HSS4303B – Introduction to Epidemiology

Jan 14, 2010 – Measures of Morbidity & Mortality

Epidemiologists: “Are they smart and good looking, or good looking and smart?"

Discuss.

From last class....

• Remember, a doctor is trying to decide what to tell the patient about the relationship between dietary fat and breast cancer

• Evidence-based medicine was employed, through which published studies were assessed

From last class

• The pyramid of evidence was considered• Methods of assessing studies were discussed• Some sources of bias were discussed

So What Has The Lit Review Revealed?

Dietary fat and breast cancer (1)• Ecologic or correlation studies have demonstrated a

consistently strong relationship between dietary habits, as estimated by per-capita consumption of dietary fat, and breast cancer occurrence in different countries.

• Plots of these data have yielded a linear relationship, with increasing fat consumption associated with higher breast cancer occurrence.

• The problem with such studies is that they do not demonstrate that increased dietary fat in individuals is associated with breast cancer occurrence in the same individuals (ie, the ecologic fallacy may be involved).

• In industrialized countries in which fat consumption and breast cancer mortality tend to be higher than in developing countries, it may not be the high fat consumers who are developing breast cancer.

Aside: Ecological Fallacy

• often called an ecological inference fallacy• assumes that individual members of a group have the

average characteristics of the group at large• Stereotypes are one form of ecological fallacy, which

assumes that groups are homogeneous• Example: study shows that areas with high

concentrations of farm animals are also the areas with lowest concentrations of childhood asthma. – It’s a fallacy to then assume that a child who has asthma

must not live near any farm animals

Dietary fat and breast cancer (2)• The comparatively high mortality rates of breast cancer in

industrialized countries may be attributable to other factors, such as earlier menarche, delayed childbearing, or other reproductive factors.

• It has been speculated that mammary neoplasms are controlled by endocrine balance, which in turn is affected by dietary factors, including fat intake.

• Women consuming high-fat diets have been shown to have more circulating estrogen than women on low-fat diets. In postmenopausal women, adipose tissue has been demonstrated to be a contributor to the production of estrogen.

• Dietary fat intake may also have modified DNA synthesis and cell duplication.

• Hormonal carcinogenesis of the breast

So? Do You Have Enough Info To Inform The Patient?

Use A Systematic Review

• A synthesis of the medical research on a particular subject. It uses thorough methods to search for and include all or as much as possible of the research on the topic. Only relevant studies, usually of a certain minimum quality, are included.– NHS

Want To See Some Examples?

• Visit CADTH.CA• Visit COCHRANE.ORG

Systematic review (1)• ______________ is a type of quantitative systematic

review in which the results of multiple studies that are considered combinable are aggregated together to obtain a precise, and hopefully unbiased, estimate of the relationship in question.

• _______________ helps in two specific ways:– 1. By combining a series of smaller studies, each with a

statistically imprecise estimate of effect, a larger sample size is obtained, with a corresponding increase in statistical precision.

– 2. By identifying the differences in findings across different studies, sensitivity analyses can be conducted that may lead to greater insight into the sources of heterogeneity.

Meta-analysis

Um… meta-analysis?

Terminology

• You will find that people use the terms “systematic review” and “meta-analysis” interchangeably and incorrectly

Systematic reviews do not have to have a meta-analysis - there are times when it is not appropriate or possible

A meta-analysis is also possible without doing a systematic review - you could just find a few studies and calculate a result, with no attempt to be systematic about how the particular studies were chosen.

More About Meta-Analysis

• A meta-analysis is a two-stage process.– The first stage is the extraction of data from each

individual study and the calculation of a result for that study (the 'point estimate' or 'summary statistic'), with an estimate of the chance variation we would expect with studies like that (the 'confidence interval').

– The second stage involves deciding whether it is appropriate to calculate a pooled average result across studies and, if so, calculating and presenting such a result.

