流行病学通常被称为公共卫生的“基石”,它是一门研究疾病的分布和决定因素,健康状况,或人群间的活动和应用于控制健康问题的学科。由于流行病学与现实生活息息相关,并更好地评估公共卫生项目和政策,学生将理解流行病学的研究方法,通过这一门课所学到的理论知识应用到当今的公共健康问题。本课程通过流行病学的视框,探讨了心血管疾病和传染病等公共卫生问题,对地区情况和全球情况都进行了讨论。
翻译: Yi Zhou
从本节课中
Study Designs
This module introduces the following study designs: experimental, cohort, case control, cross-sectional, and ecologic.
Clinical Associate Professor Department of Epidemiology, UNC Gillings School of Global Public Health
Dr. Lorraine Alexander
Clinical Associate Professor, Director of Distance Learning (North Carolina Institute for Public Health) Department of Epidemiology, UNC Gillings School of Global Public Health
[MUSIC]
Welcome.
In this segment, we're going to talk about ecologic study designs.
The key point about an ecologic study design to remember, is that either
the exposure or the health outcome, or both are measured at a group level.
Let's start and I'll show you what I mean.
The learning objectives for the ecologic study design segment are
to list the basic characteristics and explain the ecologic study design.
And also to identify the advantages
and disadvantages of the ecologic study design.
Ecologic studies are a type of observational study design.
They are one of the four types of observational
study designs that we are covering in our course.
Let's first talk about the unit of measurement.
In each of the observational study designs we've covered
so far, i.e, the cohort, case control and cross-sectional
study designs Generally, exposure data and health
outcome data are collected from each study participant.
There are some exceptions, but they won't be discussed here.
Study designs which collect data at the individual
level include cohort, case control, and cross sectional studies.
In contrast, you can also make measurements at a larger group level.
Exposure, and or health outcome data are collected at a group level.
Not an individual level.
Generally ecologic studies use a group level of measurement.
For example, the exposure measurement would be yearly
average air pollution air concentrations in five different cities.
Sometimes the health outcome occurance, proportions or
rates is only know at a group level.
For example, the yearly mortality, or death rate, from chronic
lung disease in these same cities with measured air pollution levels.
Let's compare group and individual
level data.
Group level data averages the exposure of the group, not individuals.
But individual level data provides information
on the exposure of each individual.
With group level data, we only know the health outcome of the group.
We don't know the exposure of individuals who became diseased and those who did not.
But with individual level data we are able to
link individual exposures to those who became diseased and
those who did not.
Linking individual exposures is a critical difference
to note between individual and group level data.
This leads us to the ecologic fallacy,
the major limitation of an ecologic study design.
An ecologic fallacy is concluding that an
association between the exposure and the health outcome
at a group level is true at an individual level, when this may not be true.
The reason
for this fallacy is that we do not
know the length between exposure and the health outcome.
Among individuals within each group i.e, we
don't know the number of diseases person who
were exposed or non-exposed in the high
exposure group nor in the low exposure group.
What we find at a group level may not hold true in an individual level.
Let's consider the hypothetical example that air
pollution is higher in Los Angeles than
in Denver.
But mortality from lung disease is lower in Los Angeles than in Denver.
We might come to the fallacious conclusion
that air pollution protects against lung disease deaths.
The explanation might be that persons dying of lung disease
in Denver, may have moved from high air pollution cities.
We don't know the cumulative exposures of cases and non-cases in either city.
Consider this example of an ecologic
study question.
Is the ranking of cities by air pollution
levels associated with the ranking of cities by
mortality from cardiovascular disease, adjusting for differences in
average age, percent below poverty level, and occupation?
Note that in this example there are no data at the individual level, allowing
us to link individual exposure to air
pollution with outcomes such as cardiovascular disease mortality.
Here is another example of an ecologic study question.
Have seat belt laws made a difference in motor vehicle
fatality rates, comparing years before and after laws were passed?
Note that again, there are no data at the individual level allowing us
to link individual compliance with seat belt
laws to the outcome, motor vehicle fatalities.
Now we will discuss advantages of the ecologic study design.
Group level data on exposure and health outcomes are often publicly available
in state and national databases i.e,
census data, mortality and cancer registry.
So ecologic studies have lower cost and are convenient.
Ecologic studies are useful for evaluating the impact
of community level interventions for example, fluoridation of water,
seat-belt laws, mass media campaigns, etcera.
We can compare outcomes at a community level before and after the intervention.
In the United States and many other countries, data are
regularly obtained on air quality, water quality and weather conditions.
The size of the population, the status of
the economy and the health of the population.
For example, the US Environmental Protection Agency
collects air pollution data at selected locations all around the country.
Using the national air quality monitoring network.
These monitors collect air pollution data at the group level.
In contrast, to collect individual level air pollution exposure
data, a person would need to wear an exposure monitor.
An example of group level data on a health
outcome would be obesity prevalence among low income preschool
children by state in the United States.
State and county obesity prevalences can be mapped to explore regional variations.
Comparing obesity prevalence by state, we see that California
and North Carolina are two states with higher prevalence.
If we look at obesity by county, you can see there
is a great deal more variation in obesity prevalence by county.
In fact, there are some counties
that have obesity prevalence that is above 20%.
But the state average is only 10 to 15%.
Such as in Washington state.
If we were planning educational interventions
in California and North Carolina, the county
level data will allow us to use our limited public health resources wisely.
And target specific counties.
These state and county obesity prevalence's
are examples of group level data
that are used to ecologic studies.
These publicly available records provide low cost and
convenient ways for researching variation in health outcomes at
a group level, with characteristics of the population,
the environment, or the economy, at a group level.
Now let's look an example of an ecologic study conducted on
household fire arms, or gun ownership in the United States and deaths.
In this figure, from Freagler et al 2013, we see that by state, the group level,
as household firearm ownership increases, there seems to
be an increase in firearm deaths per 100,000.
This is an example of an ecologic study in which both the exposure,
household firearm ownership, and outcome, firearm
deaths, are measured at a group level.
Another advantage of an ecologic study is that this study design can maximize
exposure differences between communities, where minimal within
community differences render individual risk studies impractical.
Whereas exposures may differ substantially
between communities, such as cities, states,
or countries, i.e, effective latitude on the risk of multiple sclerosis.
Ecologic studies are also useful for studying the effects
of short-term variations in exposures within the same community.
For example, temperature and mortality.
Examples of small exposure differences within
a community, but large between community
differences include quality of drinking water,
concentration of certain air pollutants such
as ozone and fine particles, average fat content of diet, larger differences
between countries than between individuals within the same city of a country.
Or, cumulative exposure to sunlight where there are larger differences by
latitude, north south of residence then
among individuals within the same latitude.
Now we will discuss limitations of the ecologic study design.
We have already discussed the ecologic fallacy earlier in this segment.
The ecologic fallacy refers to concluding that association, a,
a group or aggregate level are true at the individual
level when they may not be.
Another limitation of ecologic studies is that we
cannot be confident that exposure preceded the outcome.
Lastly, another limitation of ecologic studies is that
we do not know what happens to individual people.
Thus, migration into and out of communities
can bias the interpretation of ecologic studies.
This concludes the segment on ecologic study design.
What
I'd like you to remember from this segment is that in an ecologic study,
either the exposure or the health outcome, are both, are measured at a group level.
One example to help you remember that, is the
example of air pollution that's measured at a central site
location and is used to determine what the exposures
are for a population with a, a ten mile radius.
That's an example of an exposure
that's measured at a group level.
We will end the lecture on ecologic studies with a practice question.