp). The number of tp and the number of children with the disease not screen-detected at that time (i.e. false negatives (fn)), determine the sensitivity (tp/(tp + fn) of the test (Figure 1). For the approximation of the RAMSES data, input parameters were varied until the model predicted the detection of the cases of amblyopia actually detected by screening (i.e. not by the parents or others) at the corresponding screen for each of the four types of amblyopia. Thereafter a programme with one of the screens omitted was simulated to determine the relative effect on the detection of amblyopia.
Figure 1. Schematic representation of the path that an individual undergoing screening follows.
 Indicated are the data obtained from observational studies, the disease incidence and the sensitivity per screen within the path, which are used in the model. Here, n is the number of diseased population and nnon is the number of the healthy population. The fn, fp, tn and tp are the false negatives, false positives, true negatives and true positives, respectively. Here, a and b are the fractions of true positives and false positives, respectively, that comply with the referral.
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