The “Forest Plot”

• Used in meta-analysis to graphically present the pooled data and the summary conclusion

• Read about it here:– http://www.cochrane-net.org/openlearning/

Other/Forest_plot.pdf

Systematic review (2)

• The steps in a systematic review should follow a clear sequence.

• The first step is to formulate a clear and meaningful question to be addressed. – (1) the type of person(s) involved, – (2) the type of exposure that the person(s) experiences (eg, a risk

factor, a prognostic factor, a diagnostic procedure, or a therapeutic intervention),

– (3) the type of control with which the exposure is compared, and – (4) the outcomes to be addressed. In the context of the patient

profile, we might specify the question in the following way: – For premenopausal women with a family history of breast cancer, is

reduction of dietary fat consumption substantially below levels typical of the American diet likely to reduce the risk of developing breast cancer?

Systematic reviews• The next step is to search for the studies of interest.• Once the articles for potential inclusion are identified, they must be reviewed one

at a time.– Specific eligibility criteria for inclusion must be specified. The included studies should be

directly relevant to the question under consideration. • The actual analysis of the data begins with an estimation of the effect of interest in

each of the included studies. • The results are displayed in terms of estimated relative risk of developing breast

cancer associated with a reduced level of dietary fat intake. • If reducing fat in the diet decreases the risk of developing breast cancer, a relative

risk less than 1 would be expected. • Examination of the corresponding confidence intervals for the individual studies

provides some insight into the statistical precision of the results and whether they are statistically significant. By convention, 95% confidence limits typically are calculated. The odds or risk ratio often is displayed on a logarithmic scale

• Once the individual and combined estimates are obtained, it is useful to consider the level of heterogeneity across the individual results.

• Sensitivity analysis can be performed to identify patterns of results across the individual study results and potentially provide insight into any heterogeneity that exists.

Meta-analysis of dietary fat intake and risk of breast cancer

• A meta-analysis published in 2003 explored the relationship between dietary fat intake and risk of breast cancer.

• This systematic review included 45 studies (31 case–control and 14 cohort), with a combined total of over 25,000 breast cancer patients and 580,000 control or comparison subjects.

• An overall small increase in risk of breast cancer was associated with elevated total fat intake in both the case–control (OR = 1.14) and cohort studies (RR = 1.11).

• The combined association was statistically significant and was higher in the studies judged to be of better quality. Similar findings were observed in analyses of saturated fat and meat intake.

Meta-analysis of five hypothetical epidemiologic studies (A–E) of the relationship between reduced dietary fat intake and the risk of developing breast cancer.

Terms Associated with Meta-Analysis

• Kappa statistic– Measures concordance (agreement) between

raters

• Q statistic– Measures homo/heterogeneity

• I2

– describes the percentage of total variation across studies that is due to heterogeneity rather than chance

More on Q

• As we are trying to use the meta-analysis to estimate a combined effect from a group of similar studies, we need to check that the effects found in the individual studies are similar enough that we are confident a combined estimate will be a meaningful description of the set of studies.

• In doing this, we need to remember that the individual estimates of treatment effect will vary by chance, because of randomization. So we expect some variation. What we need to know is whether there is more variation than we'd expect by chance alone. When this excessive variation occurs, we call it heterogeneity.– > Q is a test for heterogeneity

Interpreting Q

• If Q>S-1, then there is significant heterogeneity– If p<0.05 then there is heterogeneity– If p>0.05 then there is homogeneity

More on I2

• I2, describes the percentage of total variation across studies that is due to heterogeneity rather than chance.

• A value of 0% indicates no observed heterogeneity, and larger values show increasing heterogeneity.

• I2 = (Q - (n-1)) / Q * 100

• 25% = low, 50%=moderate, 75%=high

Why Do We Compute Q and I2?

