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Preventing a chronic disease: the individual approach

Ian McDowell, Paula Stewart

28 October 2008

Basic Concepts in Individual and Population Health (2)

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Reminder: SIM Web Site

www. medicine.uottawa.ca/SIM

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“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 thatis supporting her condition, and where best to intervene?

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The Broader Conception Disease(from session I)

Diagnosis

Therapy

Individualoutcome

SymptomsBiological onset of disease

Clinical PhasePreclinical Phase

Impact on familywork;

economic impact, etc.

Postclinical PhaseDeterminants RiskFactors

Socialcircumstances;

servicesavailable, etc.

Lifestyles(diet,

exercise,addictions,

etc.)

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Prevention Strategies

Secondary prevention,

or screening

Potential improvementby screening

Rehabilitation,Support(tertiary

prevention)

(Green words are links)

Diagnosis

Therapy

Individualoutcome

SymptomsBiological onset of disease

Clinical PhasePreclinical Phase

Impact on familywork;

economic impact, etc.

Postclinical PhaseDeterminants RiskFactors

Socialcircumstances;

servicesavailable, etc.

Lifestyles(diet,

exercise,addictions,

etc.)

Palliation(i.e. prevent

loss of quality of life)

Promoting health & primary prevention

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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.

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Example of a causal chain for arthritis, combining general and individual factors

Costs of alternatives

Diet & exercise patterns

Previous injury?

Work life & activities

Arthritis

Level of SusceptibilityEthnicity,

genetics, etc.?

Age, sex,socio-economic

status, etc.

Will breaking the linksbe sufficient to prevent

the disease?

Personal factorsEnvironmental factors

Economicinfluences

Local climatePatterns of foodsupply & pricing

Had to continue working?

Body weight

Culture

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Alternative way to think about etiological factors

Agent

HostEnvironment

(wear & tear?biochemical changes?)

(body weight;lifestyle activities, etc.)

(food availability& options, etc)

3 categories of factors to consider:

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

Some have the factor (c)

Population

(lapse of time)

Begin enquiry here& work forwards

Sample people without

the disease

Disease (a)

Disease (b)

No Disease

No Disease

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 factor95% CI are usually presented:

e.g., RR = 1.9 (95% CI 1.5, 2.3)

Note: as you beginwith people who do nothave the disease, youcan calculate incidencebut not prevalence

Some do not (d)

Outcomes

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Design (2): Retrospective Case-Control Study

Population

SelectCases

(have the disease)

Sample ofControls

(who do not have the disease)

Exposed (c)

Exposed (a)

Not Exposed (d)

Not Exposed (b)

Begin enquiry here& look backwards

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

Reviewhistory

Reviewhistory

Note: as you beginwith people who alreadyhave the disease, youcannot calculateincidence or prevalence

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In both designs, we compare ratesto try and identify causal factors

This may not be as simple as you would like…

Crucial concepts: Confounding and Standardization

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Hospital Separation Rate for Osteoarthritis by Age Group and SexCanada, 2005/06

0

200

400

600

800

1,000

1,200

1,400

1,600

Age Group

Sep

arat

ion

s p

er 1

00,0

00

Males Females

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

<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+

ICD 10: M15-M19Source: Public Health Agency of Canada, 2008 using Statistics Canada and Canadian Institute for Health Information Data.

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.

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Osteoarthritis Hospital SeparationsCanadian Trends Over Time

y = 0.0165x2 + 1.8782x + 61.112

R2 = 0.9243

y = 26.18x2 + 368.92x + 10904

R2 = 0.9648

0

50

100

150

200

250

1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007

Year

Sep

arat

ion

s p

er 1

00,0

00

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

Sep

arat

ion

s

SeparationsCrude RateAge Standardized RatePoly. (Age Standardized Rate)Poly. (Separations)

ICD10 codes: M15-M19.Standardized rate uses 1991 Canadian Population. Includes only the ten Canadian Provinces.Source: Public Health Agency of Canada, 2008 using Statistics Canada and Canadian Institute for Health Information Data.

Green line: crude rate; blue line = age-standardized. Purple = linear regression; red = curvilinear regression

The numbers of elderly people has been growing, so the mere aging of the population would increase

numbers with arthritis.

“Confounding” by age: hence a need for standardization.Death rates by age, per 1,000 population

Baltimore city, 1965

Race < 1 yr 1-4 yr 5-17 18-44 45-64 65+

White 23.9 0.7 0.4 2.5 15.2 69.3

Black 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

Allages

14.3

10.2

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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 age-group 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 age-structures of the 2 original populations.

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Osteoarthritis MortalityCanada, 1950-2004

0.0

0.1

0.2

0.3

0.4

0.5

0.6

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

Dea

ths

per

100

,000

0

20

40

60

80

100

120

140

160

180

200

Dea

ths

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.

Mortality from osteoarthritis, Canada, 1950-2004. The yellow bars show numbers of deaths, and the green line expresses this

as a rate per thousand. Blue line corrects for changing age structure.

Obesity?

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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Perceived Susceptibility to Disease

· Demographics (age, sex, ethnicity, etc.) · Personality, social class, etc. · Knowledge about the disease, etc.

Perceived Threat of the Disease

· Raised awareness (mass media, etc) · Personal advice (physician, etc)· Symptoms· Illness of family member or friend

Perceived benefits of taking action, minusPerceived barriers to

action

Likelihood of TakingRecommended Health Action

Modifying Factors

Perceived Severity of Disease

Cues to Action

Health Belief Model (originally by G.M. Hochbaum, 1958)

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?