Chapter 02 Concepts and Methods in Physical Activity Epidemiology.

Chapter 02

Concepts and Methods in Physical Activity Epidemiology


Epidemiologists – Use the application of scientific method to the study of the distribution and dynamics of disease in a population for the purposes of identifying factors that affect this distribution and then seek to modify the risks.– These risks are called risk factors. (ex. Physical Inactivity is a

risk factor for CHD)

Risk Factors – Increase the likelihood of morbidity (sickness) and premature mortality (death). Thus, risk factors increase the probability of disease in a group of individuals who have that risk factor (characteristic) vs. those who do not.


Epidemiology has three distinct goals:– To describe the distribution of disease– To identify risk factor associated with the disease– To prevent disease occurrence by modifying risk factors

(Small Group Activity) Read information on John Snow in text, p. 18.– How did Dr. Snow describe the distribution of disease– How did he identify the risk factor associated with disease– How did he prevent the disease occurrence by modifying the risk

factor?

Web Resources

www.cdc.gov/epiinfo/ - This site describes Epi Info, a series of programs for Microsoft Windows developed by the Centers for Disease Control and Prevention for use by public health professionals in conducting outbreak investigations and managing databases and statistics for public health surveillance (Dean 1999; Dean et al. 1996). Click “Download” to see further instructions for downloading Epi Info to a personal computer, or browse the Epi Info pages to learn more about the programs. Concepts and Methods in

Physical Activity Epidemiology

http://www.cdc.gov/epiinfo/


Physical Activity Epidemiology– Physical activity epidemiology studies factors associated with

participation in a specific behavior— physical activity —and how this behavior relates to the probability of disease or injury.

– Examples of this type of study include:– description of the level of physical activity in a population, – comparison of levels of physical activity among populations, – determination of factors associated with participation in physical

activity, – and investigation of the association between physical activity

and the risk for chronic diseases such as coronary heart disease (CHD), stroke, diabetes, osteoporosis, and cancer.


Epidemiologic Measures A fundamental measurement in epidemiology is the

frequency with which an event under study occurs, usually an injury, disease, or cause of death in a population.

– Incidence: Are the numbers of new cases of disease or injury during the time of study (period of interest). How many new cases of “X” occurred in the population from 2000-2010? Thus, incidence infers that these are changes in health status.

– Prevalence: Are the numbers of persisting cases during the time of study. Prevalence of disease is a function of both incidence and duration. Thus, prevalence of disease can increase can increase as a result of an increase in incidence or the duration of time disease is present before recovery or death.

If the incidence or prevalence of a condition is known, the incidence rates and prevalence rates can be calculated. (see next slide)


Calculation of Incidence and Prevalence Rates– Frequency (number of events) per unit time divided by the

average size of the population at risk (average size is determined at the midpoint time of the study)

RATE = Number of events

Average population size

If the incident rate was 115 new cases out of 10,000 persons, the rate would also be 11.5 per 1000, and 1.15 per 100, or 1.15%.

– Incidence rates provide a measure of the rate at which people contract disease over a specific time period.

– Prevalence rates indicate the rate of people who were diseased over a specific time period.

– Rates can also be assessed for risk factors ( How many “new” persons engaged in an “unhealthy” behavior)


Prevalence rates give a “snapshot” of the population at a given time, and may be useful in determining proper intervention and treatment protocols.

Incidence rates give a “snapshot” of the changes in disease state ( increase, decrease, unchanged).

Prevalence rates are not useful when determining factors that may increase the probability of disease, because high prevalence does not necessarily equal high risk. High prevalence may be due to increased survival rates (decreased death rates, rapid cure). Accordingly, low prevalence rates may be due to higher death rates.

Think about it…It is particularly important to be sure that the information you use to make

comparisons among groups is based on actual rates:

For example, a sports medicine physician reports that he has seen 100 cases of ruptured patellar tendons in runners over the past year. Does this indicate that running is the cause of this problem and that indeed it is a large problem that needs to be dealt with? The answer is that, with information only on the number of cases (numerator) and no information regarding the number of people at risk (denominator), it is impossible to tell. To make these assessments, you need to know how many runners visited the clinic over the course of the year. If 100 runners were seen and 100 cases of ruptured patellar tendons were diagnosed, then the incidence would be 100%, a potentially serious problem! On the other hand, if 1000 runners were seen, the rate would be only 10%, requiring a completely different interpretation.


