Abstract
We propose two new methods to estimate secular trends in the incidence of a chronic disease from a series of prevalence studies and mortality data. One method is a direct inversion formula, the second method is a least squares estimation. Both methods are validated in a simulation study based on data from a diabetes register. The results of the validation show that the proposed methods may be useful in epidemiological settings with sparse resources, where running a register or a series of follow-up studies is difficult or impossible.
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CITATION STYLE
Brinks, R., Hoyer, A., & Landwehr, S. (2016). Surveillance of the incidence of non-communicable diseases (NCDS) with sparse resources: A simulation study using data from a national diabetes registry, Denmark, 1995-2004. PLoS ONE, 11(3). https://doi.org/10.1371/journal.pone.0152046
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