Odds Ratio Research Paper
APPLIED EPIDEMIOLOGY
1. Discuss odds ratio.
An odds ratio (OR) is a measure of association between an exposure and an outcome. The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure. Odds ratios are most commonly used in case-control studies, however they can also be used in cross-sectional and cohort study designs as well (with some modifications and/or assumptions).
In statistics, imagine each individual in a population either does or does not have a property ″A,″ and also either does or does not have a property ″B.″ For example, ″A″ might be "has high blood pressure,″ and ″B″ might be ″drinks more than one alcoholic drink a …show more content…
day,″ where both properties need to be appropriately defined and quantified (the properties need not be medical, though, and they need not be ″good″ or ″bad″). The odds ratio[1][2][3] (usually abbreviated ″OR″) is one of three main ways to quantify how strongly the having or not having of the property A is associated with having or not having the property B in that population. As the name implies, to compute the OR, one follows these steps: 1) compute the odds that an individual in the population has ″A″ given that he or she has ″B″; 2) compute the odds that an individual in the population has ″A″ given that he or she does not have ″B″; and 3) divide the first odds by the second odds to obtain the odds ratio, the OR. If the OR is greater than 1, then having ″A″ is ″associated″ with having ″B″ in the sense that the having of ″B″ raises (relative to not-having ″B″) the odds of having ″A.″ Note that this is not enough to establish that B is a contributing cause of ″A″: it could be that the association is due to a third property, ″C,″ which is a contributing cause of both ″A″ and ″B.″
Definition: An odds ratio (OR) is defined as the ratio of the odds of an event occurring in one group to the odds of it occurring in another group, or to a data-based estimate of that ratio.
An odds ratio estimates the probability of disease given exposure to a specific factor by measuring the probability of exposure given the presence of disease.
Example: At a recent UM football game, a terrorist group flew a crop duster over the stadium and dusted the western side of the stands with anthrax spores. Fortunately, the wind was blowing toward the west. Once cases were discovered, the investigation began.
A total of 100 people who sat on the eastern side developed disease and 1000 people who sat on the western side developed disease. Of the 100,000 people in attendance, 50,000 were sitting on the western side of the stadium and were considered to have been exposed.
From the given data, here is the 2x2 table and odds ratio calculation.
Anthrax Yes
Anthrax No
West (Exposure Yes) …show more content…
1000
49000
East (Exposure No)
100
49900
Odds Ratio = [(1000) (49,900)] / [(100) (49,000)] = 10.18
Interpretation: The odds of developing anthrax were 10 times higher for those sitting on the western side compared to those sitting on the eastern side of the stadium.
2. Disease Cross Sectional Studies
Cross-sectional studies involve data collected at a defined time. They are often used to assess the prevalence of acute or chronic conditions, or to answer questions about the causes of disease or the results of medical intervention. They may also be described as censuses. Cross-sectional studies may involve special data collection, including questions about the past, but they often rely on data originally collected for other purposes. They are moderately expensive, and are not suitable for the study of rare diseases. Difficulty in recalling past events may also contribute bias.
Definition: A cross-sectional study is a descriptive study in which disease and exposure statuses are measured simultaneously in a given population.
This study type can be thought of as providing a "snapshot" of the frequency and characteristics of a disease in a population at a particular point in time. Data that are collected as part of a cross-sectional study can be used to assess the prevalence of acute or chronic conditions in a population.
Advantages to Cross-Sectional Studies
Does not require follow-up and is therefore less costly and quicker than other designs.
Are often representative of a population, rather than a smaller sub-population.
Disadvantage of Cross-Sectional Studies
Since exposure and disease status are measured at the same time it is not possible to determine the direction of the association. In other words, it is not known if the exposure preceded the disease and is therefore a potential cause of
disease.
Example: A cross-sectional study can be used to look at the association between obesity and television watching. A sample of people from the population that you are interested in can be polled and asked about their height/weight ratio and the number of hours of television the person watches each week. This study will give insight as to whether obesity and television watching are associated, but it will not help to determine which might cause the other. In other words, it is not known if obesity causes more television watching or if more television watching causes obesity.
