introduction to healthcare research methods: correlational studies, case series and cross-sectional...

PHC215

By Dr. Khaled Ouanes Ph.D.

E-mail: k.ouanes@seu.edu.sa

Twitter: @khaled_ouanes

INTRODUCTION TO

HEALTHCARE RESEARCH

METHODS

Correlational /Ecological

Studies

Correlational studies are also called

ecological or aggregate studies.

This type of studies uses population-level

data to examine the relationship between

exposure rates and disease rates.

We are thus in the case of a study in which

units of analysis are populations or groups

of people rather than individuals.

i.e. The focus will be on the comparison of

populations/groups rather than individual patients

or participants.

Examples

Does the percentage of adults with multiple sclerosis tend to be higher in countries farther from the equator?

Does the rate of asthma tend to be higher in cities with higher levels of air pollution?

Does the prevalence of diabetes tend to be higher when we have higher prevalence of obesity?

Population-level data are used

to look for associations between

two or more group

characteristics

Data Sources

At least one data source (if not more) thatcontains comparable information about thepopulation characteristics of interest must beidentified.

Information about all the variables of interestmust be available for a suitable number ofpopulations, which can be grouped by place orby time.

Examples of Populations

All Western European countries

The largest 25 metropolitan areas in the Arab world

All Sub-Sahara countries

A random sample of survey Areas in London

Historic data for the past decades from one or more place-based populations

Exposures and Outcomes

At least one characteristic of the populations

being examined is designated as an exposureExposures are often environmental measures likely to be fairly consistent across an

entire population

At least one characteristic is designated as an

outcome

Aggregate Data

Population characteristics are in the form of

aggregate (grouped) data, such as: the proportion of each population with a particular characteristic

the average value of the variable in the population

Examples of Exposures

The percentage of adults older than 30 who have not completed

at least 12 years of education

The mean income in the population

The median age

The number of rainy days over a given year in the population

The average ultraviolet radiation index during midday in the

hottest month of the year

Examples of Diseases

The prevalence of obesity among adults

The mean BMI (body mass index) among adults

The annual mortality rate from asthma

Cautions

Correlational studies are valid only if the data

points are comparable.A data point is a discrete unit of information. Generally, any single fact is a data point.

In a statistical or analytical context, a data point is usually derived from a

measurement or research and can be represented numerically and/or graphically.

In some populations, exposures and diseases may

be routinely undercounted or routinely over-

diagnosed compared to other populations.

Cautions

If multiple sources of data are used or if the data

were collected over a lengthy period of time,

then the definition of exposure or disease may

differ from one population to another and may

not be comparable.

Data Management Example

Data should be entered into a spreadsheet

Each population (A, B, C, etc.) is in its own row

Each exposure and each outcome is in its own

column

Analysis: Correlation

On a scatterplot used to illustrate correlation, each point represents one population in the study.

The exposure is plotted on the x-axis, and the outcome or

disease is plotted on the y-axis.

Do you see a Correlation?

Do you see a Correlation?

Analysis: Correlation

1. When all the points fall neatly in a line, then the

correlation is strong.

2. When the points are not exactly linear but a line for

trend can be drawn, then the correlation is mild or

moderate.

3. When the points appear to be randomly placed

and no obvious line can be drawn through them,

then the correlation is weak or nonexistent.

Analysis: Correlation

If higher levels of exposure are linked to higher rates of disease, then the slope is positive.

If higher levels of exposure are linked to lower rates of disease, then the slope is negative.

Analysis: Correlation

For continuous variables and other variables with

responses that can be plotted on a number line, a

Pearson correlation coefficient (r) should be used to calculate the correlation.

For variables that assign a rank to responses or that have

ordered categories, use the Spearman rank-order

correlation (designated by the letter r or the Greek letter

r (rho) in most statistical programs).

Analysis: Correlation

The Pearson method is built on the notion that if

Measurement 1 trails Measurement 2 (directly or

inversely), you can get some indications on how

linked they are by calculating Pearson's r -the

correlation coefficient-, which is a quantity

derived from the products of the differences

between each M1 and its average and each M2

and its average.

Analysis: Correlation

Spearman's rank coefficient is similar to Pearson in

producing a value from -1 to +1, but you would

use Spearman when the rank order of the data

are important in some way.

