In epidemiology, prevalence is the proportion of a particular population found to be affected by a medical condition (typically a disease or a risk factor such as smoking or seatbelt use) at a specific time. [1] It is derived by comparing the number of people found to have the condition with the total number of people studied and is usually expressed as a fraction, a percentage, or the number of cases per 10,000 or 100,000 people. Prevalence is most often used in questionnaire studies.
Prevalence is the number of disease cases present in a particular population at a given time, whereas incidence is the number of new cases that develop during a specified time period. [2] Prevalence answers "How many people have this disease right now?" or "How many people have had this disease during this time period?". Incidence answers "How many people acquired the disease [during a specified time period]?". However, mathematically, prevalence is proportional to the product of the incidence and the average duration of the disease. In particular, when the prevalence is low (<10%), the relationship can be expressed as: [3]
Caution must be practiced as this relationship is only applicable when the following two conditions are met: 1) prevalence is low and 2) the duration is constant (or an average can be taken). [3] A general formulation requires differential equations. [4]
In science, prevalence describes a proportion (typically expressed as a percentage). For example, the prevalence of obesity among American adults in 2001 was estimated by the U. S. Centers for Disease Control (CDC) at approximately 20.9%. [5]
Prevalence is a term that means being widespread and it is distinct from incidence. Prevalence is a measurement of all individuals affected by the disease at a particular time, whereas incidence is a measurement of the number of new individuals who contract a disease during a particular period of time. Prevalence is a useful parameter when talking about long-lasting diseases, such as HIV, but incidence is more useful when talking about diseases of short duration, such as chickenpox. [ citation needed ]
Lifetime prevalence (LTP) is the proportion of individuals in a population that at some point in their life (up to the time of assessment) have experienced a "case", e.g., a disease; a traumatic event; or a behavior, such as committing a crime. Often, a 12-month prevalence (or some other type of "period prevalence") is provided in conjunction with lifetime prevalence. Point prevalence is the prevalence of disorder at a specific point in time (a month or less). Lifetime morbid risk is "the proportion of a population that might become afflicted with a given disease at any point in their lifetime." [6] [7]
Period prevalence is the proportion of the population with a given disease or condition over a specific period of time. It could describe how many people in a population had a cold over the cold season in 2006, for example.[ citation needed ] It is expressed as a percentage of the population and can be described by the following formula:
Period prevalence (proportion) = Number of cases that existed in a given period ÷ Number of people in the population during this period[ citation needed ]
The relationship between incidence (rate), point prevalence (ratio) and period prevalence (ratio) is easily explained via an analogy with photography. Point prevalence is akin to a flashlit photograph: what is happening at this instant frozen in time. Period prevalence is analogous to a long exposure (seconds, rather than an instant) photograph: the number of events recorded in the photo whilst the camera shutter was open. In a movie each frame records an instant (point prevalence); by looking from frame to frame one notices new events (incident events) and can relate the number of such events to a period (number of frames); see incidence rate.[ citation needed ]
Point prevalence is a measure of the proportion of people in a population who have a disease or condition at a particular time, such as a particular date. It is like a snapshot of the disease in time. It can be used for statistics on the occurrence of chronic diseases. This is in contrast to period prevalence which is a measure of the proportion of people in a population who have a disease or condition over a specific period of time, say a season, or a year. Point prevalence can be described by the formula: Prevalence = Number of existing cases on a specific date ÷ Number of people in the population on this date [8]
It can be said that a very small error applied over a very large number of individuals (that is, those who are not affected by the condition in the general population during their lifetime; for example, over 95%) produces a relevant, non-negligible number of subjects who are incorrectly classified as having the condition or any other condition which is the object of a survey study: these subjects are the so-called false positives; such reasoning applies to the 'false positive' but not the 'false negative' problem where we have an error applied over a relatively very small number of individuals to begin with (that is, those who are affected by the condition in the general population; for example, less than 5%). Hence, a very high percentage of subjects who seem to have a history of a disorder at interview are false positives for such a medical condition and apparently never developed a fully clinical syndrome.[ citation needed ]
