Measures of disease frequency (Incidene, Prevalence)

Wanted to Share some information with all from BMJ site.

http://www.bmj.com/epidem/epid.2.html


Dr. Mufti Zubair

1) Incidence:
The frequency with which something, such as a disease, appears in a particular
population or area. In disease epidemiology, the incidence is the number of
newly diagnosed cases during a specific time period. The incidence is distinct
from the prevalence which refers to the number of cases alive on a certain
date.





2) Incidence,
a measure of the risk of developing some new condition within a specified
period of time.





3) Incidence
refers to the frequency of development of a new illness in a population in a
certain period of time, normally one year. When we say that the incidence of
this cancer has increased in past years, we mean that more people have
developed this condition year after year, i.e.:, the incidence of thyroid
cancer has been rising, with 13,000 new cases diagnosed this year.




1) In epidemiology, the prevalence
of a disease in a statistical
population is defined as the total number of cases of the disease in
the population at a given time, or the total number of cases in the population,
divided by the number of individuals in the population.

2) Prevalence
refers to the current number of people suffering from an illness in a given
year. This number includes all those who may have been diagnosed in prior
years, as well as in the current year. The incidence of a cancer is 20,000 year
with a prevalence of 80,000 means that there are 20,000 new cases diagnosed
every year and there are 80,000 people living in the United states with this illness,
60,000 of whom were diagnosed in the past decade and are still living with the
disease. The number of people cured of the disease is not included in
prevalence.

Mathematically prevalence can be defined as follows

let a = the number of individuals in the population with the disease at a
given time

let b = the number of individuals in the population without the disease at a
given time

a

Prevalence a = _______

a + b

Incidence Vs prevalence (example)

Schizophrenia is a devastating mental illness and a major contributor to the
global burden of disease. In their quest to understand schizophrenia
epidemiology, John McGrath and colleagues have previously undertaken a
systematic review of schizophrenia incidence—that is, the number of new cases
diagnosed each year in a specified population. They now report results from a
second systematic review that examines published studies on the prevalence of
the disease—i.e., on the number of people who are suffering from the disease at
a given time or within a specified time interval. (Incidence studies can
suggest risk factors that may underlie variations in the disease. Prevalence
studies are central to health systems planning.)

About Prevalence and Incidence Statistics



Statistical information such as prevalence, incidence, deaths, and other
data is provided from numerous sources and is subject to numerous provisos.
Nevertheless, it is hoped to be useful, if not completely accurate.

Prevalence versus incidence: Prevalence and incidence are different
measures of a disease's occurrence. The "prevalence" of a condition
means the number of people who currently have the condition, whereas
"incidence" refers to the annual number of people who have a case of
the condition. These two measures are very different. A chronic incurable
disease like diabetes can have a low incidence but high
prevalence, because the prevalence is the cumulative sum of past year incidence
rates. A short-duration curable condition such as the common
cold can have a high incidence but low prevalence, because many
people get a cold each year, but few people actually have a cold at any given
time (so prevalence is low and is not a very useful statistic). To understand
prevalence versus incidence, consider these examples (which over-simplify but
are still hopefully useful):

• Short-duration disease:
  A person who has a common cold for one day, would be
  added to the incidence statistics, but (theoretically anyway) shouldn't be
  on the prevalence list.
  
• Newly diagnosed chronic
  disease: A person diagnosed with diabetes will be on the incidence
  numbers and prevalence numbers in that first year, but then only on the
  prevalence numbers for second or later years.
  
• Deaths: A person who
  dies from a disease stops being on the prevalence data for both later
  years and also the current year (unless prevalence statistics include this
  time period). That person will be on the incidence numbers only for the
  year they were diagnosed, and not in the year they die if they had the
  disease more than a year. A death from a short disease like flu does get
  included in incidence, but not prevalence. A death after many years from a
  long-term disease like diabetes removes that person from
  prevalence numbers (and they should only have been on the incidence data
  their first diagnosis year).
  

Maximum of prevalence or incidence: Taking the maximum value of
either of the prevalence and incidence numbers for a disease is a reasonably
useful indicator that is used in certain places throughout this information. It
is a kind of "people affected" measure that gives an approximate value
to the number of people who would have to deal with a condition in any given
year.

Problems with prevalence data: Prevalence attempts to measure the
number of people affected by a condition at any given time. There are various
possible problems with prevalence data:

• Diagnosed versus
  undiagnosed prevalence: Two estimates of prevalence are not
  necessarily comparable. Some estimates attempt to quantify the number of
  diagnosed people. Other prevalence estimates attempt to include
  undiagnosed people who unknowingly have the condition. Some prevalence
  numbers include only symptomatic conditions whereas others may include
  latent infections.
  