• The degree of heterogeneity will determine what method we use to compute the summary statistics for our meta-analysis– > eg, when heterogeneity is high, we use what’s

called a “random effects model”

• More info:– http://amchang.net/StatTools/ESDiff_Exp.php

Heterogeneity statistics for examples of meta-analyses from the literature. Meta-analyses were conducted using either meta or metan in STATA15

Heterogeneity test

TopicOutcome/analysis

Effect measure

No of studies

Q df PI2(95%

uncertainty interval)*

Tamoxifen for breast cancer16 Mortality Peto odds

ratio 55 55.9 54 0.40 3 (0 to 28)

Streptokinase after myocardial infarction17 Mortality Odds ratio 33 39.5 32 0.17 19 (0 to 48)

Selective serotonin reuptake inhibitors for depression13 Drop-out Odds ratio 135 179.9 134 0.005 26 (7 to 40)

Magnesium for acute myocardial infarction18 Death Odds ratio 16 40.2 15

0.0004

63 (30 to 78)

Magnetic fields and leukaemia19 All studies Odds ratio 6 15.9 5 0.007 69 (26 to 87)

Amantadine11 Prevention of influenza

Odds ratio 8 12.44 7 0.09 44 (0 to 75)

Meta-analyses of six case-control studies relating residential exposure to electromagnetic fields to childhood leukaemia.

Summary odds ratio calculated by random effects method

Eg.

Okay, on to today’s topic...

Smoking more than one pack of cigarettes a day increases the risk of cardiovascular disease by 70%-80%, and smoking more than two packs per day increases the risk by 200%. Passive smoke increases the risk by 30%. -Basic Res Cardiol 2000 (Suppl I); 95:152-158

normal rate of miscarriage of pregnancy is 2 to 3%, and amniocentesis increases that risk by an additional 1/2 to 1%. -ds-health.com

A 1-month delay in treatment for an early-stage primary breast cancer with a 130-day doubling time increases the risk of axillary lymph node involvement by 0.9% -Obstet Gynecol. 1996 Mar;87(3):414-8.

A measure of the occurrence of new cases of the disease of interest in a population

The probability of obtaining an outcome, given the presence or change in status of an exposure

?

• Risk is a measure of the occurrence of new cases of the disease of interest in a population

new cases A• Risk (R) = ----------------- = ----

persons at risk N• Risk has no units and lies between 0 (no new cases) and 1 (the

entire population has the disease of interest)• Risk can be expressed as a percentage also• If one person in a group of six gets flu the risk of getting flu in

that group is• R= 1/6 =0.17 =17%

In 1022 cancer patients with fever and granulocytopenia, 530 patients developed clinically or microbiologically documented bacterial infections

What is the risk of infection in granulocytopenic febrile cancer patients?

530/1022 = 0.518 = 51.8%

Remember This?

A recent study found that 30% of women who date online have had sex on the first date with gentlemen they've met online. Moreover, the study found that 77% of these women had had unprotected sex in those encounters. -www.onlinedatingmagazine.com/news2007/womenonlinedatersrisky.html

"When you have unprotected sex with people you are meeting online, you are playing russian roullette [sic] with your health. It's not a matter of 'if' you'll get a sexually transmitted disease, but rather 'when' and 'how many'.”-’STI expert’ responding to the results of the study

How would you go about calculating the actual risk of a heterosexual Canadian woman contracting an STI fro unprotected sex with a random Canadian man?

What would you need to know?

•Prevalence of STIs in Canada (among whom? Which diseases?)

•What about transmission rate? (Above only computes risk of exposure)

Canada’s STI Surveillance Report:www.phac-aspc.gc.ca

The current prevalence is164 cases per 100,000 population, or about 0.16% of the total population, assuming a conservatively estimated base population of 35 million

But is the right prevalence statistic to use?

The age-specific chlamydia burden among Canadian men aged 25+ is 9374 cases.

Canadian adult male population of about 20 million

Risk of Canadian male having Chlamydia = 9374/20000000 = 0.00047 = 0.05%

Is that the end of the story?

Transmission

So far we've been talking about the chances of being exposed to an STI. What about actually contracting one?

