Cross-Sectional Studies Sence - Coursework Example

Epidemiology Student’s name: Institution: Date: Cross sectional studies Cross sectional studies often involve the collection of data from a population or a sample representative of the entire population. Cross sectional studies are mainly done on the basis of observation of the population under study to determine the contributing association that exists between the risk factors of the disease and the outcome. In cross-sectional study, the epidemiologist often defines the population they intend to study and then perform a study to the group members regarding their diseases and exposure statuses (Gordis, 2009). This data represents a particular time and, therefore, it is similar to a snapshot of the entire population. The cross sectional study is important in evaluating variables and the respective diseases. Some of the most attractive elements of cross sectional studies is that it is specific in nature, in that it targets to answer the problem in question for instance the causes of disease as well as the possible results that may be brought about by the possible interventions. This study involves the use of routine data and, therefore, exposes the researcher to a large amount of data that may be necessary for a comprehensive study of the phenomenon in questions (Gordis, 2009). Temporal relationship When seeking to provide support for possible causative association between the risk factor and the disease outcome, temporal relationship can be applied effectively using cross-sectional studies. This implies that the study will offer in depth analysis of the causative factor as well as the outcome of a possible intervention. Cross sectional study can, therefore, be used to acquire this information and assign the casualty to the recommended relationship. Most often, the temporal relationship that exists between the risk factor and the disease outcome cannot be determined easily, however, through the use of cross sectional studies, the data used are for a particular period of time hence making easy to establish any temporal relationship that occurred between the risk factor and the disease. Strength of association Cross sectional studies are relatively important in establishing the strength of association that existed between the risk factor and the outcome of the prescribed disease. The in-depth nature of cross sectional studies makes it easier for epidemiologists to know how strong the bond that attached the two associations was and hence come up with the best and fitting interventions (Olsen, 2010). Consistency Consistency refers to the levels of conformity between the risk factor and the disease outcome. Under the cross sectional design, the subject under discussion is, therefore, analyzed the level of recurring is determined to see the relationship that exists between the risk aspect and the disease outcome (Samet, 2009). The levels of consistency can therefore be measured and used in analyzing the causative agent of a given disease Biological gradient (dose-response relationship) The biological gradient can equally be established through the cross sectional study. The degree of exposure to the causative agent should determine the levels of effect. However, there are instances where just the mere presence of any of the factors may trigger or cause the effect (Gordis, 2009). The cross sectional design can help in establishing the causative association that exists between the disease outcome and the risk factors associated with it. Biological plausibility The cross-sectional analysis between the risk factor and the disease outcome can, therefore, be used in determining the causative agent a disease. This can be done after analysis of various factors vis a vis the biological knowledge that gives references to this. Case control During the case control study, epidemiologists often work backwards, starting from the causative effect towards the suspected cause. Case control may also be known as retrospective studies, and the participants are often chosen because of existence or nonexistence of the disease infection or the outcome in question. Therefore, the case study often to consolidate a group of people that have health problems and a different group that does not have any health problem to act as a control experiment for the study. The comparison is often made between these two groups in an effort to determine the existence of the risk factors. For instance, a group of people suffering from diabetes could be picked and another group without diabetes, and then a comparison is made between the two groups in regards to the habits in their lifestyle history. The causative relationship in this case is, therefore, determined through the calculations of the odds ratio (Hern & Savitz, 2013). Relatively, case control has a number of advantages attributed to it; some of these are the ability to examine numerous exposures for any single outcome. They can also be used in the study of rare diseases as well as the diseases that have long latency periods. As compared to cohort studies, they are less expensive and quicker to conduct; this makes them among the most suitable options for investigating disease outbreaks. Temporal relationship: this aims at analyzing the cause and the effects, and under normal circumstance, the cause often precedes the effects. Case study may be appropriate in this case since it