Crude, Specific, and Standardized Rates Standardizing rates between populations allows for comparisons in the

rates of incidence and prevalence between the two populations.

– Crude Rates: Rates that are based on a total population without consideration of any of the population characteristics.

– Specific Rates: Rates that are calculated separately for population subgroups.

– Standardized Rates: Crude rates that are adjusted for some population characteristic to allow valid comparisons of rates among populations where the distribution of the characteristic (disease) may be different. Standardized rates are crude rates that have been adjusted to control for the effect of some population characteristic.TO MAKE COMPARISONS OF RATES BETWEEN TWO POPULATIONS WITH UNEQUAL DISTRIBUTIONS OF RISK FACTORS, STANDARDIZED RATES SHOULD BE USED.


Example – Table 2.1, p.20. (Direct Standardization)

– Population age ranges are unequal in the eldest and youngest, where the rate is the greatest, and least, thus effecting the crude rate

– The age categories are adjusted for number, and allows us to make comparisons using the standardized rates in the lower table.

– These are adjusted rates are used for comparison only. “All things being equal…the death rate in population “B” would be twice that of “A”.” (note this conclusion is the opposite we would attain if using only crude rates)

– To make a valid comparison of the death rates in these two populations, it is necessary to adjust death rates to account for the difference in age distribution.

A note about Adjusted Rates…

Though standardized rates are useful for making valid comparisons across populations, it must be remembered that they are fictional rates. The adjusted rates can vary, depending on the standard population that is used in the adjustment process. Therefore, the adjusted rate can be misleading and should be used only for comparison purposes.



Research Design in Epidemiology

– Design: the way that participants are grouped and compared according to behavior or attributes (e.g., physical activity or fitness), the health-related events being studied, time, and factors other than physical activity or fitness that could explain the occurrence of health-related events.

The goal of a design in physical activity epidemiologic research is to make sure that comparisons of groups based on differences in physical activity or fitness are not biased by other factors.

In other words, the research design used determines whether it is reasonable to infer that physical inactivity was a direct, or the only, explanation for the occurrence of an injury, disease, or death.


Research Design in Epidemiology

– Variables: Independent – What is being manipulated (changed) ? Dependent – What is being measured ?

– When change in the independent variable is manipulated by the investigator, the design is experimental.

– When the independent and dependent variables are observed or manipulated across a period of time, the design is longitudinal or prospective.

– When the study looks back in time after the occurrence of injury, disease, or death in an attempt to reconstruct an influencing factor, such as physical activity habits, the design is retrospective.

– Observational design is when the change in the dependent variable occurs as a result of natural history ( self-initiated by the people being studied).


– Several types of research designs are commonly used in epidemiologic research: cross-sectional surveys, case–control studies, cohort studies, and randomized controlled trials ( See Table 2.2, p. 21).

The design employed in any particular study depends on the questions to be answered, the time and financial resources available, and the availability of data.

– The major advantages and disadvantages of the commonly used epidemiologic study designs are summarized in Table 2.3, p.22.

Research Design in EpidemiologyRetrospective Design: is when the study looks back in time after the occurrence of injury or disease in an attempt to “reconstruct” the risk factor(s) that were responsible for the injury or disease.


Common Types of Research Design:– The most common types of epidemiology research designs are

the cross-sectional surveys, case-control, prospective cohort, and randomized trail studies.

– Cross Sectional Surveys Measure both risk factors and the presence (or absence) of disease

at the same point in time It is quick and easy to conduct Can assess risk factors and disease outcome ( prevalence ) Cannot associate (compare) risk factors and the presence or

absence of disease (Read example in text, “Health and Religion Project , p. 22)

Can be useful in generating hypotheses on risk and disease


– Case Control Studies Subjects are selected on the presence of disease of interest and

matched with controls who do not have the disease Appropriate for study of rare events or diseases Is quick and inexpensive (involves interview process that

determines risk factors, possible multiple risk factors) Can only study one event / disease per study Cannot be used to determine absolute risk and is subject to recall

bias, making temporal relationships uncertain. Case control studies may be used to determine whether a more

time consuming study may be needed (Cohort Study)

– Prospective Cohort Studies A subject group (cohort) is selected at random from a defined

population. Baseline risk factor analysis is conducted on the cohort and the

cohort is tracked over a period of time ( longitudinal study) for the incidence of those exposed to the risk factor and those who are not for the incidence of disease

Thus, prospective cohort studies are also called longitudinal studies and/or incidence studies

Provides an absolute measure of risk because baseline measures were recorded; however it can only assess the factors measured at baseline

Allows for the study of multiple disease outcomes, and allows the researcher to assess the changes in risk over time.