A different but related problem in evaluating the public health significance of psychiatric conditions has been highlighted by Robert Spitzer of Columbia University: fulfillment of diagnostic criteria and the resulting diagnosis do not necessarily imply need for treatment. [9]
A well-known statistical problem arises when ascertaining rates for disorders and conditions with a relatively low population prevalence or base rate. Even assuming that lay interview diagnoses are highly accurate in terms of sensitivity and specificity and their corresponding area under the ROC curve (that is, AUC, or area under the receiver operating characteristic curve), a condition with a relatively low prevalence or base-rate is bound to yield high false positive rates, which exceed false negative rates; in such a circumstance a limited positive predictive value, PPV, yields high false positive rates even in presence of a specificity which is very close to 100%. [10]
A disease is a particular abnormal condition that adversely affects the structure or function of all or part of an organism and is not immediately due to any external injury. Diseases are often known to be medical conditions that are associated with specific signs and symptoms. A disease may be caused by external factors such as pathogens or by internal dysfunctions. For example, internal dysfunctions of the immune system can produce a variety of different diseases, including various forms of immunodeficiency, hypersensitivity, allergies, and autoimmune disorders.
Epidemiology is the study and analysis of the distribution, patterns and determinants of health and disease conditions in a defined population.
In epidemiology, incidence is a measure of the probability of occurrence of a given medical condition in a population within a specified period of time. Although sometimes loosely expressed simply as the number of new cases during some time period, it is better expressed as a proportion or a rate with a denominator.
The science of epidemiology has matured significantly from the times of Hippocrates, Semmelweis and John Snow. The techniques for gathering and analyzing epidemiological data vary depending on the type of disease being monitored but each study will have overarching similarities.
Binge eating disorder (BED) is an eating disorder characterized by frequent and recurrent binge eating episodes with associated negative psychological and social problems, but without the compensatory behaviors common to bulimia nervosa, OSFED, or the binge-purge subtype of anorexia nervosa.
In healthcare, a differential diagnosis (DDx) is a method of analysis of a patient's history and physical examination to arrive at the correct diagnosis. It involves distinguishing a particular disease or condition from others that present with similar clinical features. Differential diagnostic procedures are used by clinicians to diagnose the specific disease in a patient, or, at least, to consider any imminently life-threatening conditions. Often, each individual option of a possible disease is called a differential diagnosis.
The positive and negative predictive values are the proportions of positive and negative results in statistics and diagnostic tests that are true positive and true negative results, respectively. The PPV and NPV describe the performance of a diagnostic test or other statistical measure. A high result can be interpreted as indicating the accuracy of such a statistic. The PPV and NPV are not intrinsic to the test ; they depend also on the prevalence. Both PPV and NPV can be derived using Bayes' theorem.
In epidemiology, case fatality rate (CFR) – or sometimes more accurately case-fatality risk – is the proportion of people who have been diagnosed with a certain disease and end up dying of it. Unlike a disease's mortality rate, the CFR does not take into account the time period between disease onset and death. A CFR is generally expressed as a percentage. It is a measure of disease lethality, and thus may change with different treatments. CFRs are most often used for with discrete, limited-time courses, such as acute infections.
The epidemiology of autism is the study of the incidence and distribution of autism spectrum disorders (ASD). A 2022 systematic review of global prevalence of autism spectrum disorders found a median prevalence of 1% in children in studies published from 2012 to 2021, with a trend of increasing prevalence over time. However, the study's 1% figure may reflect an underestimate of prevalence in low- and middle-income countries.
