m2 medical epidemiology how to fairly compare disease frequencies between groups

M2 Medical Epidemiology

How to Fairly Compare Disease Frequencies Between Groups

How to Fairly Compare Disease Frequencies Between Groups

Simple epidemiologic indices: review/summary Interpreting epidemiologic comparisons:

overview– chance– bias– confounding

Adjustment of epidemiologic indices for confounding– direct– indirect

Simple epidemiologic indices: review/summary

Questions: What fraction of a group has the condition

now? What fraction of the community carries the

condition at any one time? What is the endemic level of the condition,

relative to the size of a community? What fraction of UI students has hay fever

now?


Answer: Point prevalence, or prevalence for short. A dimensionless proportion. Sometimes erroneously called “prevalence

rate”


Questions:

What is the cumulative risk (probability) of developing a condition at least once during a fixed time period?

What fraction of a group can we predict will have developed a condition over a given time period, or during an epidemic?

Why must I take this medicine, doctor? What are my chances of a heart attack in the next ten years, if I don't?


Answers: Cumulative incidence A dimensionless proportion Called the attack rate when describing

infectious disease outbreaks, – e.g., The attack rate in the county during the West

Branch hepatitis outbreak was estimated as 6.5%=65 cases/1000 population.

One women in 11 (9%) is expected to develop breast cancer during her lifetime.

Simple epidemiologic indices: review/summary Questions:

How strong is the process causing new cases? How many new cases occur per person per unit

time, or other unit of experience (e.g., per passenger-trip, per passenger-mile traveled)?

How many new cases of esophageal cancer occur in Illinois/1000 population per year?

How many ruptured spleens occur from automotive accidents in Illinois, per million person-miles traveled?

How many new HIV infections occur per 1000 acts of vaginal intercourse? Of anal intercourse?


Answer: Incidence density (rate, dimension = new

cases per unit of experience, such as person-year, passenger-mile, sexual acts) – e.g. 5 new cases per 1000 persons per year =

– 5 new cases per 1000 person-years =

– .005 new cases per person per year

Units = e.g.

– New cases / [persons x years]

– New cases / million passenger-miles

– New cases / 100 sexual acts


Examples: Mortality rate: The death density, i.e. the

incidence density of death.

For political units in which records are kept routinely and where the population size may be constantly changing, often calculated using the mid-year population as denominator.

The mid-year population approximates the total person-years exposure in the population for the full year.


Examples: Case-Fatality rate: The cumulative incidence

of death due to a disease, during the course of the disease.

i.e. the fraction of cases which result in death from the illness.

Equivalently, the chance of dying from a case of the disease.

Case Fatality Rate

The cumulative incidence of death due to a disease, during the course of the disease.

i.e. the fraction of cases which result in death from the illness.

Equivalently, the chance of dying from a case of the disease.

Number of deaths from a specific disease/number of cases of the disease.

Usually overestimates. Why?


Type ofincidence

Cumulative Density

Numerator Number of new cases

Denominator Population at risk atbeginning of interval

Person-years at risk, sometimesapproximated by population atrisk at midperiod

Assumptions Constant populationobserved over entireperiod

Constant risk over observationspan, so that 1 person observedfor two years gives sameinformation as 2 persons observedfor 1 year

Uses To estimate risk forspecified period andprognosis afterdisease event

For inference about causalprocesses; to adjust for differentlengths of observation for eachindividual, loss to follow-up,


Incidence density (ID) vs. Cumulative incidence (CI)

Question:

In a population of 100 persons, deaths occur at the rate (incidence density) of 52 per 100 person-years or, equivalently, 1 per 100 person-weeks.

After one year of this, what proportion of the 100 people will have died?


Answer:

41%,

not 52%


For all factors stable,

P = ID x MD

where

P = Prevalence

MD = Mean Duration

Example

If incidence is 12 new cases per 1000 person-years.