• Different methods of
  gathering prevalence data: Prevalence numbers may also have been
  computed via various estimate methods ranging from research studies to
  phone surveys.
  
• Prevalence and
  "cured" or "remission" conditions: Conditions that
  go into "remission" but are not necessarily "cured",
  such as cancer, cause problems for prevalence
  data. Some such estimates use 5-year prevalence or 10-year prevalence
  estimates, which includes only people who have had cancer 5 or 10 years
  previously (even if they are "cured"). This effectively assumes
  that a remission becomes a cure after 5 or 10 years, so the person is then
  excluded from the prevalence numbers.
  

Problems with incidence data: Incidence data attempts to measure the
number of people who become affected with a condition each year. Incidence
includes only new conditions, not ongoing treatment of existing conditions. The
actual number of people affected by a condition in a year can be less than
incidence reports in cases where people get multiple cases (e.g. common cold).
Two incidence rates are not necessarily comparable. Some incidence data uses
government notifications, others based on physician or hospital diagnoses, and
various other methods. Some estimates of incidence for under-diagnosed conditions attempt to
justify a larger incidence rate than is reported by doctors or medical
authorities, whereas other rates may use only the official reported rates.

Rates of incidence/prevalence calcuations: This
site attempts to manipulate prevalence and incidence data to give more relevant
data, such as to report the percent of the population affected, total number of
people affected nationally, or the odds in a "1 in 1000" format.
These computations are based on population data for the relevant reporting region
(usually the national USA).
Some computation rates use different base data: prevalence, incidence, or
maximum of prevalence/incidence. In some cases where the data is reported as a
word such as "common", "rare", "uncommon" or
similar phrase, an arbitrary numerical percentage has been applied to this
information. Data that is reported based on births, such as 1-in-3000 births,
has either been left as is (for chronic conditions) or modified by an estimate
of the number of births. Data reported as a percent of pregnancies or pregnant
women has been calculated using an estimate of the number of pregnancies
annually.

Lifetime risk data: Some conditions report a risk factor for having a
condition in your lifetime. For example, cancer is widely reported to affect
about 1 in 3 people in their lifetime. These rates are naturally much higher
than either prevalence or incidence data, because they are effectively the
cumulative risk of incidence/prevalence over multiple years.

General problems with the data: In addition to the above discussion,
there are various general qualifiers with regard to prevalence, incidence, and
any of the other types of data. Use of the data may incur the old
apples-and-oranges comparison problem because of data differences. Problems
with using the data include:

• Unclear sources: there are
  numerous statistics reported in articles and on the internet, and
  determining the actual study or survey on which an estimate is based is
  often difficult, even for statistics reported by health authorities or government
  agencies.
  
• Data ranges: where a rate is
  reported as a range, such as "3 to 5 million people", the lower
  number is arbitrarily chosen and used here. This is a conservative
  assumption, but may cause some estimates to be lower than they should.
  
• Different definitions of
  prevalence: some prevalence numbers use estimates of people diagnosed,
  others try also to include estimated of undiagnosed people, and some use
  different values like 5-year prevalence or 10-year prevalence data.
  
• Different sources: data has
  been collected from numerous sources, and the reputability and accuracy of
  each source cannot reasonably be completely confirmed.
  
• Different study
  methodologies: the data comes from various studies that used different
  methodologies. Some data comes from government notification bodies, other
  from patient phone surveys, others using various methods of estimation,
  and so on. Many estimates are computed from a small sample and then
  extrapolated to a larger population group, and this method has various inherent
  limitations to its accuracy.
  
• Different disease categories:
  some data may use different categorization arrangements to determine who
  has a particular disease. Some studies use the ICD categories, others do
  not, and there are actually small variations in the different ICD
  categorizations in any case. For example, should wheezing be part of
  asthma or separate?
  
• Different years: data may
  come from numerous different years.
  
• Different locations: data may
  come from different countries, states, or areas.
  
• Different age groups: data
  may refer to a particular age group, such as "3% of adults", and
  may not necessarily reflect the overall prevalence in the entire
  population of all ages.
  
• Different racial factors:
  some data may reflect a particular race more accurately and not apply to
  the entire population.
  
• Inherent reporting bias:
  although most reputable organizations use official indepedent statistics,
  some organizations may tend to quote higher numbers because either (a)
  they see the medical condition every day and assume it is highly
  prevalence, or (b) to make the conditions they monitor seem more important
  such as to justify funding levels or seek donations.
  
• Country-specific information:
  Most of the data is reported from USA sources, and may be of
  limited value to other countries. For example, certain conditions have a
  much higher prevalence worldwide, especially in developing countries, than
  in industrialized nations like the USA.
  

For Vedio

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What's the difference between 'incidence' and 'prevalence'?