Basic Concepts in Individual
and Population Health (2)
Preventing a chronic disease: the individual
approach
Ian McDowell, Paula Stewart
28 October 2008
1
Reminder: SIM Web Site
www. medicine.uottawa.ca/SIM
2
“Rickety Agnes”
71 year-old lady with
swollen & painful
joints.
• She is more concerned about her rent payments
than in losing weight.
• What balance of symptomatic treatment
versus tackling behavioral & environmental factors?
• How do we think about the chain of causation that
is supporting her condition, and where best to intervene?
3
The Broader Conception Disease
(from session I)
Determinants
Social
circumstances;
services
available, etc.
Risk
Factors
Preclinical
Phase
Clinical Phase
Postclinical Phase
Individual
outcome
Lifestyles
Biological
(diet,
onset of
exercise,
addictions, disease
etc.)
Symptoms
Therapy
Diagnosis
Impact on
family
work;
economic
impact, etc.
4
(Green words
are links)
Prevention Strategies
(tertiary
prevention)
Promoting health &
primary prevention
Determinants
Social
circumstances;
services
available, etc.
Risk
Factors
Rehabilitation,
Support
Preclinical
Phase
Clinical Phase
Postclinical Phase
Individual
outcome
Lifestyles
Biological
(diet,
onset of
exercise,
addictions, disease
etc.)
Symptoms
Therapy
Potential Diagnosis
improvement
by screening
Secondary
prevention,
or screening
Impact on
family
work;
economic
impact, etc.
Palliation
(i.e. prevent
loss of quality
of life)
5
Thinking about causes
• If we want to prevent disease, we need to modify truly
causal factors. How do we identify causes?
• There is never a single cause, but many levels of
interacting causal factors: ‘upstream determinants’
through to ‘proximal causes’
• Useful to distinguish “How?” questions (causal
mechanisms) from “Why?” questions (reasons why
something occurred)
• Biological science is good at the mechanisms. The goal of
‘nomothetic’ science is to derive general laws
• The ‘why’ questions seem more difficult; social sciences
seek to explain individual cases: ‘idiographic’ science.
• This also reflects the distinction between the causes of
cases, and determinants of incidence rates.
6
Example of a causal chain for arthritis, combining
general and individual factors
Personal factors
Environmental factors
Patterns of food
supply & pricing
Costs of alternatives
Culture
Local climate
Work life
& activities
Economic
influences
Diet &
exercise
patterns
Body
weight
Will breaking the links
be sufficient to prevent
the disease?
Age, sex,
socio-economic
status, etc.
Had to
continue
working?
Ethnicity,
genetics, etc.?
Previous
injury?
Level of Susceptibility
Arthritis
7
Alternative way to think about
etiological factors
3 categories of
factors to
consider:
Environment
(food availability
& options, etc)
Agent
(wear & tear?
biochemical changes?)
Host
(body weight;
lifestyle activities, etc.)
Cf. Fireman’s mantra: a fire requires air, fuel and heat
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“Why?” questions:
Determinants of Health
• “Determinants” a widely used term; somewhat vague
• Refers to background causal influences that affect the
general level of health in a population (“Why do women
live longer than men?”)
• Often refer to broad forces that are difficult to alter
• Determinants predict incidence rates in populations, but
don’t specify mechanisms
• Individual variation from population rate is influenced by
“risk factors”
• Determinants closely linked to theme of population
health and will return in the third session in this series.
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Determinants of Health (Health Canada’s list)
• Biology
• Personal health practices; social support
• Environmental quality
– physical hazards (quality of air, water, food
production, roads, …)
– socio-economic (work opportunities, social
networks, community norms,….)
• Public policies/legislation
– income, housing, taxation, speed limits….
• Health and social services (type, quality,
access)
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Two philosophical approaches to explanation
•
•
•
•
Aristotelian
To make facts
teleologically
understandable
Applied to actions &
intentional agency
“Why?” questions
Used in human & social
sciences
•
•
•
•
•
Galilean
To explain & predict
Commonly applied to
events
Causal mechanisms
Generally “how?”
questions
Used in natural
sciences
Both are relevant to medicine. If you are going to
treat Agnes successfully, you need to understand why she
behaves as she does, not just how her arthritis grows.