Is expensive and time consuming, and can suffer from subject drop-out and lack of follow-up.

– Randomized Control Trial (gold standard of research designs for testing a research hypothesis)

Participants are randomly selected and assigned to a control or experimental group

Can directly compare the effects of the independent variable on the dependent variable, thus sit is considered the gold standard for intervention evaluation

Expensive and time consuming Results are often cannot be generalized to the population Lack of compliance and drop-outs may occur

Summary on Study Designs

The ultimate goal of a research design is to assess the degree to which change in an independent variable (e.g., physical activity or fitness) is causally associated with change in a dependent variable (e.g., injury, disease, or death). It is important to remember the inherent strengths and weaknesses of the various study design options as we discuss the evidence for physical activity in reducing the risk of chronic disease.


Evaluating Associations in Epidemiologic Studies– Regardless of how complex the issues under study become,

most epidemiologic research can be conceptually framed in a standard 2 × 2 table (See table 2.4).

– Prospective Cohort Studies (Review Table 2.5 / 2.6) Incidence Rate = Those who developed CHD. Incidence is the

number of those individuals who developed CHD out of the entire population in the category (sedentary or active). In Table 2.6, the incidence of disease is equal to the number who developed CHD out of the Sedentary, and out of the Active Groups.

– Sedentary = 400 /(400+ 5600) = 0.6666 or 6.7%– Active = 100 / (100+3900) = 0.025 or 2.5%

Risk Difference: Subtract the incidence rate of the non-risk group from the at risk group (4.2%). If the risk difference is zero ( 0% ), then the risk factor makes no difference in CHD. If the risk ratio is greater than zero, then the risk factor is harmful. If the risk factor was actually protective, the risk ratio is less than zero.


– Attributable Risk (a.k.a. risk difference): the estimate of the amount of risk attributed to the risk factor. If the risk difference is 4.2%, thus 4.2% of the risk in this population is attributable to the exposure of the risk factor of sedentary behavior.

– Relative Risk (RR): is the ratio of the risk (incidence) in the exposed group to the risk of the unexposed group.

RR = 0.067 / 0.025 = 2.68 (see formula, p.28, text) Thus, if the incidence of disease was the same in both the

sedentary and active groups, the RR would be 1.0 In this case, the sedentary group is 2.68 times higher than the active

group Recall that the absolute risk for each group is 6.7% for the

sedentary, and 2.5 % for the active group in some cases the relative risk can be extremely high even while the

absolute risk in both groups is rather low.

Relative risks can also be used to judge the efficacy of clinical outcomes in randomized controlled trials.

– Example : If 20% of heart patients in a control group who get usual care or no treatment die from a second heart attack, but the rate is only 10% in an exercise group, the absolute risk difference between the groups is 10%. Inverting that difference (i.e., 1.0/0.10 = 10) yields what is known as the number needed to treat (NNT). In this example, an additional life would be saved for every 10 patients treated with exercise.



– Odds Ratio (OR): – compares the likelihood of an event between two groups. – is calculated by dividing the odds of exposure to the risk factor in

the diseased group by the odds of exposure in the non-diseased group. Thus, comparing THE CHANCES of developing CHD in the group that was sedentary vs. active.

OR = Disease / No Disease (a/b)/(c/d) OR = (ad/bc)

Comparing if you were active and did not develop disease or sedentary and did develop disease TO if you were active and developed disease or sedentary and did not develop disease (ad/bc)

Case Control Study– A case–control study doesn’t permit computation of a relative

risk directly since the people studied are selected because they already have a disease rather because they have been exposed to a risk factor. In a case–control study, participants are selected based on disease status (i.e., whether the disease is present or absent). Table 2.7 is a 2 × 2 table illustrating the organization of data from a case–control study.

– If exposure to the risk factor is positively related to the disease, then the proportion of cases exposed to the risk factor should be greater than the proportion of controls exposed to the risk factor.

– The only measure of strength of the risk of the association between risk factor and disease is the Odds Ratio

– In most instances the ORs from well- conducted case–control studies are reasonable estimates of the relative risk that would have been derived from a prospective cohort study, provided that the overall risk of disease (i.e., prevalence or incidence rates) in the population is low (i.e., less than 5%).