makes comparison between the medical histories of disease occurrence within a given group against another control group that is considered healthy. Possible causative association between the disease risk factor and the outcome can, therefore, be determined. Strength of association: this involves the clarity of the existing relationship between the risk factor and the disease outcome. The case control study can also be used in establishing the existing causative relationship between the disease outcome and the risk factor (Olsen, 2010). This design may not be effective but may yield viable results upon keen examination. Consistency: under this design, the association between the risk factor and the disease outcome is made and is performed repeatedly among the populations under study for a period of time. The use of case control may not be effective in establishing the levels of consistency that exists between the risk factors, as well as the disease outcome. The data used is historical and may not provide accurate information on the levels of recurrence of the association. Biological gradient (dose-response relationship): this determines the manner in which the relationships respond to a dose. The case control may be suitable for establishing biological gradient since it uses historical data that is then compared and the manner in which the group responds is used in determining the dose relationship. Biological plausibility: the explanation for the existence of the relationship between the risk factors, as well as the disease outcome, should be biologically sensible and offer a valid explanation. In this case, the case control offers a viable stand through which this analysis can be made and using historical data can help in explaining biologically the behavioural pattern of the relationship between the variables. Cohort studies For cohort study, the study population is selected relative to their levels of exposure, and this is done regardless of whether the disease under study is present or not. The outcomes are then compared on the basis of individual’s level of exposure (Steenland, 2009). The prospective studies are also known as a prospective study because they often follow the targeted study population forward in time. A perfect example would be categorizing people according to their smoking status, and then monitoring them for about 10 or 20 years to establish if they will develop lung cancer. Cohort studies may also be used in the investigation of disease outbreaks in small and well defined populations. For instance, when seeking to address the question diarrhea outbreak in company picnic (Olsen, 2010). In such a case, the attendees will be asked about the foods and the beverages that they consumed during the picnic. The existing relationship between exposure and disease outcome is thus established through the calculation of relative risk of exposure. Strength of association: the cohort study takes a different approach that may not be suitable for establishing a relationship between the risk factor and the disease outcome. The cohort study uses forward time to make its analysis and may only be effective in cases where the results are not required urgently. Consistency: to achieve consistency through the cohort studies, one needs to wait for a given period of time and perform various tests to establish the nature of the relationship. The information provided by the cohort study is never available at the moment, but rather may take time before development. Determining the levels of accuracy for such may be difficult since it lays on forward research timing. Temporality: this can be established by the cohort method and a certain level of effectiveness can be achieved. Observing the manner in which the risk factor and the disease outcome relate can be easy and also allows for preparation research hypotheses. Biological Plausibility: the cohort studies can be used in determining the plausibility of the existing relationship between a risk factors, as well as the disease outcome (Olsen, 2010). In this, scientific explanation can be given to the manner in which the two variables relate and possible ways of intervention. Biological gradient: the cohort studies offers a perfect environment for dose response relationship. This is because the studies can be carried out procedurally with each factor under close monitoring (Hern & Savitz, 2013). In the end, the dose response relationship can be analyzed and their effects on the association between the risk factors, as well as the disease outcome. Write a short essay on sources of bias in epidemiological research Bias in epidemiology can be described as the systematic error that causes incorrect estimate of the existing association between the exposure and the risk factors of disease. Biases are often classified in accordance to the directional changes they produce within any given parameter such as the odds ratio (OR) (Samet, 2009). Negative bias often yields the estimates that are close to the null value, relatively, the away-from-the-null bias gives higher estimates more than the true ones. Biases occur from various sources and different forms, most of these are dependable on the association that is existent between the risk factors of the disease and the exposure. Stated underneath are among the most common forms and sources of bias. Selection bias This is the systematic error that is often