A case-control study was performed to determine whether the head injury was associated with an increased risk of brain tumors in children. 200 cases with brain cancer were identified from the cancer registry, and 200 controls were recruited from the same neighborhoods where the cases lived. The mothers of the children completed a questionnaire that asked them to describe their children’s history of head injury. The investigators found that the mothers of the children with brain tumors reported a past head injury for 70 of the cases while a history of the head injury was reported in 30 of the controls. What type of bias was likely to have influenced the findings of this study, and why? What can be done to minimize this type of bias?
A case-control study is a form of retrospective type of research. In retrospective study, information is gathered from the past. The kind of bias from the research that was likely to influence the findings of the case-control study was the recall bias. This type of bias takes place when the participants cannot recall their preceding events or experiences correctly or, at times, missing out some details (Aschengrau, & Seage, 2020). The participants do not usually have a complete or accurate picture of what happened. In the case study, the participants involved in the study had cases of brain tumors. Some of the mothers with brain tumors maybe did not have a clear picture of what happened or could not remember their past events. Patients with brain tumors tend to forget or what is termed as memory difficulties. Interviewer bias could also take place in the given case. This is because of the differences in the data collection methods, interpretation, and procedures used by the interviewers.
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The recall bias can be minimized by using a prospective study which can eliminate reporter bias. A prospective study is when the research involves a patient’s follow up for a specific time instead of using past events: this can be implemented by choosing participants with a new diagnosis if possible and making a follow up on the patients (Aschengrau, & Seage, 2020). To minimize interviewer bias, the researcher should define the research question carefully: this is for a more natural understanding of the main objective of the study. The interviewer should also choose and implement appropriate data collection and analysis methods.
Question 2
Consider each of the following scenarios and state whether or not the variable in the question is a confounder, and why. A study of the risk of pulmonary hypertension among women who take diet drugs to lose weight. The crude relative risk of pulmonary hypertension comparing diet drug users to non-users is 17.0, and the age-adjusted relative risk is 5.0. Is age a confounder in this study?
Age is the confounder variable in this study; this is because it is used to weigh the age-specifics rates observed among women who take drugs to lose weight and those who don’t take the drugs by the proportion of each age group in a standard population (Aschengrau, & Seage, 2020). Hence, age had an indirect influence on the risk of pulmonary hypertension among women who take drugs to reduce weight.
A cohort study of liver cancer among alcoholics. Incidence rates of liver cancer among alcoholic men are compared to a group of non-alcoholic men. Is gender a confounder in this study?
Gender is the confounding variable in this study: this is because the outcome variable is liver cancer, and the independent variable is alcoholics. The male gender are affected by the rates of alcohol intake and liver cancer is predominant among alcoholics.
A case-control study of the risk of beer consumption and oral cancer among men. In this study, cigarette smoking is associated with beer consumption and is a risk factor to oral cancer among both beer drinkers and nondrinkers. Is cigarette smoking a confounder in this study?
Cigarette smoking is a confounder variable. In the study, the dependent variable or the outcome variable is oral cancer, and the independent variable is beer consumption. Cigarette smoking is a confounder variable because it affects both the outcome variable and the independent variable but not directly. There is an indirect relationship between beer consumption and oral cancer among men, where cigarette smoking can cause cancer and also a risk factor to beer consumption.
Question 3
A study followed 900,000 US adults from 1992 t0 2008. At baseline, all participants were screened and determined to be cancer-free and their body mass index (BMI) was calculated. Body mass index is a measure of obesity that is calculated using a person’s height and weight. Subjects were separated into the following groups according to their BMI: (a) normal weight, (b) slightly overweight, (c) moderately overweight and (d) greatly overweight. 57,145 deaths from cancer occurred in the population during the follow-up period. The following results were seen for men and women when the heaviest members of the cohort (greatly overweight) were compared to those with normal weight:
Men: Risk ratio of cancer death = 1.5, 95% confidence interval = 1.1-2.1
Women: Risk ratio of cancer death = 1.6, 95% confidence interval = 1.4-1.9
State in words your interpretation of the risk ratio given for the men . In this study, the risk ratio indicated that men that were considerably overweight had 1.5 times the risk of cancer deaths compared to men that were normal weight. We are 95% confident that the risk of greatly weighted men and the normal weighted dying from cancer is between 1.1 -2.1.
State in words your interpretation of the risk ratio given for the women . The women that were greatly overweight in this study had 1.6 times the risk of cancer deaths compared to the women that were normal weight. We are 95% confident that the risk of women who are greatly weighted and normal weighted dying from cancer is between 1.4-1.9 (Aschengrau, & Seage, 2020).
Does gender confounds these results. The results are confounded by gender. The risk of greatly weighted women and the regular weighted dying from cancer is higher than for men who are greatly weighted and normal weighted dying from cancer. The gender distribution is unequal, and hence gender is a confounding variable. Gender affects the outcome variable, which is death from cancer and also the BMI.
The authors stated that they controlled for confounding many risk factors using multivariate analysis. State an alternative method that the authors could have used to control for the confounding in the design or analysis. Also, name two confounding variables that you think should be controlled using this method.
Confounding can also be controlled by standardization. Standardization entails making use of a target population, acquired from either the data source or an exterior source. Standardization mostly incorporates two main approaches to handle confounding: the direct and the indirect standardization, which leads to adjusted rates and standardized ratios. The direct standardization is proposed since the steadiness of comparisons is maintained when a higher rate in one reserch in a study population in comparison to another is upheld. Indirect standardization is highly recommended in situations where unstable rates are encountered across stratum (Aschengrau, & Seage, 2020) . In the study, the variables the confounding variable that should be controlled are age and gender. Disease or death rates are only standardized to age, race, gender (Aschengrau, & Seage, 2020). Since the study investigates cancer deaths the variables that should be confounded are age and gender.
References
Aschengrau, A., & Seage, G. R., III. (2020). Essentials of epidemiology in public health (4th ed.). Burlington, MA: Jones & Bartlett.