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Enough philosophy... let’s take a practical approach!
Criteria to assess causation
• Temporality (cause should precede effect)
• Strength of association (weak causes unlikely to produce
major effects)
• Dose-response (is there a gradient of effect?)
• Reversibility (if cause removed, does effect disappear?)
• Consistency (does it happen in every study?)
• Biological plausibility (how may it work?)
• Specificity (does only this factor produce the effect?)
• Analogy (have you seen similar effects elsewhere?)
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Study Designs for Identifying
Causal Factors
Observational designs:
• Cohort (a.k.a. ‘longitudinal’ or ‘follow-up’) study
• Case-control study
Experimental designs:
• Randomized controlled trial
• Quasi-experimental studies
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Observational Design (1)
Prospective Cohort Study
Begin enquiry here
& work forwards
Population
Sample people
without
the disease
Outcomes
Disease (a)
Some have the
factor (c)
No Disease
Disease (b)
Some do not (d)
No Disease
(lapse of time)
Statistic = Relative Risk [RR] = (a/c) divided by (b/d)
(= ratio of incidence in exposed
compared to non-exposed)
RR > 1 implies a hazard;
RR < 1 implies a protective factor
95% CI are usually presented:
e.g., RR = 1.9 (95% CI 1.5, 2.3)
Note: as you begin
with people who do not
have the disease, you
can calculate incidence
but not prevalence
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Design (2): Retrospective Case-Control Study
Begin enquiry here
& look backwards
Exposed (a)
Review
history
Not Exposed (b)
Exposed (c)
Not Exposed (d)
Review
history
Select
Cases
(have the
disease)
Sample of
Controls
(who do not
have the
disease)
Statistic = Odds Ratio [OR] = (a/b) divided by (c/d)
This shows how many times more likely were the cases
to have been exposed than the controls.
OR values interpreted in same way as RR
Population
Note: as you begin
with people who already
have the disease, you
cannot calculate
incidence or prevalence
15
In both designs, we compare rates
to try and identify causal factors
This may not be as simple as you would like…
Crucial concepts:
Confounding and Standardization
16
Osteoarthritis is a disease of elderly people.
If the population is getting older, this will complicate a comparison of change in the
disease over time.
Hospital Separation Rate for Osteoarthritis by Age Group and Sex
Canada, 2005/06
1,600
1,400
Separations per 100,000
1,200
1,000
800
600
400
200
0
<1
1-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
Males
0
0
0
0
1
2
4
9
18
42
85
163
290
495
759
1,017
1,084
885
482
Females
0
0
0
0
1
2
3
9
14
32
76
185
380
639
950
1,263
1,385
1,107
533
Age Group
Males
Females
ICD 10: M15-M19
Source: Public Health Agency of Canada, 2008 using Statistics Canada and Canadian Institute for Health Information Data.
17
The numbers of elderly people has been growing,
y = 26.18xnumbers
+ 368.92x + 10904
so the mere aging of the population would increase
Osteoarthritis Hospital Separations
R = 0.9648
with
arthritis.
Canadian Trends Over Time
2
2
250
70,000
60,000
Green line: crude rate; blue line = age-standardized.
Purple = linear regression; red = curvilinear regression
50,000
150
40,000
30,000
100
Separations
Separations per 100,000
200
20,000
50
10,000
0
0
1971
1973 1975
1977
1979 1981
1983 1985
1987
1989 1991
Year
1993
1995 1997
1999 2001
2003
2005 2007
y = 0.0165x 2 + 1.8782x + 61.112
R2 = 0.9243
Separations
Crude Rate
Age Standardized Rate
ICD10 codes: M15-M19.
Poly. (Age Standardized Rate)
Poly.
Standardized rate uses 1991 Canadian Population. Includes only
the(Separations)
ten Canadian Provinces.
Source: Public Health Agency of Canada, 2008 using Statistics Canada and Canadian Institute for Health Information Data.