introduced in cases where the selected study population is not an actual representation of the target population (Hern & Savitz, 2013). Selection bias can always be controlled by measuring all the variables affecting the study subjects, and such must be precursors of both the exposure and the outcome, or the joint distribution between these variables is recognized in the entire target population and finally the collection probabilities of the levels of each of the variables are known. Forms of Selection Bias Selection bias often takes various forms, most of which are affected by various combination of exposure and diseases. For any selection bias to cause distortion in any given association, the selection probability needs to be disproportionate throughout the various combinations of exposure and disease (Samet, 2009). Most of these selection biases are dependent on the major study designs which are the case control, cohort and the cross-sectional design. Non-response bias Nonresponsive bias often occurs when people fail to contribute or participate on the presumed surveys. In most cases, individuals that are busy often fail to participate in such unless they have an inherent interest on the issues (Olsen, 2010). This is among the most common challenges that epidemiologists often face when they conduct their surveys and people fail to turn up. Most probably, one of the major causes of this are the current social and technological developments where people tend to be very protective about their personal information. As a result, they decline to participate in researches or surveys that they feel may require personal information. In lieu of the above, there is always every possibility that the participants’ opinion may not be the actual representation of the entire target population (Cort, 2009). When only a few people turn up or response, the data provided is insufficient and may not offer biological plausibility in the data presented. In designs like the case control often, consider non-response bias as a very critical issue and of great effect to the outcome of the survey. As well, in most cases, the people that accept to participate in the case control survey design are often healthy and such may not be the exact representation of the population that is under target. Neyman bias This is also known as selective survival bias and mainly occurs in cases where the participants are selected among the people already having the disease and not actually from those that have been newly diagnosed with the disease. The main distinctive factor in this case is the disease prevalence as well as the incident disease; in which case the prevalent disease cases are where the subjects have existing conditions of the disease and incident as where the subjects do not have any existing condition of the disease (Olsen, 2010). If the survey is performed based on the prevalent disease, then it means that only the factors that influence cure will be established. This may not give a correct representation of the target population. Berkson’s bias This type of Bias often occurs in cases where the hospitalization probability differs in terms of whether the victims were exposed or not. At instances where the exposed cases have a high likelihood of hospitalization as compared to the non-exposed cases, then reliance on only one of the above cases may result to ambiguity in the outcome achieved. Inclusion Bias This mainly occurs in cases where the selected controls are most likely to exposed than the population from which they have been derived. In this perspective, the exposure is often associated with the increased risk of contracting the disease as compared to the risk posed by the entire population (Hern & Savitz, 2013). In such case, the sample selected may be an accurate representation of the targeted group. The controls in this survey will, therefore, exhibit similar characteristics to those of the subjects under survey. Exclusion Bias The exclusion bias often occurs in tow different perspectives; it can be used to decipher two distinct forms of biases. They are randomized often and may come because of certain reasons. Some if the reasons include ineligibility in which case the patients under survey do not meet the recommended eligibility criteria by the research after the randomization process (Samet, 2009). An element of protocol violation may also lead to exclusion bias, in such a case, patients have often failed to receive the treated allocated for them whether knowingly or unknowingly. Instances of protocol violation may also be brought about by the withdrawal of patients from the process. Problems that may be caused by selection bias Evaluation of the effects of selection bias may not be a very simple process. Determination of this demands the presence of a number of information most of which include the accurate levels of participation and having sound knowledge of the exposure and the outcomes for each of the participants as well as the non-participants. With this information, the levels of bias can be determined. The major problem with the selection bias is that, in most cases, this data is never available hence calculating the levels of accuracy of the entire process can be quite challenging. As well, prediction of the magnitude and the direction of the bias may also be very challenging. To achieve this, a comparison of the results of all the designs can be