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<p>Log in</p><p>Search term</p><p>Go to:</p><p>Go to:</p><p>Go to:</p><p>Go to:</p><p>Go to:</p><p>1.</p><p>2.</p><p>3.</p><p>4.</p><p>5.</p><p>6.</p><p>Go to:</p><p>StatPearls [Internet].</p><p>Prevalence</p><p>Steven Tenny; Mary R. Hoffman.</p><p>Last Update: May 22, 2023.</p><p>Introduction</p><p>In medical epidemiology, prevalence is defined as the proportion of the population with a</p><p>condition at a specific point in time (point prevalence) or during a period of time (period</p><p>prevalence). [1] Prevalence increases when new disease cases are identified (incidence), and</p><p>prevalence decreases when a patient is either cured or dies. Many times, the period prevalence</p><p>will provide a more accurate picture of the overall prevalence since period prevalence includes</p><p>all individuals with the condition between two dates: old and new (incident) cases, as well as</p><p>those who were cured or died during the period. [2][3]</p><p>Clinically, prevalence is most commonly described as the percentage with the disease in the</p><p>population at risk. We commonly hear this in everyday discussion, and most find these</p><p>references intuitive to interpret, such as "currently, X% of Americans were overweight or</p><p>obese."</p><p>Function</p><p>Prevalence = (Total number with disease) / (Population at risk for the disease)[2][4]</p><p>Alternatively, if the disease process tends to last a long time and both the incidence and</p><p>cure/death rates are relatively stable then prevalence can be calculated based on the incidence</p><p>and duration of disease.</p><p>Prevalence = (Incidence) x (disease duration)</p><p>Let us use an example to examine and clarify prevalence.</p><p>Example 1</p><p>You talk to all 200 people in your town on a spring day and find 60 of them have allergy</p><p>symptoms. The point prevalence of allergies in your town would be 30% or 3 in 10 individuals</p><p>calculated as:</p><p>(60 people with allergy symptoms) / (200 people at risk) = 0.3 = 30%</p><p>You spend the next week interviewing all 1500 people in the next town over and find 300 people</p><p>report allergy symptoms. The period prevalence for the next town over is 20% for the week or 2</p><p>in 10 individuals calculated as:</p><p>(300 people with allergy symptoms during the week) / (1500 people at risk) = 0.2 = 20%</p><p>Example 2</p><p>As stated previously, we can use the incidence and disease duration to calculate prevalence if</p><p>both are relatively constant.</p><p>The annual incidence of the brain tumor glioblastoma is approximately 2.5 / 100,000 people</p><p>annually in the United States. The median survival at the time of diagnosis is approximately 15</p><p>months (1.25 years). What is the prevalence of glioblastoma?</p><p>Prevalence = (Incidence) x (disease duration)</p><p>Incidence = 2.5 new cases / 100,000 people annually</p><p>Disease duration = 1.25 years</p><p>Prevalence = (2.5 cases / 100,000 people annually) x (1.25 years) = 3.125 cases / 100,000</p><p>people</p><p>Thus approximately 3.125 people out of every 100,000 individuals in the United States currently</p><p>have a glioblastoma or approximately 10,125 people in the United States are currently living</p><p>with the diagnosis of a glioblastoma. This figure was calculated as:</p><p>(3.125 cases / 100,000 people) x (324,000,000 people in the United States) = 10,125 cases</p><p>of glioblastoma in the United States</p><p>It should be stressed that the second method is only valid if both the incidence and</p><p>surival/disease duration are relatively constant.</p><p>Issues of Concern</p><p>Prevalence is commonly confused with incidence. Incidence is the rate of new cases or events</p><p>during a specified time period for a population at risk whereas prevalence is the total cases</p><p>present at one specific time, both new and old cases. Incidence occurs when the new case is</p><p>diagnosed, and each new case diagnosed increases the prevalence. Prevalence decreases when</p><p>the disease is cured, or the patient dies. The cure for a disease or death of a patient does not</p><p>affect the incidence of the disease whereas it decreases the prevalence. In the image below</p><p>incidence is the new additions to the reservoir, prevalence is the total in the reservoir, and</p><p>cure/death decreases the reservoir. Stated another way, the incidence is a measure of the risk of</p><p>getting the disease during a specified time period whereas prevalence is a measure of how much</p><p>burden of the disease there is in the population at one specific moment in time.[5][6]</p><p>A second common error with prevalence is not correctly defining the population at risk. The</p><p>total number of people living with ovarian cancer is estimated to be 0.13% of females (222,060</p><p>women living with ovarian cancer in the United States / 170,000,000 females in the United</p><p>States). If one were to make a mistake and use the total population of the United States instead</p><p>of only females the prevalence would be incorrectly reported as 0.07%. This 0.07% prevalence</p><p>is not correct as only females are at risk, not all individuals.</p><p>Prevalence is also not static among different groups. We will take two different examples,</p><p>osteosarcoma and essential hypertension, and compare their prevalence stratified by age group.