2. A Patient’S Profile:
• A 60 year old previously healthy female, research
chemist recently developed shortness of breadth and
nose bleeding.
• Pale, pulse 110/ min, low (20%) hematocrit, elevated
(20000/l) leukocyte counts, low platelet (15000/l)
with PBF showing atypical myeloblasts
• Hospitalized for Suspected acute myelogenous
leukemia; confirmed by bone marrow aspirate and
biopsy.
• Chemotherapy started, about 3 weeks later, her temp.
abruptly rose to 39C and neutrophil count dropped to
100 /l.
• No source of apparent infection;
3. Patient Profile…ctd:
• Importance of Risk assessment!!
• How likely is it that patient has a bacterial
infection?
• Her blood and urine cultures were taken, and
broad spectrum antibiotics administered (empiric
treatment)
• Potential Risk of complications from delayed
antibiotic outweighed empiric use of antibiotic
• Cultures confirmed staphylococcus aureus in blood
4. Measures of Disease Occurrence
Epidemiologic measures - to assess outcomes
and thereby guide decisions
• Risk (the likelihood that a person will contract a
disease)
• Prevalence (Load; the amount of disease
already present in the population)
• Incidence Rate (how fast is the new occurrence
of disease)
5. Defined
Population
Have
Disease
Do not
have
disease
Do not have
disease at
baseline
PAR
Prevalent
cases
1. Identify
Population
3. Follow
only those
who did not
have the dis.
2. Determine
who has the Dis.
& who doesn’t
Do not have
disease at
baseline
Develop Dis.
Do not have
disease
Follow up for 1 year
incident
cases
6. Risk (cumulative incidence)
• It is a measure of the occurrence of new cases
• i.e. Proportion of unaffected persons (PAR) in
the population who, will contract the disease
over a specified period of time
New cases
Person at Risk
R =
• Has no unit;
• lies between 0 and 1
7. onset end
A
B
C
D
E
F
Hypothetical study of group of six subjects
1995 96 97 98 99 00 01 02 03 04
Dx …………………………………………Death
97 02
99
97
99 02
Dx…......………..
97
02
What is the Risk of Dis. development within 2 years of enrolment?
New cases
R =
Person at risk
= 1/6 = 0.17 OR 17%
8. Example….
• If 596 patients developed Hosp. Acquired infection
out of 5031 patients admitted in comprehensive
cancer centre
• Estimate a cancer patient’s risk of getting HAI.
• Risk period?
- Starts 48 hrs after hospitalization and ends 48 hrs
after discharge.
New cases
R =
Person at risk
= 596/5031 = 0.12 OR 12%
9. • Can we apply this risk to our patient?
• More likelihood of infection for our patient
can come from studies on similar
subjects…having fever, and low granulocyte
count….
• Now if 1022 such cancer patients were studied
and 530 had HAI then the Risk of HAI is
530/1022 = 0.52 i.e. 52%
10. Measures of Disease Occurrence ctd…
• Prevalence (Burden of Disease)–
indicates number of existing cases of a disease in a
population at a time.
• E.g. An important question in deciding antibiotic use
to the patient is the type and magnitude of infection
anticipated!!
• We know that individuals with low neutrophil count
are susceptible to wide variety of infections…
– S.aureus was cultured from 62 out of 96 patient’s specimens
• Prev. of S.aureus infection = 62/ 96 = 0.65 i.e. 65%
11. onset end
A
B
C
D
E
F
Hypothetical study of group of six subjects
1995 96 97 98 99 00 01 02 03 04
Dx …………………………………………Death
97 02
99
97
99 02
Dx………………
97
02
What is the Prevalence of Disease in 2001?
Total cases
P =
Total population
= 1/4 = 0.25 OR 25%
12. Measures of Disease Occurrence ctd…
• Incidence Rate – measures the rapidity with
which new cases of the disease develop.
• Estimated by observing a population and
counting the number of new cases over Net
Time (person years) i.e.
Incidence Rate = New cases/ Total person time
A subject at risk of disease followed for 1 yr, or
5 yrs contributes 1 or 5 person-years of
observation respectively.
13. onset end
A
B
C
D
E
F
Hypothetical study of group of six subjects
0 1 2 3 4 5 6 7 8 9
Dx …………………………………………Death
97 02
99
97
99 02
Dx,,,,,,,,,,,,,,,,,,,,,,,,
,,,,,,
97
02
How many person years are contributed by A, B, C, D E and F?
04
Total new cases
IR=
Total person years
= 2/22 = 0.09 cases /person years
i.e. 9 cases/ 100 person-yrs
04
04
98
Observation years
95
14. Example of HAI ctd…
• Those 5031 remained under observation for a total
of 127859 patient days
• What is the average length of stay?