And duration of illness is 6 months. What is the average prevalence? 6 per thousand


Relative Risk = RReither

CUMULATIVE INCIDENCE RATIOCIR = CI1/CI0

or

INCIDENCE DENSITY RATIO IDR = ID1/ID0

Association: a statistical feature of comparisons(s), with six possible explanations:

Causation, with exposure promoting disease Chance Bias : 2 categories

Selection Bias

Measurement bias Confounding variable(s) Causation, with disease promoting appearance of

the exposure

Always ask: are there plausible alternative explanations for the data?

Chance

due to random variation from sampling or measurement

addressed using– statistical tests of hypotheses (p-values)– confidence intervals– power analyses

Bias. 2 types

Selection, the way you selected subjects for the study biased your results.

Measurement, the way you measured variables in your subjects biased the results.

Selection bias

Bias from the use of a non-representative group as the basis of generalization to a broader population of subjects or patients.

For instance, a common bias of this type appears when – the prognosis of patients newly diagnosed with a

given disease is inferred from the study of hospitalized patients with this disease at a major referral center,

and

– the disease in question has a broad spectrum behavior.

Selection bias

More commonly We have 2 groups Exposed and unexposed We compare them with regards to an

outcome. But the way we selected the 2 groups causes

differences in the outcome that have nothing to do with the exposure.

Example if we used hospitalized smokers as the exposed and healthy volunteer non-smokers as the unexposed.

Selection Bias (Admission Rate -- Berkson)

DISEASES OF BONES AND ORGANS OF MOVEMENT VS.RESPIRATORY DISEASE: TWO CROSS-SECTIONAL COMPARISONS

DISEASES OF BONES AND ORGANS OFMOVEMENT

GENERAL POPULATION

THOSE HOSPITALIZED

IN PRIOR 6 MONTHS

YES NO TOT. YES NO TOT.

YES 17 207 234 5 15 20RESPIRATORYDISEASE

NO 184 2376 2560 18 219 237

TOTAL 201 2583 2784 23 234 257

ODDS-RATIO = 1.06 ODDS-RATIO =4.06

1ADAPTED FROM ROBERTS RS, SPITZER WO, DELMORE T, AND SACKETT DL. JCHRON DIS 31:119-28.

CANCER CASES

CONTROLS

PRESENT

54

133

TB ABSENT

762

683

TOTAL

816

816

% WITH TB

6.6

16.3

EOR = 0.36, P<.001 (CHI-SQUARE)

Selection Bias (Berkson) Necropsies

More Selection Biases Whenever we compare a group of

patients who use a drug to those who don’t in a non experimental observational study (cohort, not randomized).

The 2 groups differ in many respects. One of the most important respects is

that the patients on the drug have a reason to be on it (indication). The others don’t. Called “Bias by indication”.

Bias by indication

For example calcium channel blockers have 2 indications hypertension and coronary disease.

If you compare hypertensive patients who are on Ca blockers to those who are on other agents (not randomized, totally at the discretion of their doctors), we would find:

Bias by indication

Patient on Ca blockers have higher prevalence of CAD

Also higher prevalence of risk factors for CAD

So if you do an observational study of hypertensive patients, comparing the outcome in those on Ca blockers to those on other agents, you may find

Bias by indication

That patients on Ca blockers have much worse outcomes.

This is bias by indication. You can adjust and correct for

preexisting heart disease and for risk factors, but may not be enough.

Bias by indication

If you compare hypertensive patients who are on minoxidil or hydralazine to those on other agents you find

That patients on those agents have higher BP Is it because they don’t work as well ? No, the opposite. They are reserved for those

with severe resistant hypertension. That is the indication for those agents.

Survivor Treatment Bias

Patients who received statin during admission for MI had much lower in-hospital mortality.

Statin? The ones who died are different. Some died very soon after admission

(no statin).

Competing Medical Issues Bias

Some were so sick that they were treated with multiple drugs, modalities, ICU etc.

No statin

Bias by contraindication

If you compare hypertensive patients who are on beta blockers to those on other agents you find that they have better outcomes.

That does not mean they are better for you. No, this comparison is biased by contraindication.

Beta blockers are contraindicated in severe COPD, CHF, PVD etc.

Measurement bias

Systematic or non-uniform failure of a measurement process to accurately represent the measurement target, e.g.

– different approaches to questioning, when determining past exposures in a case-control study.

