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Abstract
Background
This project relates prevalence‐time data, incidence rate data, age of onset, system plateau level and system time constant, using exponential equations, as they apply to progressive myopia, useful over several decades.
Methods
Cross‐sectional refractive data is analysed for nine studies with a total number of subjects at 444.6-K (345, 981, 7.6-K, 39, 421-K, 383, 2-K, 12-K, 255), with ages ranging from five to 39 years. Basic exponential equations allow calculation of the prevalence versus time function Pr(t) as a percentage and the incidence rate function In(t) (percentage per year), system time constant t0 (years), onset age t1 (years) and saturation plateau level <S> (percentage).
Results
The prevalence of myopia as a function of time Pr(t) (years) and incidence of myopia as a function of time In(t) (percentage per year) are continuously generated and compared with prevalence/incidence data from various reports investigating student populations. For a general medical condition, typical values for time constant t0 may range from one week to five years, depending on the health condition. Typical plateau levels for myopia may range from 35 to 95 per cent. Herein, data from nine demographic studies of myopia are analysed for prevalence Pr(t) with an accuracy within 14 per cent and incidence In(t) within 2.6 per cent per year, onset t1 = 1.5 years, time constant t0 = 4.5 year. By comparison, linear regression can predict the prevalence of myopia Pr(t) within 11 per cent and estimates a constant incidence rate for myopia In(t) of 4.7 per cent per year (95 per cent CI: 2.1 to 7.3 per cent per year].
Conclusions
The initial incidence rate at onset age In(t1) and system time constant t0 are inversely related. For myopia, onset age, time constant and saturation plateau level are fundamental system parameters derived from age‐specific prevalence and incidence data.