The phrase "adjust the prevalence" is correct and usable in written English.
It can be used in contexts where you want to change or modify the frequency or occurrence of something, often in research or statistical discussions.
Example: "To better understand the impact of the intervention, we need to adjust the prevalence of the condition in our study population."
Alternatives: "modify the frequency" or "alter the occurrence".
Furthermore, unlike the individual-level analysis that is adopted in most DHS to adjust the prevalence estimates for test refusal which require predicting the probability of an individual non-tester being infected based on his/her peculiar characteristics, the method we propose here is an aggregate model which does not suffer from the limitations of individual-level analyses.
Information from the Republic of Turkey, Ministry of Interior Affairs, Office of Population and Citizenship 2003 household survey was used to adjust the prevalence rates by the direct method [ 30].
12 Data permitting, we will attempt to adjust the prevalence estimates from auscultation-based studies using a cross-walking procedure and report these effects on the pooled prevalence estimates separately.
Therefore, no attempt at adjusting the prevalence estimates based on the risk of bias judgment was made.
Both of these methods (0.50 method and PABAK) are ways of adjusting the prevalence, which can be an advantage when studying a condition found in only a small proportion of the population.
The standardisation adjusted the prevalence of risk factors downwards (if the risk factor was more common in upper socioeconomic groups) or upwards (if the risk factor was more prevalent in the lower socioeconomic groups), suggesting that our population was relatively more affluent.
The estimated prevalence among those who refused testing is then used to adjust the overall prevalence in the total population in the area.
Another major limitation of this analysis was the use of information on 29% of deaths (and accounting for 36% of the exposure) with known HIV status to adjust the HIV prevalence in the nearly two-thirds of population with unknown HIV status.
Therefore, correction of inaccurate norms is one important component of these programmes to adjust the often overestimated prevalence of smoking in adolescence towards more conservative and realistic norms.
In this way, we can compare prevalence estimates using different algorithms by adjusting the observed prevalence appropriately.
We first linearly adjusted the predicted prevalence in the monograph, to account for the lower actual prevalence from 1980 to 1990.