– more complete medical history and physical examination of subjects who have been exposed to an agent suspected of causing a disease than of those who haven't been exposed to the agent.

INFLUENCE OF INTENSITY OF SEARCHING FOREXPOSURE UPON REPORTED PROPORTIONS EXPOSED

PRIOR EXPOSURE TO IRRADIATION

STUDY

ROUTINEQUESTIONING &RECORD SEARCH

INTENSIVEQUESTIONINGAND RECORD

SEARCH

36 CASES OFNISHIYAMA ETAL.1

28 47

22 CASES OFRAVENTOS ETAL.2

0 50

1Nishayama, Schmidt, And Batsakis, J Amer Med Assoc 181:1034-38.

1Raventos, Horn, And Ravdin, J Clin Endocr Metab 22:886-91.

Measurement Bias -- Recall Bias

Measurement Bias

Family information bias

The flow of family information about exposures and illnesses

is stimulated by and directed to a new case in its midst.

REPORTEDPARENTAL HISTORY

WITHRHEUMATOIDARTHRITIS (%)

WITHOUTRHEUMATOIDARTHRITIS (%)

NEITHER PARENT 27 50

ONE PARENT 58 42

BOTH PARENTS 15 8

TOTAL 100 1001ADOPTED FROM SCHULL AND COBB, J CHRON DIS 22:217-22.

Measurement Bias

EFFECT OF THE SOURCE OF FAMILY INFORMATION UPONTHE RESULTS OF THE FAMILY HISTORY

SIBLING PROVIDING FAMILY HISTORY

REPORTEDPARENTAL HISTORY

WITHRHEUMATOIDARTHRITIS (%)

WITHOUTRHEUMATOIDARTHRITIS (%)

NEITHER PARENT 27 50

ONE PARENT 58 42

BOTH PARENTS 15 8

TOTAL 100 1001ADOPTED FROM SCHULL AND COBB, J CHRON DIS 22:217-22.

Measurement Bias -- Family Information

Avoid confounding

Confounding refers to distortion of the true biologic relation between an exposure and a disease outcome of interest, due to a research design and analysis that fail to properly account for additional variables associated with both. Such variables are referred to as confounders or, less formally, as lurking variables.

ONE-MONTH INFANT SURVIVAL STATUS

SURVIVAL

AMOUNT OFCARE

DEAD ALIVE TOTAL MORTALITY(%)

LESS 20 373 393 5.1

MORE 6 316 322 1.9

TOTAL 26 689 715 3.6

Confounding

ON ONE-MONTH INFANT SURVIVAL STATUS: CLINIC A

SURVIVAL

AMOUNT OFCARE


LESS 3 176 179 1.7

MORE 4 293 297 1.4

TOTAL 7 469 476 1.5

ONE-MONTH INFANT SURVIVAL STATUS: CLINIC B

SURVIVAL

AMOUNT OFCARE


LESS 17 197 214 7.9

MORE 2 23 25 8.0

TOTAL 19 220 239 8.0

Confounding

COMMUNITY A (E.G. STATE OF ILLINOIS)AGE GROUP NUMBER IN

COMMUNITYDEATHSIN YEAR

MORTALITY(% PERYEAR)

YOUNGER 70,000 700 1%OLDER 30,000 3000 10%TOTAL 100,000 3700 3.7%

COMMUNITY B (E.G. DANVILLE)AGE GROUP NUMBER IN

COMMUNITYDEATHSIN YEAR

MORTALITY(% PERYEAR)

YOUNGER 30,000 300 1%OLDER 70,000 7000 10%TOTAL 100,000 7300 7.3%

Confounding

CITY A

CITY APOPULATION

OBSERVEDDEATHS

CITY A MORTALITYRATE (PER 100,000 P-Y)

TOTAL 300,000 54 54/3 =18

CITY B

CITY BPOPULATION

OBSERVED DEATHS CITY BMORTALITY RATE

(PER 100,000 P-Y)

TOTAL 100,000 22 22

Direct Rate Adjustment

CITY A CITY B

AGEGROUP

Numberin

POPULATION

Numberof Deaths

MortalityRate(PER

100,000P-Y)