• Since 596 patients developed HAI the IR would be
– 596/ 127859= 0.0047 cases/ patient days
• Can be expressed for better readability as 4.7
cases/ 1000 patient days
• Interpretation: among patients similar to those
studied, on an average, about 0.47% patient/day
would be expected to develop a HAI (1/200 cases)
127859 / 5031
= 25.41
15. Calculation of IR for a Large Pop.
• Calculating person-years (PT) for each individual
would be too cumbersome! Alternatively
• PT = (Av. Size of PAR) X (Length of observation)
• In many instances, relatively few people develop
the disease and there is no other demographic
shift hence whole Pop. Can be taken as At
Risk…i.e. not excluding patients
16. Calculation of IR for a Large Pop.
• PT = (Size of entire Pop.) X (Length of
observation)
• If there are an estimated 1,91,85,836 women in
an area btw 1996 and 2000 (5 yrs period) and
2957 women were newly diagnosed with Acute
myelocytic leukimia (AML)
• What is the annual incidence rate of AML ?
• 1,91,85,836 women x 5 Yrs = 9,59,29,180 WY
• IR = 2957 new cases/ 9,59,29,180 WY =
3.1cases /1,00,000 WY
17. Characteristic Risk Prevalence Incidence Rate
What is
measured
Probability of
Disease
occurance
Proportion of
Pop. With disease
Rapidity of
Disease
Occurrence
Units None None Cases/ person-
time
Time of
disease Dx
Newly
diagnosed
Existing cases Newly diagnosed
Synonyms Cumulative
Incidence
- Incidence Density
Characteristics of Risk, Prevalence & Incidence Rate
In our Hypothetical Ex. In 2001 Prev. was 25%,
2 Yr. Risk was 17% and the IR was 9 cases/ 100 yrs
18. Problems with Incidence and
Prevalence Measurements
• Problems with Enumerator
– The first problem is defining who has the disease.
– The next issue is Method of data collection – interview, self
reporting , survey… associated biases!!
• Problems with Denominators
– everyone in the group represented by the denominator must
have the potential to enter the group that is represented by
the numerator…
PAR concept
• Problems with Hospital Data
19. Relationship Between Incidence and
Prevalence
• There is an important relationship between
incidence and prevalence: in a steady-state
situation, in which the rates are not changing
and in-migration equals out-migration, the
following equation applies:
• Prevalence = Incidence × Duration of disease
20. Example
• 2,000 persons are screened for tuberculosis,
Using chest x-rays: 1,000 are upper-income
(HIG) individuals and 1,000 are lower-income
(LIG) individuals.
• X-ray findings are positive in 100 of the HIG
and in 60 of the LIG.
• Can we therefore conclude that the risk of
tuberculosis is higher in HIG people than in
LIG people?
22. 20 30 40 50 60 70 80
0
100
200
300
400
20%
15%
10%
5%
0%
Annual
Rate/
100000
Percent
of
total
cases
Breast cancer incidence rates and distribution of cases by age
Age in yrs
The incidence is increasing so dramatically with
age, why are only fewer than 5% of the cases
occurring in the oldest age group of women?
24. Incidence stable but prevalence increasing
indicates:-
24
0
5
10
15
20
25
30
35
40
45
1
9
9
0
1
9
9
3
1
9
9
6
1
9
9
9
Prevalence
Incidence
New Program or
Better Dx Test !!!
•Death is prevented
and Dis is not cured
• Diagnosed more
•Immigration of cases
25. Incidence maintained but prevalence
declining means:-
25
0
5
10
15
20
25
30
35
1
9
9
0
1
9
9
2
1
9
9
4
1
9
9
6
1
9
9
8
incidence
prevalence
New effective drug!
Or Dis. Became more
Virulent/ fatal,
Emigration of cases
26. Survival
• Probability of being alive for a specific length of
time
• For a Chronic Dis. Like cancer, 1 and 5 Year
survival rates are often used as indicator of the
severity of the disease and the prognosis.
• E.g. if 5-Yr survival for active TB is 0.79, it means
that only 79% of patients with active TB survive
at least 5-Yrs after diagnosis
• Survival Newly Dx Pts.– Deaths
Newly Dx Pts.
For a specified time
27. Dx onset end
A
B
C
D
E
F
Hypothetical study of group of six subjects
0 1 2 3 4 5
Observation years
Patients
Censored
Death
Censored
Death
What is the 2 year survival rate?
2 year survival rate = 5/6 = 0.83 i.e. 83%
What is the 2 year Risk of Death?
2 year Risk of Death = 1/6 = 0.17 i.e. 17%
5 yr S If we assume B & E survive all 5 yrs = 5/6= 0.67=83% !