The Pearson test is more widely used.

r = –1: all points lie perfectly on a line with a negative slope

r = 1: all points lie perfectly on a line with a positive slope

r = 0: no association between the exposure and outcome

r2 shows how strong a correlation is without indicating the

direction of the association

Analysis: Correlation

Analysis

Use linear regression models when the goal is to:

compare more than two variables

understand the relationship between two variables

while controlling or adjusting for the effects of other

variables

Age Adjustment

When the populations being compared havevery different age structures, age adjustmentmay be necessary to make a fair comparisonamong populations.

Avoiding the Ecological Fallacy

Correlational studies compare groups rather than individuals.

No individual-level data are included in the analysis, only population-level data.

The incorrect attribution of population-level associations to individuals is called the ecological fallacy.

Even though a population with a higher rate of exposure to something has a higher rate of

disease than populations with lower exposure rates, individuals in that population who have a high level of exposure do not necessarily have

the disease.

Avoiding the Ecological Fallacy

Avoiding the Ecological Fallacy

The experience of an individual in a populationmay vary significantly from the populationaverage.

It would be incorrect to assume that any oneindividual from a country with a high averagebody mass index (BMI) will be obese or that anindividual from a country with a low average BMIwill not be obese.

However, it is appropriate to identify trends in populations and to use those observations to

generate hypotheses for individual-level studies that will test for relationships between the characteristics of interest in individuals.

Avoiding the Ecological Fallacy

Key Characteristics of Correlational

(Ecological) Studies

Case Series

Uses of Case Series

Describing the characteristics of and similarities

among a group of individuals with the same signs

and/or symptoms of disease

Identifying new syndromes and refining case

definitions.

Clarifying typical disease progression

Developing hypotheses for future research

Sample Size

Some case series for rare conditions may

require only a few participants

Other studies may include several hundred

individuals

Getting Started…

Select one disease or condition of interest

Determine what will be new and interesting about

the study

Identify an appropriate and available source of

cases

Establish a clear case definition that spells out

inclusion criteria and exclusion criteria.

Case Definitions

Specify characteristics related to:

The disease or procedure ICD codes (International Classification of Diseases codes) are often used as

part of the definition

Person

Place

Time

Sample Case Definitions

Data Collection

Primary data: interviews of cases using a

questionnaire and/or qualitative techniques

Secondary data from patient charts (medical

records)

It is often helpful to create a questionnaire that guides the extraction of

information from medical records

Be aware that patient charts are often incomplete; missing information

about a symptom does not mean that the patient did not experience it

Most case studies do not require any advanced analyses or any numbers beyond simple counts

and frequencies.

Key Characteristics of a

Case Series

Cross-Sectional Surveys

Overview

The goal of a cross-sectional survey, alsocalled a prevalence study, is to measure theproportion of a population with a particularexposure or disease at one point in time basedon a representative sample of a population.

Cross-sectional surveys are among the most popular study approaches in the health sciences because they allow for

the relatively rapid collection of new data.

Uses

Cross-sectional surveys are used to:

Describe communities

Assess population needs

Evaluate programs

Establish baseline data prior to the initiation

of longitudinal studies

Representative Populations

Cross-sectional studies use a simple study design:

The researcher asks a few hundred people to

complete a short questionnaire and then analyzes the

data.

However, there is one very important requirement: the

participants must be reasonably representative of

some larger population.

Representative Populations

If a researcher wants the results of a survey to be

generalizable to all town residents, it is NOT acceptable to

use a convenience population such as:

Friends

Fans attending a football game

Shoppers at a store at a given time on a chosen day

Individuals attending a clinic

Pupils attending a neighbourhood school

Representative Populations

If the results of a cross-sectional survey are

intended to reflect the profile of an entire

town (or other population group), then the

study’s sampling strategy must recruit a

population that is as diverse as the town.

Analysis: Prevalence

Prevalence = the proportion of the population with a given trait at

the time of the survey

Analysis: Comparative Statistics Prevalence rate ratios (PRRs) compare the prevalence of a characteristic in

2 population subgroups by taking a ratio of their prevalence rates

Note: An exposure can be said to be “associated” or “related” to a disease,

but a cross-sectional survey cannot show that an exposure caused a

disease.

Key Characteristics of

Cross-Sectional Surveys

PHC215

By Dr. Khaled Ouanes Ph.D.

E-mail: k.ouanes@seu.edu.sa

Twitter: @khaled_ouanes

HEALTHCARE RESEARCH METHODS

Based on the textbook of introduction to health research methods – K.H. Jacobsen