# Prevalence and incidence relationship help

### Incidence and Prevalence | AREP

In disease epidemiology, two terms, Incidence and Prevalence, are very much and quite often used. What is (are) the main flu in my village). Hope this helps!. e) The relationships between incidence and prevalence The current section introduces the commonly used measures that help our understanding of the. (1)Department of Zoology, Spatial Ecology and Epidemiology Group, prevalence with mathematical relationships to predict the incidence rate of Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't.

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It is more meaningful when the incidence rate is reported as a fraction of the population at risk of developing the disease e. Obviously, the accuracy of incidence data depends upon the accuracy of diagnosis and reporting of the disease. In some cases including ESRD it may be more appropriate to report the rate of treatment of new cases since these are known, whereas the actual incidence of untreated cases is not. Incidence rates can be further categorized according to different subsets of the population — e.

Prevalence Prevalence is the actual number of cases alive, with the disease either during a period of time period prevalence or at a particular date in time point prevalence. Period prevalence provides the better measure of the disease load since it includes all new cases and all deaths between two dates, whereas point prevalence only counts those alive on a particular date.

Prevalence is also most meaningfully reported as the number of cases as a fraction of the total population at risk and can be further categorized according to different subsets of the population. So, on average, they developed illness halfway through the year. The denominator of the person-time rate is the sum of all of the person-years for each study participant. So, someone lost to follow-up in year 3, and someone diagnosed with the disease in year 3, each contributes 2.

Properties and uses of incidence rates An incidence rate describes how quickly disease occurs in a population. It is based on person-time, so it has some advantages over an incidence proportion.

Because person-time is calculated for each subject, it can accommodate persons coming into and leaving the study. As noted in the previous example, the denominator accounts for study participants who are lost to follow-up or who die during the study period.

### Principles of Epidemiology | Lesson 3 - Section 2

In addition, it allows enrollees to enter the study at different times. Person-time has one important drawback. Person-time assumes that the probability of disease during the study period is constant, so that 10 persons followed for one year equals one person followed for 10 years.

Because the risk of many chronic diseases increases with age, this assumption is often not valid. Long-term cohort studies of the type described here are not very common.

## Incidence and Prevalence

However, epidemiologists far more commonly calculate incidence rates based on a numerator of cases observed or reported, and a denominator based on the mid-year population. This type of incident rate turns out to be comparable to a person-time rate.

Finally, if you report the incidence rate of, say, the heart disease study as 2. Person-time is epidemiologic jargon. To convert this jargon to something understandable, simply replace "person-years" with "persons per year. It also conveys the sense of the incidence rate as a dynamic process, the speed at which new cases of disease occur in the population.

Calculating Incidence Rates Example A: Investigators enrolled 2, women in a study and followed them annually for four years to determine the incidence rate of heart disease. After one year, none had a new diagnosis of heart disease, but had been lost to follow-up. After two years, one had a new diagnosis of heart disease, and another 99 had been lost to follow-up. In response, cartographic approaches have been developed that use maps of infection prevalence termed the P.

While maps of PfPR are becoming increasingly robust, in part because of the proliferation of high-quality data on infection prevalence from nation-wide household surveys, the relationship between PfPR and clinical incidence remains relatively poorly understood and informed by a much smaller and less standardized empirical evidence base.

Recent efforts to construct a suitable PfPR—incidence relationship for P.

Over the past decade, a number of sophisticated microsimulation models have been developed that aim to capture all important components of the malaria transmission system, providing a platform to investigate many aspects on the basic epidemiology of the disease and the likely effect of different control strategies 89 Such models simulate infections at the level of distinct individuals within a population, each having experienced a unique history of past exposure and treatment 1112and therefore allow inference of the community-level PfPR—incidence relationship.

However, conflicts in their predictions arising from differences in the conceptual structures of these models cannot yet be distinguished from those simply because of differences in the data sets used in their calibration, nor indeed from any potential spatiotemporal or ethnic heterogeneity in the underlying relationship.

Hence, no consensus yet exists on an appropriate form of the PfPR—incidence curve for use in disease-burden estimation and for addressing other important public-health questions. The unique potential of microsimulation models for performing detailed epidemiological modelling under realistic conditions 13 comes at the price of a much greater computational demand than for steady-state models.

As a result, the calibration of microsimulation models against empirical data sets has proven a persistent difficulty for applications of these methods across the health sciences 14and in particular for malariology 15 To overcome this challenge in the present study we introduce a novel model-emulation procedure on the basis of the technique of functional regression 1718 —in which kernel-weighting methods are used to generate a map from the input space of entomological inoculation rate EIR seasonality profile plus model parameter vector to the output space of age-incidence curve plus age-PfPR curve on the basis of a pre-compiled library of noisy, small runtime simulation outputs.