18
“Confounding” by age: hence a need for standardization.
Death rates by age, per 1,000 population
Baltimore city, 1965
Race
All
ages
< 1 yr
1-4 yr
5-17
18-44
45-64
65+
White
14.3
23.9
0.7
0.4
2.5
15.2
69.3
Black
10.2
31.3
1.6
0.6
4.8
22.6
75.9
Note: whites have higher
overall rate, even though
they have lower rates in
each age-group!
This paradox arises because of the
much higher mortality rates
in the 65+ age-group, and because
fewer blacks reach this age,
so contribute fewer
cases overall
So, What Do We Do?
Answer: calculate death rates in each age
(maybe also sex) group separately.
This is called ‘standardization’, or
‘adjustment’, of the rates.
Imagine you want to compare two or more
populations to identify a causal factor.
Standardization removes the confounding
effects of extraneous variables (most
often differences in age between the
populations).
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How do you do this?
1. Classify each population into age groups and
calculate rates (here, mortality) separately for
each age-group in the two populations
2. Apply these rates to the corresponding agegroup in a standard (reference) population,
normally the whole country, and work out how
many deaths will occur
3. This produces two hypothetical sets of
mortality figures, but they are now comparable
because you have removed the different agestructures of the 2 original populations.
21
Mortality from osteoarthritis, Canada, 1950-2004.
The yellow bars show numbers of deaths, and the green line expresses this
Osteoarthritis Mortality
as a rate per thousand. Blue line
corrects
Canada,
1950-2004 for changing age structure.
0.6
200
180
0.5
160
120
0.3
100
Deaths
Deaths per 100,000
140
0.4
80
0.2
60
40
0.1
20
0.0
0
1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004
Year
Deaths
Crude Rate
Age Standardized Rate
ICD10 codes: M15-M19. Note that the coding schemes for this condition changed in 1968, 1978 and 2000 and this may influence trends.
Standardized rate uses 1991 Canadian Population.
Source: Centre for Chronic Disease Prevention and Control, Public Health Agency of Canada, 2007 using Statistics Canada, Vital Statistics Data.
22
Back to Arthritic Agnes...
How can we influence her behaviour?
– Give her advice? Hmmm...
– Peer influence? How to arrange?
– Top down: government policy, legislation, etc? [We’ll
discuss this in the third lecture]
Models for understanding unhealthy behaviours
– Health belief model - cognitive
• Describes ‘predisposing’, ‘enabling’ and ‘reinforcing’ factors
– Stages of change model
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Health Belief Model (originally by G.M. Hochbaum, 1958)
Perceived Susceptibility
to Disease
Modifying Factors
· Demographics (age, sex, ethnicity, etc.)
· Personality, social class, etc.
· Knowledge about the disease, etc.
Perceived Severity
of Disease
Perceived Threat
of the Disease
Perceived benefits of
taking action, minus
Perceived barriers to
action
Cues to Action
· Raised awareness (mass media, etc)
· Personal advice (physician, etc)
· Symptoms
· Illness of family member or friend
Likelihood of Taking
Recommended Health Action
Stages of Change
(J. Prochaska, 1985)
• Pre-contemplation (no intention of
changing)
• Contemplation (intends to act +/- 6
months)
• Readiness for action (preparing for
change in immediate future)
• Action (is making, or has made changes)
• Maintenance (working to prevent relapse)
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Other ideas for individual
behaviour change
• Health Risk Appraisal
– A computerized way to present patients with
information on their health risks that also
computes the potential survival benefits of altering
their health behaviours (e.g., if you quit smoking,
this is how much longer you can expect to live).
• Patient decision aids
– Invented in Ottawa, a systematic way to help
patients reach difficult decisions (e.g., whether to
have surgical or medical treatment) that require
balancing information on risks and benefits.
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Buzz Groups
Maintaining a healthy body weight among adults
• What are the predisposing factors?
• What are the barriers?
• What are the enabling factors?
• What are the reinforcing factors?
• For each one, how would you intervene to
improve the factor? What is the doctor’s role in
such action?
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