S URVEYS AND QUASI -
EXPERIMENTAL DESIGNS

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Surveys are investigations aimed at describing accurately the characteristics of populations for specific variables.
Surveys are commonly used in health research for the following purposes:
1. To establish the attitudes, opinions or beliefs of persons concerning health-related issues. The data collection techniques often include questionnaires or interviews.

2. To study characteristics of populations on health-related variables, such as utilization of health care, blood pressure, emotional problems or drug use patterns.
3. To collect information about the demographic characteristics (age, sex, income, etc.) of populations. A government census can be an important source of knowledge concerning population characteristics.

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The outcomes of the surveys can be the bases for hypotheses and theories concerning the causes of illness in a community. The area of health science concerned with such matters is called epidemiology.

E PIDEMIOLOGY
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Epidemiology is the field of study that is concerned with the distribution and determinants of health and illnesses in groups of people.
Epidemiology has been defined as the study of determinants of health and illness in a given population.
Epidemiological studies may be descriptive, where researchers study the incidence and prevalence of health and illness, or analytical, where researchers aim to identify the multiple and interacting factors that determine health and illness in a specific community.

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Epidemiological investigations are essential to data collection in public health, as this discipline is concerned with the study of health populations.
It differs from much clinical research in that it is oriented to the population or group rather than individual level.
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Epidemiology goes back to the time of
Hippocrates who was concerned with the effects of environments upon the health of populations.

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The work of John Snow in the 19th century concerning the epidemiology of cholera is also considered to be a major landmark in the development of the discipline.
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Snow mapped the distribution of cholera cases in
London and demonstrated that the water supply to different houses supplied by different water companies was involved with the transmission of the disease.
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The existence of the cholera organism was inferred from these observational data.

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Descriptive epidemiology focuses on specific target populations and compares the occurrence of different health or disease states within these populations.
Thus the numbers of cases, new cases and the relative risk of having the target condition are key components of the epidemiological approach.
Two key terms in epidemiology are incidence and prevalence.
The incidence of a disease is the rate at which new cases occur in a specified population during a specified period.
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The prevalence of a disease is the proportion of a specified population that have the target condition at a specified point in time.

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These basic definitions are often used to construct indicators of population health that can be compared across different countries.
The World Health Organization (WHO) maintains statistics concerning rates such as the birth rate, the infant mortality rate and perinatal mortality rate.
The WHO website provides a wealth of comparative epidemiological data
(http://www.who.int/en/).

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Analytical epidemiology is concerned with how diseases are transmitted or spread within populations. The classic epidemiological approach conceptualizes disease within the framework of host, agent, vector and environment.

D ESIGNS FOR COMPARING GROUPS
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Case-control designs:
Researchers using this design select a group of participants or patients who have a specific disease or disability (case) and compare them with a control group.
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The cases and the controls are compared on previous exposure to defined risk factors, such as pathogens, life events or diseases.
The method of data collection is usually retrospective.

E XAMPLE :
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Say: that there is an outbreak of food poisoning in a community with 20 people diagnosed with this condition.
These people constitute the ‘cases’. The control group would consist of people in the community who are not suffering from food poisoning.
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The hypothesis driving the investigation is that the outbreak was due to a specific unsafe food which was consumed by the affected individuals.

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Data collection would involve interviewing the cases to identify the source of the contamination.
Say that 18 out of 20 cases had eaten oysters at
‘Oscar’s Oyster Bar’ in the previous two days.
It is also found that only two of the healthy controls had eaten at Oscar’s, but had eaten food other than oysters. The analysis of this hypothetical data would clearly indicate the risk factor for the food poisoning outbreak.
Following further analyses of Oscar’s Oyster Bar and samples from the cases for pathogens (e.g. salmonella), the hypothesis would be confirmed and Oscar would have to rethink his seafood supplier.

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This simple example understates the complexity of the multiple and interacting determinants of health outcomes, in particular chronic diseases such as stroke or cancer.
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However, case-control designs are relatively simple to implement and provide useful evidence which requires further verification.

C ORRELATIONAL DESIGNS
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The aim of correlational studies in the health sciences is to identify interrelationships among variables.
In epidemiology these studies are referred to as
‘ecological’ designs.
Epidemiologists refer to correlational designs as ecological designs.
As correlational designs, they aim to establish associations between groups or variables, e.g. association between socio-economic class and the incidence of type 2 diabetes.

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Let us look at a simple illustration of correlational or ecological study.
Say that clinical observations indicate that people who suffer from coronary heart disease tend to be overweight. Such observations might generate the hypothesis: ‘There is a positive correlation between being overweight and the probability of coronary heart disease’.

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 eight and the probability of coronary heart disease’.
Here, the investigator will need to draw a representative sample of the population of interest
(let’s say 500 men and 500 women randomly selected from a population of healthy men and women aged 40 living in a specified district).
The next step would be to measure the participants’ weights and heights. These measures might be monitored over a period of time to check for drastic changes in weight.
The second variable would be measured by the criterion of whether or not the participant suffered from heart disease during a specified period (e.g. 10 years), representing the length of the study. The incidence of coronary disease can then be converted into a probability for a particular category of weight
(see Table).

Q UASI EXPERIMENTAL DESIGNS
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If preventive interventions are to be undertaken to reduce the risk factors associated with a disease, then we require reasonable evidence that these factors are, in fact, causally related to the disease. Quasi-experimental and cohort designs are often used for this purpose.

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The term cohort study is preferentially used in the domains of epidemiology and public health.
These designs can resemble experiments, with the important difference that there is no random assignment into treatment groups.
However, the investigator can control the time and place in which a treatment is introduced or withdrawn. One such method is a time-series design.

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Usually, these designs are prospective in that the participants are assessed at the beginning and then followed and assessed over a period of time.

T IME SERIES DESIGNS
 ime-series designs involve repeated observations before and after the administration of a treatment or intervention. In this way, changes in the sequence of observations following the introduction of a treatment may represent the effects of the treatment on the observed variable.

E XAMPLE
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Let us look at an example illustrating the use of time-series designs in health sciences research.
Returning to the risk factor of ‘being overweight’ we discussed previously, the following investigation using a time-series design could provide evidence for a causal relationship between this variable and cardiac disease.

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1. Select an appropriate population to study.
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2. Specify the dependent variable; that is, some clear-cut measure of ‘coronary heart disease’. A commonly used measure may be the incidence of the disease.
3. Introduce an appropriate treatment that reduces the magnitude of the risk factor. In our example, a health promotion package could be introduced, emphasizing exercise and good eating habits. Let us assume that this intervention is adequately financed and a significant proportion of the population adheres to the program.

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4. Monitor the dependent variable over a period of time. It is essential to have readings of the variable both before and after the introduction of treatment. In this instance, the incidence of coronary illness would be determined from the medical records of hospitals, clinics and physicians.