The transmission rate of Chlamydia is between 30% and 40%. In other words, only 30-40% of sexual encounters with an infected person will result in the disease being transmitted.

30% of 0.05% = 0.015%40% of 0.05% = 0.020%

So, assuming the more conservative estimate (0.02% chance of both exposure and transmission), a Canadian woman would have to sleep with 5000 men to get anything resembling the “guarantee” of an STI that the “expert” suggested.

What is wrong with this analysis?

Two Terms:

• Mortality

• Morbidity

Two Terms:

• Mortality– Rate of death (due to a disease)

• Morbidity– Rate of presence of a disease in a population– A diseased state

Measurements of Morbidity

• Incidence– And “cumulative” incidence

• Prevalence– And “point” prevalence

•Rate of new cases

•Rate of all cases

Incidence

• Is the number of new cases of a disease that occur during a specified period of time in a population at risk of developing the disease

– Incidence measures risk in specific groups of people (sex, age, occupation, ethnic background)

– The denominator includes population at risk– Time must be specified and all individuals in the denominator

must be at risk during that period of time– Time is arbitrary and depends on disease (week, month, year

or a decade)– Can be expressed as a % or as a rate per 1000 people or in

person-time (e.g., person-years)

Incidence

• In general, incidence is:

A = number of new cases -------

P =population at risk

Give over a specific time period

However

• Sometimes we follow a single population to see how many new cases occur

• And sometimes we follow a series of individuals at different times and for different durations to see which ones manifest as new cases– > we have to calculate incidence differently in

each case

Cumulative incidence vs Incidence Density

• Cumulative incidence (also called incidence proportion) is incidence calculated using a period of time during which all of the individuals in the population are considered to be at risk for the disease• i.e., when following an entire population

• Incidence density is calculated by including in the denominator the sum of time during which each individual was at risk (person years)• i.e., when following a group of individuals who have

been observed for different durations each

Incidence Density

A = number of new cases

____________ P T = population at risk (P)

duration of risk (T)

Given as a rate per population-timeE.g. cases per 1000 person-years

http://www.phac-aspc.gc.ca/publicat/haest-tesvs/appendix-eng.php

Another Way of Looking at It

http://www.phac-aspc.gc.ca/publicat/haest-tesvs/appendix-eng.php

http://www.phac-aspc.gc.ca/publicat/haest-tesvs/appendix-eng.php

Incidence

• Raywatville has 1000 people. Over a two year period, 28 people develop dumb-ass disease. What is the incidence of dumb-ass disease in Raywatville over this period?

IR = 28/1000 = 28 cases per 1000 population or = 2.8%

Is this cumulative incidence or incidence density?

Incidence

• Raywatville has 1000 people. Over a two year period, 28 people develop dumb-ass disease. What is the incidence of dumb-ass disease in Raywatville over this period?

IR = A/PT = 28/(1000x2)= 14 per 1000 person-years

Is this cumulative incidence or incidence density?

This is an easy example of density, since all the residents of Raywatville were observed for the same duration

Incidence density = A_______ P x T

= A cases______________P persons x T time

= 28 cases----------------------1000 persons x 2 years

= 14 cases ---------- person-years

Example (p.21)

• A total of 5031 patients were observed for a total period of 127,859 patient-days

• 596 patients developed a nosocomial infection• What is the incidence rate of nosocomial

infection in the hospital? (Give it as number of cases per 1000 patient-days)

4.7 cases / 1000 patient-days

Is this cumulative incidence or incidence density?

596 new cases divided by 127,859 patient-days, all multiplied by 1000

Another Example• In the United States, the National Cancer Institute maintains a network of

registries that collect information on all new occurrences of cancer within populations residing in specific geographic areas.

• Collectively, these registries cover about 14% of the population of the United States, and between 1996 and 2000, 2957 females were newly diagnosed with acute myelocytic leukemia in these areas.

• An estimated 19,185,836 females lived in these combined areas on average during this 5-year period.