used to determine the magnitude; however, this demands that all the survey experiments be compatible with all the three designs. In many instances, it is easy to establish the direction of the selection bias, but the magnitude is often far much beyond calculation. Solution to the above problems Good framework for selection of the control needs to be established. This will ensure levels of consistency are maintained throughout the entire survey process. As well, controls that represent the selected samples ought to be chosen; this can be done through selection that bears similar resemblance to the selected sample in all aspects except the outcomes. This will increase the levels of accuracy of the entire process. Prediction should also be made on the factors that are likely to transition the exposure and the diseases well as the controls. This in turn reduces the likelihood of cases that will bring the matching effect among the groups under study. Moreover, statistical imputation may be used to reduce the bias caused by lack of certain information during the analysis stage (Hern & Savitz, 2013). When critical information is missing, the accuracy of the process is often threatened. Imputing the statistics makes it easy to determine the probability of a certain sequence, and that probability can be used in filling the gap for the lost information. Information bias Apart from the selection bias, information bias is another common form of bias that often takes place. Information bias is mainly caused by errors occurring in the measurement of the study elements among the population groups (Gordis, 2009). Consequently, miscalculation may also result when study subjects are assigned to the wrong exposure groups or disease. During such instances, the patient may be diagnosed with the wrong disease hence causing information bias. There are various forms of information bias, some of these include: Interviewer bias: this occurs in cases where the interviewer is aware of the study hypotheses. In such cases, they tend to manipulate the results into what they expect hence may ruin the integrity of the entire process. For instance, if the interviewer is aware of the specific exposure of disease, he/ she may be tempted consciously or unconsciously to incline the information to suit the intended results (Hern & Savitz, 2013). Gestures produced during the interview may also cause interviewer bias. In this case, the interviewer may use certain gestures and questions to knowingly or unknowingly compel the respondent into responding in a certain manner. This may not represent the actual opinion of the individual. Recall bias: this is where a person may not be in a position to remember accurately the occurrence of certain events. People's ability to remember accurately depends on a number of factors. In most cases when people cannot vividly recall the manner in which the events transpired, they are often assisted to ponder over such issues (Hern & Savitz, 2013). In the process of pondering, they may make conclusions that are not relevant to the information required. This will result in inconsistency hence demeaning the accuracy of the targeted results. Conclusion From the above analysis, it is evident that the cross-sectional studies, the case control studies and the cohort studies have different ways in which they interact with the risk factors and the disease outcome. The approaches used by both the parties is different and each is distinctive from one another. The three epidemiological approaches are suitable for analyzing different situations and such are applicable when seeking to know certain information (Gordis, 2009). For instance in the cross-sectional studies, the research is often very specific in seeking analysis about the phenomenon under study; it therefore offers a wide avenue for a range of information that is needed for the research analysis. The case studies on the other hand offer a different approach in which the epidemiologists analyze the information backwards beginning from the causative effects to the suspected cause (Hern & Savitz, 2013). Through this, numerous exposures can be studied for any single outcome hence making it easy to use. The cohort studies take into account the levels of exposure of the study population regardless of the exposure levels of the group. The biases in epidemiology can also be categorized into two; these are the selection bias and the information bias. All these relate to the information that is needed in epidemiological studies. References Cort, R. (2009). Book Review:Environmental Epidemiology. Volume 2: Use of the Gary Literature and Other Data in Environmental Epidemiology Committee on Environmental Epidemiology. The Quarterly Review of Biology, 74(4), 504. Gordis, L. (2009). Epidemiology (4th ed.). Philadelphia: Elsevier/Saunders. Hernán, M. A., & Savitz, D. A. (2013). From Big Epidemiologyto â Colossal Epidemiology. Epidemiology, 24(3), 344-345. Olsen, J. (2010). Concepts of Epidemiologyâ”An Integrated Introduction to the Ideas, Theories, Principles and Methods of Epidemiology. Epidemiology, 14(3), 380. Samet, J. M. (2009). Concepts of Epidemiology: Integrating the Ideas, Theories, Principles and Methods of Epidemiology, 2nd Edition: By Raj Bhopal. American Journal of Epidemiology, 169(8), 1041-1042. Steenland, K. (2009). Case studies in occupational epidemiology. New York: Oxford University Press. Read More