A cross sectional study measures the prevalence of health outcomes or determinants of health, or both, in a population at a point in time or over a short period. Such information can be used to explore aetiology - for example, the relation between cataract and vitamin status has been examined in cross sectional surveys. However, associations must be interpreted with caution. Bias may arise because of selection into or out of the study population. A cross sectional survey of asthma in an occupational group of animal handlers would underestimate risk if the development of respiratory symptoms led people to seek alternative employment and therefore to be excluded from the study. A cross sectional design may also make it difficult to establish what is cause and what is effect. If milk drinking is associated with peptic ulcer, is that because milk causes the disease, or because ulcer sufferers drink milk to relieve their symptoms? Because of these difficulties, cross sectional studies of aetiology are best suited to diseases that produce little disability and to the presymptomatic phases of more serious disorders.
Other applications of cross sectional surveys lie in planning health care. For example, an occupational physician planning a coronary prevention programme might wish to know the prevalence of different risk factors in the workforce under his care so that he could tailor his intervention accordingly.
REFERENCES
Wikipedia, the free encyclopedia
Cornfield, J (1951). "A Method for Estimating Comparative Rates from Clinical Data. Applications to Cancer of the Lung, Breast, and Cervix". Journal of the National Cancer Institute 11: 1269–1275.
Mosteller, Frederick (1968). "Association and Estimation in Contingency Tables". Journal of the American Statistical Association (American Statistical Association) 63 (321): 1–28. doi:10.2307/2283825.
Greenfield B, Henry M, Weiss M, Tse SM, Guile JM, Dougherty G, Zhang X, Fombonne E, Lis E, Lapalme-Remis, Harnden B. Previously suicidal adolescents: Predictors of six-month outcome. Journal of the Canadian Association of Child and Adolescent Psychiatry. 2008;17(4):197–201.
"When to use the odds ratio or the relative risk?", by Carsten Oliver Schmidt, Thomas Kohlmann, Int J Public Health 53 (2008) 165–167 1661-8556/08/030165-3 DOI 10.1007/s000 -00 -7068-3 © Birkhäuser Verlag, Basel, 2008
1. Discuss odds ratio.
An odds ratio (OR) is a measure of association between an exposure and an outcome. The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure. Odds ratios are most commonly used in case-control studies, however they can also be used in cross-sectional and cohort study designs as well (with some modifications and/or assumptions).
In statistics, imagine each individual in a population either does or does not have a property ″A,″ and also either does or does not have a property ″B.″ For example, ″A″ might be "has high blood pressure,″ and ″B″ might be ″drinks more than one alcoholic drink a …show more content…
day,″ where both properties need to be appropriately defined and quantified (the properties need not be medical, though, and they need not be ″good″ or ″bad″). The odds ratio[1][2][3] (usually abbreviated ″OR″) is one of three main ways to quantify how strongly the having or not having of the property A is associated with having or not having the property B in that population. As the name implies, to compute the OR, one follows these steps: 1) compute the odds that an individual in the population has ″A″ given that he or she has ″B″; 2) compute the odds that an individual in the population has ″A″ given that he or she does not have ″B″; and 3) divide the first odds by the second odds to obtain the odds ratio, the OR. If the OR is greater than 1, then having ″A″ is ″associated″ with having ″B″ in the sense that the having of ″B″ raises (relative to not-having ″B″) the odds of having ″A.″ Note that this is not enough to establish that B is a contributing cause of ″A″: it could be that the association is due to a third property, ″C,″ which is a contributing cause of both ″A″ and ″B.″
Definition: An odds ratio (OR) is defined as the ratio of the odds of an event occurring in one group to the odds of it occurring in another group, or to a data-based estimate of that ratio.
An odds ratio estimates the probability of disease given exposure to a specific factor by measuring the probability of exposure given the presence of disease.
Example: At a recent UM football game, a terrorist group flew a crop duster over the stadium and dusted the western side of the stands with anthrax spores. Fortunately, the wind was blowing toward the west. Once cases were discovered, the investigation began.
A total of 100 people who sat on the eastern side developed disease and 1000 people who sat on the western side developed disease. Of the 100,000 people in attendance, 50,000 were sitting on the western side of the stadium and were considered to have been exposed.