</p><p>Osteosarcoma tends to occur in the pediatric age group and has a second incidence peak in the</p><p>elderly. Osteosarcoma has approximately a 20% 10-year survival rate. Thus a pediatric patient</p><p>diagnosed with osteosarcoma will likely no longer be alive in 10 years. If there are few new</p><p>cases in teenagers and adults, the prevalence will decrease as more individuals leave the disease</p><p>group (by death or cure) than new cases are diagnosed. Later in life, there is the second</p><p>incidence peak where senior citizens start developing osteosarcoma at increasing rates. This will</p><p>add new cases to the osteosarcoma group, and thus the prevalence will increase in senior citizens</p><p>corresponding to this second peak in incidence.</p><p>Essential hypertension, on the other hand, has an average life expectancy of decades after</p><p>diagnosis. As people get older, more individuals are newly diagnosed with essential</p><p>hypertension (new incident cases), and thus the prevalence of essential hypertension increases</p><p>with increasing age as we add the new diagnoses of essential hypertension to all of the old cases</p><p>of essential hypertension still alive.</p><p>Clinical Significance</p><p>Prevalence is a measure of how common a disease process is found in a specified at-risk</p><p>population at a specific time point or during a specified time period. Prevalence is thus a</p><p>measure of disease burden for the specified population, or how commonly someone with the</p><p>disease will be encountered in the specified population.[2]</p><p>In biostatistics, prevalence could be considered similar to the pre-test probability. That is, before</p><p>any testing, the probability of a person in the specified population having the disease is the same</p><p>as the prevalence of the disease in the population. If the prevalence of a disease is 1% of the</p><p>population, then we would expect approximately 1 in 100 people to have the disease before any</p><p>testing.</p><p>Prevalence Impact on Positive Predictive Value (PPV) and Negative Predictive Value (NPV)</p><p>Prevalence thus impacts the positive predictive value (PPV) and negative predictive value</p><p>(NPV) of tests. As the prevalence increases, the PPV also increases but the NPV decreases.</p><p>Similarly, as the prevalence decreases the PPV decreases while the NPV increases.</p><p>For a mathematical explanation of this phenomenon, we can calculate the positive predictive</p><p>value (PPV) as follows:</p><p>PPV = (sensitivity x prevalence) / [ (sensitivity x prevalence) + ((1 – specificity) x (1 –</p><p>prevalence)) ]</p><p>If we hold all values except for the prevalence the same then as prevalence increases the</p><p>numerator will also increase for PPV. In the denominator note the last term of “1 – prevalence.”</p><p>Thus as prevalence increases towards 100% (a value of one) the term “1 – prevalence” goes</p><p>towards zero. This drives the second part of the denominator, “(1 – specificity) x (1 –</p><p>prevalence)”, to smaller and smaller values as prevalence increases. Thus at a very high</p><p>prevalence the value of “1 – prevalence” goes towards zero and the PPV equation reduces to:</p><p>PPV = (sensitivity x prevalence) / [ (sensitivity x prevalence ) + ((1 – specificity) x (0)) ]</p><p>=</p><p>PPV = (sensitivity x prevalence) / [ (sensitivity x prevalence) + (0) ] =</p><p>PPV = (sensitivity x prevalence) / (sensitivity x prevalence) = 1</p><p>Similarly we can write the negative predictive value (NPV) as follows:</p><p>NPV = (specificity x (1 – prevalence)) / [ (specificity x (1 – prevalence)) + ((1 –</p><p>sensitivity) x prevalence) ]</p><p>For the NPV as the prevalence increases (goes towards one) the term “1 – prevalence” becomes</p><p>smaller making the numerator smaller. In the denominator NPV has the same first term as the</p><p>numerator, “specificity x (1 – prevalence)” which will also become smaller as the prevalence</p><p>increases. The second term in the denominator, “(1 – sensitivity) x prevalence” will increase as</p><p>the prevalence increases. As the prevalence comes very close to 100% we can write NPV as:</p><p>NPV = (specificity x (1 – 1)) / [ (specificity x (1 – 1)) + ((1 – sensitivity) x 1) ] =</p><p>(specificity x 0) / [ (specificity x 0) + (1 – sensitivity) ] =</p><p>0 / (0 + (1 – sensitivity)) = 0</p><p>Review Questions</p><p>Access free multiple choice questions on this topic.</p><p>Comment on this article.</p><p>Figure</p><p>Incidence is the new additions to the reservoir; prevalence is</p><p>the total in the reservoir; and cure/death decrease the</p><p>reservoir. Contributed by Steven Tenny, MD, MPH, MBA</p><p>References</p><p>Kier KL. Biostatistical applications in epidemiology. Pharmacotherapy. 2011 Jan;31(1):9-22.</p><p>[PubMed]</p><p>Whiting PF, Davenport C, Jameson C, Burke M, Sterne JA, Hyde C, Ben-Shlomo Y. How</p><p>well do health professionals interpret diagnostic information? A systematic review. BMJ</p><p>Open. 2015 Jul 28;5(7):e008155. [PMC free article] [PubMed]</p><p>Parkin DM, Fernández LM. Use of statistics to assess the global burden of breast cancer.</p><p>Breast J. 2006 Jan-Feb;12 Suppl 1:S70-80. [PubMed]</p><p>McKenna SL, Dohoo IR. Using and interpreting diagnostic tests. Vet Clin North Am Food</p><p>Anim Pract. 2006 Mar;22(1):195-205. [PubMed]</p><p>Walker N, Bryce J, Black RE. Interpreting health statistics for policymaking: the story</p><p>behind the headlines. Lancet. 2007 Mar 17;369(9565):956-63. [PubMed]</p><p>Glatstein E, Makuch RW. Illusion and reality: practical pitfalls in interpreting clinical trials.</p><p>J Clin Oncol. 1984 May;2(5):488-97. [PubMed]</p><p>Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.</p><p>Disclosure: Mary Hoffman declares no relevant financial relationships with ineligible companies.</p><p>Copyright © 2023, StatPearls Publishing LLC.</p><p>This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International</p><p>(CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided</p><p>that the article is not altered or used commercially. 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