Sentence examples for estimation of incidence from inspiring English sources

However, it suggests some consistency between our estimation of incidence and previous prevalence estimates.

Our aims for this paper were to improve the methods for indirect estimation of incidence and to apply those methods to estimate cancer incidence in China using updated national mortality data and regional cancer registries data.

The estimation of incidence was done using a single Maximum-Likelihood (ML) estimator based on data from both the registry and the sample.

The BED IgG Capture enzyme immunoassay (CEIA) was applied on the HIV-seropositive samples to detect recent HIV-1 infection for the estimation of incidence among those with HIV infection.

To address this, we made the adjustment and the estimation of incidence under a range of assumptions to quantify the uncertainty due to this approach (indicated in the error bars and uncertainty intervals in Figure 3).

Although incidence has previously been estimated from serial prevalence data and survival assumptions, back-calculation from AIDS case reporting, self-reported serologic history, or passive anonymous/linked surveys [5] [7], [9] [11], the accurate estimation of incidence has traditionally relied on prospective HIV-1 testing and longitudinal follow-up of people at risk [12] [15].

As expected, the mode of data documentation had some impact on the estimation of incidences.

The partial prevalence was estimated using a classical approach; i.e., annual estimations of incidence by age and observed survival probabilities [ 6].

This study may have resulted in either an over- or an under-estimation of incidence and prevalence rates.

This will have tended to result in a general under-estimation of incidence rates which in turn would lead to an under-estimation in individualised risk estimates.

Population-based database studies, where suitable sources of information exist, are likely to be the most economical option for studying the condition; however, there is likely to be a degree of inclusion of false positive cases, over-estimation of incidence and thus non-uniformity of the cases examined.