Numberin

POPULATION

Numberof Deaths

MortalityRate(PER

100,000P-Y)

0-19 60,000 12 20 20,000 3 15

20-50 180,000 18 10 20,000 1 5

>50 60,000 24 40 60,000 18 30

TOTAL 300,000 54 18 100,000 22 22

Age specific mortality rate

CITY A CITY B

AGE GROUP

Standard Population

Age-specific

Mortality Rate

(/100K)

Expected Deaths

Age-specific

Mortality Rate

(/100K)

Expected Deaths

0-19

20,000

20

4

15

3

20-50

40,000

10

4

5

2

>50

40,000

40

16

30

12

TOTAL

100%

24

17


ANY CITY

AGE GROUP

Standard Population

Age-specific

Mortality Rate

Expected Deaths (PER

100,000 P-Y)

0-19

20%

…

…X0.2

=…

20-50

40%

…

…X0.4

=…

>50

40%

…

…X0.4

=…

TOTAL

100%

Age adjusted mortality

rate



CITY A

AGE GROUP

STANDARD POPULATION

CITY A AGE-SPECIFIC

MORTALITY RATE

OBSERVED DEATHS (PER

100,000 P-Y)

0-19

50%

20/100,000 P-Y

10

20-50

30%

10/ "

3

>50

20%

40/ "

8

TOTAL

100%


CITY A

AGE GROUP

STANDARD POPULATION

CITY A AGE-SPECIFIC

MORTALITY RATE


100,000 P-Y)

0-19

50%

20/100,000 P-Y

10

20-50

30%

10/ "

3

>50

20%

40/ "

8

TOTAL

100%

21

DIRECTLY ADJUSTED RATE=DAR=21/100,000 P-Y


CITY B

AGE GROUP

STANDARD POPULATION

CITY B AGE-SPECIFIC

MORTALITY RATE


100,000 P-Y)

0-19

50%

15/100,000 P-Y

7.5

20-50

30%

5/ "

1.5

>50

20%

30/ "

6

TOTAL

100%

15

DIRECTLY ADJUSTED RATE=DAR=15/100,000 P-Y


CITY A

AGE GROUP

STANDARD POPULATION

CITY A AGE-SPECIFIC

MORTALITY RATE


100,000 P-Y)

0-19

33.3%

20/100,000 P-Y

6.67

20-50

33.3%

10/ "

3.33

>50

33.3%

40/ "

13.33

TOTAL

100%

23.33

OR, DAR=(1/3)(20+10+40)PER 100,000P-Y=23.3/100,000 P-Y


CITY B

AGE GROUP

STANDARD POPULATION

CITY B AGE-SPECIFIC

MORTALITY RATE


100,000 P-Y)

0-19

33.3%

15/100,000 P-Y

5

20-50

33.3%

5/ "

1.66

>50

33.3%

30/ "

10

TOTAL

100%

16.67

OR, DAR=(1/3)(15+5+30) PER 100,000 P-Y=16.7/100,000 P-Y

Indirect Rate Adjustment

Calculate “Expected Deaths”

Divide Observed Deaths by Expected

Deaths (O/E)

SMR (Standardized Mortality Ratio)


Calculate SMR standardized mortality ratio.

SMR = Observed mortality / Expected mortality

To Calculate that you need to calculate expected mortality.


STANDARD POPULATION MORTALITY = 28/100,000 P-Y

0-19 year old: 24/100k ; 20-50: 16/100k; >50: 50/100k

CITY A

AGE GROUP

CITY A

POPULATION

STANDARD POPULATION AGE-SPECIFIC MORTALITY

RATE

EXPECTED

DEATHS (PER 100,000

P-Y)

0-19

60K

20-50

180K

>50

60K

TOTAL

300K


STANDARD POPULATION MORTALITY = 28/100,000 P-Y

0-19: 24; 20-50: 16; >50: 50

CITY A

AGE GROUP

CITY A

POPULATION


RATE

EXPECTED

DEATHS (PER 100,000

P-Y)

0-19

60K

24/100,000 P-Y

14.4

20-50

180K

16/ "