In medicine and statistics, sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a medical condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true positives and specificity is a measure of how well a test can identify true negatives:
Life expectancy has been rising rapidly and South Korea ranked 3rd in the world for life expectancy. South Korea has among the lowest HIV/AIDS adult prevalence rate in the world, with just 0.1% of the population being infected, significantly lower than the U.S. at 0.6%, France's 0.4%, and the UK's 0.3% prevalence rate. South Korea has a good influenza vaccination rate, with a total of 43.5% of the population being vaccinated in 2019. A new measure of expected human capital calculated for 195 countries from 1920 to 2016 and defined for each birth cohort as the expected years lived from age 20 to 64 years and adjusted for educational attainment, learning or education quality, and functional health status was published by the Lancet in September 2018. South Korea had the sixth highest level of expected human capital with 26 health, education, and learning-adjusted expected years lived between age 20 and 64 years.
The prevalence of mental disorders has been studied around the world, providing estimates on how common mental disorders are. Different criteria or thresholds of severity have sometimes been used.
In epidemiology, data or facts about a population are called denominator data. Denominator data are independent of any specific disease or condition. This name is given because in mathematical models of disease, disease-specific data such as the incidence of disease in a population, the susceptibility of the population to a specific condition, the disease resistance, etc. disease-specific variables are expressed as their proportion of some attribute of the general population, and hence appear as the numerator of the fraction or percentage being calculated, general data about the population typically appearing in the denominator; hence the term "denominator data."
Population impact measures (PIMs) are biostatistical measures of risk and benefit used in epidemiological and public health research. They are used to describe the impact of health risks and benefits in a population, to inform health policy.
Pre-test probability and post-test probability are the probabilities of the presence of a condition before and after a diagnostic test, respectively. Post-test probability, in turn, can be positive or negative, depending on whether the test falls out as a positive test or a negative test, respectively. In some cases, it is used for the probability of developing the condition of interest in the future.
Schizophrenia affects around 0.3–0.7% of people at some point in their life, or 21 million people worldwide as of 2020. By using precise methods in its diagnosis and a large, representative population, schizophrenia seems to occur with relative consistency over time during the last half-century.
Health in Malta has seen improvements in recent years, with one of the highest life expectancies in Europe. Malta has a good overall quality of health and has seen rapid growth and improvement in key health indicators. Malta has seen significant development in the practice of mental health which has been supported by new infrastructure and increased government health spending. The introduction of health-focused government initiatives, particularly around nutrition, alcohol, smoking, and health will likely contribute to the further improvement of overall health nationwide.
Globally, an estimated 537 million adults are living with diabetes, according to 2019 data from the International Diabetes Federation. Diabetes was the 9th-leading cause of mortality globally in 2020, attributing to over 2 million deaths annually due to diabetes directly, and to kidney disease due to diabetes. The primary causes of type 2 diabetes is diet and physical activity, which can contribute to increased BMI, poor nutrition, hypertension, alcohol use and smoking, while genetics is also a factor. Diabetes prevalence is increasing rapidly; previous 2019 estimates put the number at 463 million people living with diabetes, with the distributions being equal between both sexes icidence peaking around age 55 years old. The number is projected to 643 million by 2030, or 7079 individuals per 100,000, with all regions around the world continue to rise. Type 2 diabetes makes up about 85-90% of all cases. Increases in the overall diabetes prevalence rates largely reflect an increase in risk factors for type 2, notably greater longevity and being overweight or obese. The prevalence of African Americans with diabetes is estimated to triple by 2050, while the prevalence of whites is estimated to double. The overall prevalence increases with age, with the largest increase in people over 65 years of age. The prevalence of diabetes in America is estimated to increase to 48.3 million by 2050.
The evaluation of binary classifiers compares two methods of assigning a binary attribute, one of which is usually a standard method and the other is being investigated. There are many metrics that can be used to measure the performance of a classifier or predictor; different fields have different preferences for specific metrics due to different goals. For example, in medicine sensitivity and specificity are often used, while in computer science precision and recall are preferred. An important distinction is between metrics that are independent on the prevalence, and metrics that depend on the prevalence – both types are useful, but they have very different properties.
The discipline of forensic epidemiology (FE) is a hybrid of principles and practices common to both forensic medicine and epidemiology. FE is directed at filling the gap between clinical judgment and epidemiologic data for determinations of causality in civil lawsuits and criminal prosecution and defense.
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