From the given data, here is the 2x2 table and odds ratio calculation.
Anthrax Yes
Anthrax No
West (Exposure Yes) …show more content…
1000
49000
East (Exposure No)
100
49900
Odds Ratio = [(1000) (49,900)] / [(100) (49,000)] = 10.18
Interpretation: The odds of developing anthrax were 10 times higher for those sitting on the western side compared to those sitting on the eastern side of the stadium.
2. Disease Cross Sectional Studies
Cross-sectional studies involve data collected at a defined time. They are often used to assess the prevalence of acute or chronic conditions, or to answer questions about the causes of disease or the results of medical intervention. They may also be described as censuses. Cross-sectional studies may involve special data collection, including questions about the past, but they often rely on data originally collected for other purposes. They are moderately expensive, and are not suitable for the study of rare diseases. Difficulty in recalling past events may also contribute bias.
Definition: A cross-sectional study is a descriptive study in which disease and exposure statuses are measured simultaneously in a given population.
This study type can be thought of as providing a "snapshot" of the frequency and characteristics of a disease in a population at a particular point in time. Data that are collected as part of a cross-sectional study can be used to assess the prevalence of acute or chronic conditions in a population.
Advantages to Cross-Sectional Studies
Does not require follow-up and is therefore less costly and quicker than other designs.
Are often representative of a population, rather than a smaller sub-population.
Disadvantage of Cross-Sectional Studies
Since exposure and disease status are measured at the same time it is not possible to determine the direction of the association. In other words, it is not known if the exposure preceded the disease and is therefore a potential cause of
disease.
Example: A cross-sectional study can be used to look at the association between obesity and television watching. A sample of people from the population that you are interested in can be polled and asked about their height/weight ratio and the number of hours of television the person watches each week. This study will give insight as to whether obesity and television watching are associated, but it will not help to determine which might cause the other. In other words, it is not known if obesity causes more television watching or if more television watching causes obesity.
A cross sectional study measures the prevalence of health outcomes or determinants of health, or both, in a population at a point in time or over a short period. Such information can be used to explore aetiology - for example, the relation between cataract and vitamin status has been examined in cross sectional surveys. However, associations must be interpreted with caution. Bias may arise because of selection into or out of the study population. A cross sectional survey of asthma in an occupational group of animal handlers would underestimate risk if the development of respiratory symptoms led people to seek alternative employment and therefore to be excluded from the study. A cross sectional design may also make it difficult to establish what is cause and what is effect. If milk drinking is associated with peptic ulcer, is that because milk causes the disease, or because ulcer sufferers drink milk to relieve their symptoms? Because of these difficulties, cross sectional studies of aetiology are best suited to diseases that produce little disability and to the presymptomatic phases of more serious disorders.
Other applications of cross sectional surveys lie in planning health care. For example, an occupational physician planning a coronary prevention programme might wish to know the prevalence of different risk factors in the workforce under his care so that he could tailor his intervention accordingly.
REFERENCES
Wikipedia, the free encyclopedia
Cornfield, J (1951). "A Method for Estimating Comparative Rates from Clinical Data. Applications to Cancer of the Lung, Breast, and Cervix". Journal of the National Cancer Institute 11: 1269–1275.
Mosteller, Frederick (1968). "Association and Estimation in Contingency Tables". Journal of the American Statistical Association (American Statistical Association) 63 (321): 1–28. doi:10.2307/2283825.
Greenfield B, Henry M, Weiss M, Tse SM, Guile JM, Dougherty G, Zhang X, Fombonne E, Lis E, Lapalme-Remis, Harnden B. Previously suicidal adolescents: Predictors of six-month outcome. Journal of the Canadian Association of Child and Adolescent Psychiatry. 2008;17(4):197–201.
"When to use the odds ratio or the relative risk?", by Carsten Oliver Schmidt, Thomas Kohlmann, Int J Public Health 53 (2008) 165–167 1661-8556/08/030165-3 DOI 10.1007/s000 -00 -7068-3 © Birkhäuser Verlag, Basel, 2008