5 yr S If we assume B & E didn’t survive all 5 yrs = 3/6= 0.33=50%! !
28. Methods to account for censored cases
• Life Table analysis
• Kaplan-Meier analysis And Graphs
0 1 2 3 4 5
20
40
60
80
100
0
Survivors
Percent
Years since Dx
47%
68%
58%
? Median Survival Time
50
52%
Editor's Notes
AML also known as acute nonlymphocytic leukemia, tends to occur in later life, with a median age at onset of 65 years, males are at a higher risk than females.
Risk factors include – exposure to ionizing radiation, benzene, certain drugs, and perhaps cigarette smoke, more in Down syndrome
Presents with variety of symptoms – weakness, fatigue, unexplained weight loss, infection, and bleeding.
Physical examination shows pale, have multiple bruises and fever with evidence of localized infection.
Laboratory examination shows – anemia, low platelet counts, and markedly elevated leukocyte counts
Infection and bleeding in these patients is directly related to chemotherapy induced suppression of bone marrow with consequent reduction in the circulating levels of neutrophils and platelets.
In about 50% of these neutropenic patients with fever, an infection cannot be doccumented either clinically or microbiologically but on the basis evidence broad spectrum antibiotics are given
factor influencing prevalence – longer duration of disease, prolonged life span, increased incidence, in - migration of susceptible, in - migration of cases, out migration of healthy, improved dx facilities (since so many factors unrelated to the cause of dis. Determine prev.- prev studies do not provide strong evidence of causality, but a good measure for chronic diseases of slow onset)
Risk – is the probability that individuals in the population will get the disease in specified time period
What is the difference between incidence and prevalence?
Prevalence can be viewed as a snapshot or a slice through the population at a point in time at which we determine who has the disease and who does not. But in so doing, we are not determining when the disease developed. Some individuals may have developed arthritis yesterday, some last week, some last year, and some 10 or 20 years ago. Thus, when we survey a community to estimate the prevalence of a disease, we generally do not take into account the duration of the disease.
Consequently, the numerator of prevalence includes a mix of people with different durations of disease, and as a result we do not have a measure of risk.
If we wish to measure risk, we must use incidence, because in contrast to prevalence, it includes only new cases or events and a specified time period during which those events occurred.
In the medical and public health literature, the word prevalence is often used in two ways:
Point prevalence- Prevalence of the disease at a certain point in time—this is the use of the term prevalence that we have just discussed.
Period prevalence- How many people have had the disease at any point during a certain time period? The time period referred to may be arbitrarily selected, such as a month, a single calendar year, or a 5-year period. Some people may have developed the disease during that period, and others may have had the disease before and died or been cured during that period. The important point is that every person represented by the numerator had the disease at some time during the period specified.
People at Risk Who Are Observed throughout a Defined Time Period
In the first type of denominator for incidence rate, we specify a period of time, and we must know that all of the individuals in the group represented by the denominator have been followed up for that entire period. The choice of time period is arbitrary: We could calculate incidence in 1 week, incidence in 1 month, incidence rate in 1 year, incidence rate in 5 years, and so on. The important point is that whatever time period is used in the calculation must be clearly specified, and all individuals included in the calculation must have been observed (at risk) for the entire period. The incidence rate calculated using a period of time during which all of the individuals in the population are considered to be at risk for the outcome is also called cumulative incidence, which is a measure of risk.
When All People Are Not Observed for the Full Time Period, Person-Time, or Units of Time When Each Person Is Observed is used
Often, however, every individual in the denominator has not been followed for the full time specified for a variety of reasons, including loss to follow-up or death from a cause other than that being studied. When different individuals are observed for different lengths of time, we calculate an incidence rate (also called an incidence density), in which the denominator consists of the sum of the units of time that each individual was at risk and was observed. This is called person-time and is often expressed in terms of person-months or person-years of observation.
Let us consider person-years: One person at risk who is observed for one year = one person-year. One person at risk observed for 5 years = 5 person-years. But 5 people at risk, each of whom is observed for only 1 year, also = 5 person-years.
HAI = documented by cultures
was not incubating on admission
occurred at least 48 hrs after admission
occurred no more than 48 hrs after discharge
Prevalence is an important and useful measure of the burden of disease in a community.
For example, how many people in the community have arthritis? This information might help us to determine, for example, how many clinics are needed, what types of rehabilitation services are needed, and how many and what types of health professionals are needed. Prevalence is therefore valuable for planning health services. When we use prevalence, we also want to make future projections and anticipate the changes that are likely to take place in the disease burden. However, if we want to look at the cause, or etiology, of disease, we must explore the relationship between an exposure and the risk of disease, and to do this, we need incidence rates.