28.8

>50

60K

50/ "

30.0

TOTAL

100%

73.2


Calculate “Expected Deaths”

Divide Observed Deaths by Expected

Deaths (O/E)

SMR (Standardized Mortality Ratio)


STANDARDIZED MORTALITY RATIO (SMR) =

OBSERVED DEATHS/EXPECTED DEATH

54/73.2 = 74%

Indirect Rate AdjustmentSTANDARD POPULATION MORTALITY = 28/100,000 P-Y 0-19: 24; 20-50: 16; >50: 50

CITY B

AGE GROUP

CITY B

POPULATION


RATE

EXPECTED

DEATHS (PER 100,000

P-Y)

0-19

20K

24/100,000 P-Y

4.8

20-50

20K

16/ "

3.2

>50

60K

50/ "

30

TOTAL

100%

38.0


STANDARDIZED MORTALITY RATIO (SMR) =

OBSERVED DEATHS/EXPECTED DEATHS

22/38 = 58%

Proportional Mortality

The 4 leading causes of death in Chamapign County are….

CAD is the leading cause being responsible for 32% of all deaths in the County in 2002.

Proportional Mortality

Number of deaths from a specific cause/ Total number of deaths in same time

Proportional Mortality RatioPMR

Proportional Mortality Ratio

Proportion of deaths from specified cause /Proportion of deaths from specified cause in comparison population

Proportional Mortality RatioPMR

CAD is responsible for 32% of all deaths in the County in 2002. (Compared to 40% in the State of Illinois)

PMR = 32%/40% = 32/40 = 0.8 Is that good or bad ?

PMRRelative frequency of other causes of death can

affect the PMR for the cause of interest

An epidemic of a fatal disease in your population will decrease PMR for all other causes

Low mortality from a very common cause (CAD for example) in your population will increase PMR for all other causes

PMR

Fast, easy, cheap Can be calculated when all you have is

death certificates Don’t need information on demography

of population. “Leading Causes of Death”

Cause of death Percent of total deaths1 Diseases of heart 31.02 Malignant neoplasms, includingneoplasms of lymphatic andhematopoietic tissues

23.2

3 Cerebrovascular diseases 6.84 Chronic obstructive pulmonarydiseasesand allied conditions .

4.8

5 Accidents and adverse effects. . . Motor vehicle accidents. . . All other accidents and adverseeffects

4.21.92.3

6 Pneumonia and influenza 3.97 Diabetes mellitus. 2.88 Suicide 1.39 Nephritis, nephrotic syndrome,and nephrosis

1.1

10 Chronic liver disease andcirrhosis

1.1

How does one decide whether to present a set of data using crude, adjusted, or category-specific indices?

If possible, use crude indices only to produce a quick picture of the magnitude of a problem in a population, for the purpose of establishing a prima facie need for public health and/or medical services, and as a first-cut at estimating the resources needed.


Use category-specific indices when you wish to focus attention on the problem in one or a few population subgroups, when space is available to give a detailed presentation in order to communicate the fullest understanding of the data, and especially if specific indices vary between two populations being compared in a different manner in different population subgroups (e.g. effects are modified by age, sex or race).


Use adjusted rates when – you wish to avoid possible confounding, – but do not have the space to present the full

schedules of specific indices, or your audience does not have the patience for that,

Avoid adjusted rates when– there variable being adjusted out is an

“effect modifier,” that is, the relationship between groups being compared changes from stratum to stratum -- more later on this.


Note that crude indices require one only to know the numerator

cases and the denominator (population size or exposure-time) of each total population to be compared;

indirect adjustment requires knowledge of only the numerator cases from the total populations and the (joint) distributions of confounder(s) in the populations to be compared;

direct adjustment and specific rates require knowledge of both the numerator cases and the corresponding denominators within levels of the confounding variable(s), for all populations under comparison.

Note that

A directly adjusted rate of a single community means nothing by itself. It is only used to compare different communities and only if all of them are adjusted to the same standard population.

SMR of a single community IS useful. It does by itself compare 2 populations.

m2 medical epidemiology how to fairly compare disease frequencies between groups

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