• First, what is the total number of woman-years for this period?

19,185,836 women x 5 years = 95,929,180 woman-years.

Second, what is the incidence rate for leukemia for this period for this population?

IR=A/PT = 2957/95929180 = 0.03 cases per 1000 woman-years

Incidence rates

Six patients were observed for 8 years. During that time, 2 were diagnosed with dumbass disease

How would you go about computing the incidence rate of dumbass disease over this 8 year period?

Cumulative incidence or incidence density?

Incidence rates

Patient Years at risk

ABCDEF

Incidence rates

Patient Years at risk

ABCDEF

223726

Incidence rates

Incidence rate = A / PTA = 2PT = 2+2+3+7+2+6 = 22

2 / 22 = 0.09 cases / person-year

9 cases / 100 person-years

Acute hepatitis incidence in Canada

Incidence rate calculation for a big population

• Calculation of incidence (density) rates for a large population, such as that in a city, by separately enumerating the person-years at risk for each individual, would require a tremendous amount of work. Fortunately, person-time for a large population can often be calculated by multiplying the average size of the population at risk by the length of time the population is observed:

• PT = (Average size of population at risk) x (Length of observation period)

• In many instances, relatively few people in the population develop the disease, and the population undergoes no major demographic shifts during the time period of observation. In such situations, the average size of the population at risk can be estimated by the size of the entire population, using census or other data. The person-time of a large, stable population can often be estimated by

• PT = (Size of entire population) x (Length of observation period)

Identifying new cases: step 1

Identifying new cases: step 2

Why is incidence important?

Expected and observed new cases

Prevalence

http://www.worldmapper.org/display.php?selected=227 HIV

Prevalence Rate

– The number of affected persons present in the population at a specific time divided by the number of persons in the population at that time

– Measures disease burden (diseases with long morbidity have higher prevalence)

– Point prevalence measures prevalence at a certain point

– Period prevalence measures prevalence during a certain period in time

But you can also present it as a percent

Or…

PR = A -----------------

A + B

A = number of cases of the diseaseB = number of people in the population who do not have the disease, but who are at risk for getting it.

Therefore A+B = total number of people in the population

Example: Obesity in the USA(from CDC.gov)

“Point” Prevalence• the proportion of people in a population who have a

disease or condition at a particular time, such as a particular date

• A “snap shot”

• PR = number of cases on a specific date ---------------------------------------------- number of popn at risk on that date

“Period” Prevalence• 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.

• PR = number of cases in that period ------------------------------------------------------------------ number of popn during that period

23% of this class is left handed

In 2008, 28% of Americans were clinically obese

Point prevalence

Period prevalence

Interview Question Type of Measure

"Do you currently have asthma?" Point prevalence

"Have you had asthma during the last (n) years?" Period prevalence

"Have you ever had asthma?" Cumulative incidence

Consider an old film camera…

A single click of the camera produces an image. The total photons on the film, or dots on the image, constitute the point prevalence.

If you leave the shutter open for a few seconds, and point the camera to an unmoving object, then many photons accumulate on the film. This is period prevalence.

If you use a movie camera and analyze individual frames, then the number of new events in each frame constitute the incidence rate for that frame.

Incidence and prevalence

Relationship between incidence and prevalence

• Prevalence = incidence x duration of disease– When rates are stable– When in-migration = out-migration

• Rates and proportions• Spatial distribution

Point prevalence for positive x-rays

Table 3-7. Hypothetical Example of Chest X-Ray Screening: II. Point Prevalence

Screened Population

No. with Positive X-Ray

Point Prevalence per 1,000 Population

1,000 Hitown 100 100

1,000 Lotown 60 60

Table 3-8. Hypothetical Example of Chest X-Ray Screening: III. Prevalence, Incidence, and Duration

Screened Population

Point Prevalence per 1,000

Incidence (Occurrences/yr)

Duration (yrs)

Hitown 100 4 25

Lotown 60 20 3

  Prevalence = Incidence × Duration  

Breast cancer incidence

What are the pitfalls of finding Inc/Prev Data?