Nevertheless, prevalence data may at times be very useful—they may be suggestive if not confirmatory in studies of the etiology of certain diseases. For example, asthma is a disease of children for which incidence is difficult to measure because the exact time of the beginning of the disease (its inception) is often hard both to define and to ascertain. For this reason, when we are interested in time trends and geographic distribution of asthma, prevalence is the measure most frequently used. Information on prevalence of asthma is often obtained from self-reports such as interviews or questionnaires. Figure 3-15 shows current asthma prevalence in children up to 17 years of age, by state in the United States for 2001–2005. Current asthma prevalence was based on two questions: “Has a doctor or other health professional ever told you that (child's name) had asthma?” and “Does (child's name) still have asthma?” Overall, prevalence was highest in the northeastern states. The explanation for this observation is not entirely clear. Although adverse climate and polluted air may be implicated, other factors may also play a role in the high asthma prevalence in the northeast, such as more complete ascertainment of cases in the medical care system and higher asthma prevalence in Puerto Rican children who are concentrated in this region.
Another example of the value of prevalence data is seen in Figure 3-16 . One of the most significant and challenging public health problems today in the United States and in other developed countries is the dramatically increasing prevalence of obesity. Obesity is associated with significant morbidity and mortality and is a risk factor for diseases such as hypertension, type 2 diabetes, coronary disease, and stroke. In this figure, prevalence of obesity by state is shown for each of four years: 1990, 1995, 2000, and 2005. The trend over time is grim: In 1990, all reporting states reported obesity prevalence data below 15%. By 2005, all but four states had prevalence estimates above 20%; 17 states reported a prevalence of obesity equal to or greater than 25% and three of these states (Louisiana, Mississippi, and West Virginia) reported obesity prevalence over 30%.
average length of stay = 127859/5031=25.41 days
If we would have taken all patient days i.e. not excluding 596 from 127859 IR would be 0.00464 cases / patient day rather than 0.0047 cases / patient day (not much difference)
We have said that incidence is a measure of risk and that prevalence is not, because it does not take into account the duration of the disease.
Ignore the bar graph for the moment, and consider the line curve. The pattern is one of continually increasing incidence with age, with a change in the slope of the curve between ages 45 and 50 years. This change is observed in many countries. It has been suggested that something happens near the time of menopause, and that premenopausal and postmenopausal breast cancer may be different diseases. Note that, even in old age, the incidence or risk of breast cancer continues to rise.
Now let us look at the histogram—the distribution of breast cancer cases by age. If the incidence is increasing so dramatically with age, why are only fewer than 5% of the cases occurring in the oldest age group of women? The answer is that there are very few women alive in that age group, so that even though they have the highest risk of breast cancer, the group is so small that they contribute only a small proportion of the total number of breast cancer cases seen at all ages. The fact that so few cases of breast cancer are seen in this age group has contributed to a false public impression that the risk of breast cancer is low in this group and that mammography is therefore not important in the elderly. This is a serious misperception. The need to change public thinking on this issue is a major public health challenge. We therefore see the importance of recognizing the distinction between the distribution of disease or the proportion of cases, and the incidence rate or risk of the disease.
e.g. rabies
# Disease duration is reduced and it is becoming acute, or
# Disease becoming more fatal
For example, when insulin first became available, what happened to the prevalence of diabetes?
The prevalence increased because diabetes was not cured, but was only controlled. Many patients with diabetes who formerly would have died now survived; therefore, the prevalence increased.
This seeming paradox is often the case with public health programs: a new measure is introduced that enhances survival or detects the
disease in more people, and the net effect is an apparent increase in prevalence. It may be difficult to convince some people that a program is successful if the prevalence of the disease that is the target of the program actually increases. However, this clearly occurs when death is prevented and the disease is not cured. e.g. diabetes
1.Slow recovery, Fatality reduced (potent drugs available, new drugs effective) or,
3.Immigration of cases from other area (for better facility available).
Recovery is becoming rapid, (may be a new drug identified is more effective)
# Disease turns into a more fatal one (because of treatment failure, change in virulence, drug resistance) or,
# Selective emigration of cases (to seek treatment elsewhere)
Observation of each patient begins at time ‘0’ and continues until one of the following outcomes occur: death, survival for 5 yrs, or follow up ceases (the subject is censored) prior to death or completion of a full period of observation
Example: from 1992-1999 only 19% of patients survived for at least 5 yrs from the time of Dx
for persons under 65 yrs of age at Dx 5-yr survival rate = 31% while for persons above 65 yrs it is 4%
So a person of less than 65 yrs, if diagnosed with this disease would be expected to have 1 in 3 chance of surviving 5 yrs from the time of Dx while a person > 65 yrs has only 1 in 25 chance
