Sentence examples for generate prevalence from inspiring English sources

The German Migraine and Headache Society DMKG Headache Studydy investigated the regional differences in headache prevalence in Germany and therefore could not generate prevalence data for the general population [4].

The study was neither designed nor powered to generate prevalence estimates for individual countries but, through modified cluster sampling, to produce estimates for the European Union.

Therefore carefully designed national surveys to cover randomly selected districts suspected of having trachoma in all the three regions should be planned and conducted to generate prevalence data at national level.

For these analyses we used Poisson regression working models to generate prevalence ratios, adjusted for household clustering.

Finally, information on the onset and the recent nature of the particular cluster of symptoms were assessed to generate prevalence data.

A Poisson regression model with a robust variance was used to generate prevalence rate difference and prevalence rate ratio values with 95% confidence intervals (95% CI).

During subsequent analysis, definite and probable migraine were combined, as were definite and probable TTH, for generating prevalence estimates for migraine and TTH.

Based on TRA results it should be possible to exclude areas of no or low endemicity from further surveys and to target population-based prevalence surveys (PBPS) to suspected highly endemic areas, generating prevalence estimates to determine the actual need for control activities and allow their subsequent monitoring and evaluation.

Nevertheless this instrument has the advantage of being straightforward and its ease of generating prevalence estimates [ 8].

Descriptive analysis of both the outcomes and the covariates across the 9 groups based on the IQ testing generated prevalence proportions.

For each survey we generated prevalence estimates of underweight (percent below -2 standard deviation (SD) weight-for-age), stunting (percent below -2 SD length/height-for-age), wasting (percent below -2 SD weight-for-length/height), and overweight (percent above +2 SD weight-for-length/height) based on the WHO standards (WHO estimates).