• Biases?

Incidence/Prevalence Homework

• Page 28: #8, 9 and 10

Mortality

• Death rates

Mortality Rates

• the number of deaths (in general, or due to a specific cause) in some population, scaled to the size of that population, per unit time

• typically expressed in units of deaths per 1000 individuals per year

The crude death rate, the total number of deaths per year per 1000 people. The crude death rate for the whole world is currently about 8.24 per 1000 per year (according to the current CIA World Factbook.)

The perinatal mortality rate, the sum of neonatal deaths and fetal deaths (stillbirths) per 1000 births. (WHO -> 22 weeks pregnancy until 7 days of life)

The maternal mortality rate, the number of maternal deaths due to childbearing per 100,000 live births.

The infant mortality rate, the number of deaths of children less than 1 year old per 1000 live births.

The child mortality rate, the number of deaths of children less than 5 years old per 1000 live births.

The standardised mortality rate (SMR)- This represents a proportional comparison to the numbers of deaths that would have been expected if the population had been of a standard composition in terms of age, gender, etc.

The age-specific mortality rate (ASMR) - This refers to the total number of deaths per year per 1000 people of a given age (e.g. age 62 last birthday).

Infant, neonatal, and postneonatal mortality rates: United States, 1940-2005 (Wikipedia)

Google public data.

Selected leading causes of death, by sex in Canada   1997

  Number % Total Males Females

    Rate1

All causes 215,669 100.0 658.7 844.0 521.6

Cancers 58,703 27.2 181.5 229.7 148.5

Diseases of the heart 57,417 26.6 173.0 230.8 129.7

Cerebrovascular diseases 16,051 7.4 47.8 52.8 43.9

Chronic obstructive pulmonary diseases and allied conditions 9,618 4.5 29.0 44.5 20.1

Unintentional injuries 8,626 4.0 27.6 37.8 17.9

Pneumonia and influenza 8,032 3.7 23.7 31.5 19.2

Diabetes mellitus 5,699 2.6 17.4 20.6 14.8

Hereditary and degenerative diseases of the central nervous system 5,049 2.3 15.0 16.7 13.9

Diseases of arteries, arterioles and capillaries 4,767 2.2 14.3 19.5 10.6

Psychoses 4,645 2.2 13.6 13.3 13.4

Suicide 3,681 1.7 12.0 19.5 4.9

Where Do We Get These Data?

• Registries• Surveys/Studies• Surveillance

How Do We Get This Data?

• Surveillance– The monitoring of diseases in order to establish

patterns of progression

• Two types:– Active

• Going out and looking for diseases

– Passive• Sitting back and waiting for the diseases to be noticed

Active or Passive?

• Breast cancer screening programme

• Notifiable disease registry

Active

Passive

Why do we need surveillance?

Example: crude data from the Canadian Injuries Surveillance System:

Leading causes of death due to injury, 2004

-passive system based on automatic processing of death certificates and hospital charts

Example: Canadian Notifiable Disease Surveillance Report, June 2007

(Ontario is blank because no data was available from Ontario in June/2007)

Active system requiring doctors to call the government when certain illness arise

Last Thing: Ecological Studies

• Remember the ecological fallacy?

Ecological Study

• the unit of analysis is a population rather than an individual

• Very crude• Often draw associations between occupation,

environment and disease• Considered to be “hypothesis generating”

rather than “hypothesis testing”– Ie, they don’t answer questions, just ask more

Ecological Study

• Eg, a comparison of saccharine use time trends with time trends of bladder infections

• Eg, geographical distribution of farm animals compared with geographical clustering of asthma cases

• Eg, study that showed a period with increasing internet cable connections was correlated with a decrease in sexual assaults

Ecological Studies

• Cheap• Easy• Suggestive• Fun• dangerous