Additional Epidemiologic Research Estimates

– Attributable Risk (AR): Determines the “impact” of the exposure to the risk factor. It is the “estimate” of the disease burden (See values, page 28, text)

– Formulas for determining the total risk of disease (CHD) that results form the exposure to the risk factor (inactivity) among those that are exposed to that risk factor:

AR % = (Risk of those exposed to risk factor – unexposed )/ exposed

AR% = (RR-1)/RR

The AR% can also be calculated in a case-control study by substituting the odds ration for the RR when AR% = (RR-1) / RR


– Population Attributable Risk: (PAR) is the percentage of the risk of a disease that is attributable to a particular risk factor in the entire population studied. What is the percentage of the risk of CHD in this population is attributable to the risk factor (inactivity). (see text, p, 28)

Determine the total risk of the population:– Approx 500 cases of disease / 10,000 persons

Determine the risk of those without the risk factor Use the following formula: PAR% = (total risk – risk in those without the risk factor) / total risk

The results of the formula determine the risk for disease (CHD) in the population that is attributable to the risk factor (inactivity)

Diagnostic Test Terms

The sensitivity and specificity don’t alone determine the predictive value of a test. The prevalence of the outcome in a population must also be considered. Thus, a test’s predictive value depends on the frequency of the trait that underlies the test and the prevalence of the disease, relative to the decision point on the diagnostic test.


Models In Epidemiology– Epidemiology models help us to understand the causation

(Etiology), interaction, and progression of disease.

– Model Types (p.31)

Epidemiolgic Triagle– Simple association between the host (person) the agent (pathogen),

and the environment.


(Etiology), interaction, and progression of disease.– Model Types (p.31)

The Web of Causation– Accounts for multiple etiologies– Considers that the multiple etiologies may interact to promote disease,

but this makes the etiology more complex, and it is harder to predict specific health outcomes.


(Etiology), interaction, and progression of disease.

– Model Types (p.31) The Wheel

– Considered the most valid model of Epidemiologic Study because the model recognizes that the host develops from a genetic core that is modifiable to varying degrees by the environment to which the host is exposed.

Thus, ones genetic make-up predisposes how one will be affected by coming in contact with a potential agent

Also, the environment predisposes how one will be affected by the agent.


Determining Cause in Epidemiologic Studies– Casual Association is defined as an association

between categories of events or characteristics in which the alteration in one is followed by change in another

– Associations may be noncausal, even though a significant statistical association exists. Two issues, Confounding and Effect Modification, can result in “false” causal associations.


– Confounding: A stated variable being studied is believed to be the causal variable, but it is not because of other extraneous variables

Ex. Male baldness is associated with myocardial infarction, and baldness could be identified as the causal variable; however the extraneous variable not under consideration is age. Age is associated with male baldness, thus age has “confounded” the findings of the study.

It is important to have multiple studies that consider different samples and measure different “confounding” variables to add weight to a proposed causal variable.

– Effect Modification: A stated variable interacts with another variable and modifies the treatment effect of the first variable. Thus, effect modification means that the effect of the exposure on the outcome depends on the other variable. (See next slide for example)

Example of Effect Modification

Mortality decreases linearly with higher levels of physical activity. However, the slope of the decrease is much steeper among older people. The least-active older person has a much higher mortality rate than does the least-active young person. But the mortality rates for older people become increasingly similar to those of young people at higher levels of physical activity. Said another way, low activity is a bigger problem for older people, but high activity protects against mortality regardless of age. Hence, age modifies the effect of physical activity for reducing mortality risk.

Determining the degree to which certain factors act as effect modifiers can provide important information for the development of preventive strategies

Criterion for Causation (Mill’s Canons) – p35, text

-If the following Criterion are met, it is likely that the association is causal:

1. Temporal sequence: exposure to the risk factor must precede development of the disease with sufficient time to account for disease progression.

2. Strength of association: there is a large and clinically meaningful difference in disease risk between those exposed and those not exposed to the risk factor.

3. Consistency: the observed association is always observed if the risk factor is present (e.g., regardless of sex, race, age, or methods of measurement).

4. Dose response: the risk of disease associated with the risk factor is greater with stronger exposure to the risk factor.

5. Biological plausibility: the observed association is explainable by existing knowledge about possible biological mechanisms of the disease, which may be alterable (e.g., by physical activity).


END OF PRESENTATION

Chapter 02 Concepts and Methods in Physical Activity Epidemiology.

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