Research Question and Rationale
Research Question
Does high chronic stress increase the risk of developing rheumatoid arthritis among adults aged 18-65 years in Canada over the last decade, considering both subjective and objective measures?
The Rationale
The population represents adults within the age range of 18-65 years old in Canada, representing a diverse society that is prone to Rheumatoid Arthritis (RA). Hazlewood et al. (2022) inform the fact that RA remains a recognized form of inflammatory arthritis that is prevalent in 1.2% of Canadians over the age of 16. Individuals who are diagnosed with RA experience pain, fatigue, limited function, job loss, and a diminished quality of life. Elderly individuals are highly affected by rheumatoid arthritis, which makes them a relevant group to investigate (Hazlewood et al., 2022). Exposure to RA results in elevated chronic stress, a multifactorial psychosocial phenomenon with elemental immune functioning and inflammatory routes (Liu et al., 2021). According to Hitchon et al. (2020), chronic stress emanating from RA results in psychological strain through multiple psychosocial factors that range from work-related stress to financial pressure and interpersonal conflicts. Therefore, comprehension of this relationship will be the source of public health approaches that aim to lower the impact of chronic stress on rheumatoid arthritis prevalence, aiming to improve the health and wellness of the Canadian population.
Advantages and Disadvantages of Prospective Cohort Study Design
Advantages
The Framingham Heart Study, the Nurses’ Health Study, and the Nun Study exemplify the inherency of cohort studies still being preserved in epidemiological research as a prospective cohort design. One of the primary advantages of prospective cohort design is long-term planning, which involves a specific and prominent design and employing trained staff to warrant the reliability of measurement of all the relevant variables (Werner‐Seidler et al., 2023). This point in time helps to record the correct presentation of variables that contribute to exposure to their fullest extent, which is demanded by in-depth research and analysis of data for clarity and precision.
Moreover, another advantage is the study provides the option of designing the method to fit the data collection, and thus, better data would be obtained, which could lead to more complete and accurate information. The accuracy of the data used fosters the result’s reliability and variability to allow extrapolation in future research (Werner‐Seidler et al., 2023). Furthermore, these studies permit the differentiation of the order of succession between an antecedent exposure and an outcome in time, which is a pertinent criterion in determining the causal relationship. Also, cohort studies allow researchers to calculate incidence rates and relative risks and to produce information regarding the strength of the association between exposure and the outcome occurrence (Andrade, 2022). The cohort characterizes the exposure information of their subjects throughout their participation time, which helps the discovery of the dose-response relationship and the research of the possible mechanisms that exist to explain the results of the associations.
Disadvantages
A specific drawback of prospective cohort study is the long time it takes to implement and follow up, mainly when waiting for the diseases or events to be revealed, which is time-consuming and a financial expense. The expanded timeframe renders such studies ineffective in terms of researching diseases with long periods of latency because it may take years or even decades to see observable outcomes (Andrade, 2022). Besides, the extended follow-up period increases the risk of losing participants, who may come and go from the study under consideration, which, therefore, needs to be completed for complete data and information biasing the results (Andrade, 2022). Also, selection bias is the most insecure among prospective cohort studies when differential losses exist in the exposed and unexposed groups during the follow-up (Werner‐Seidler et al., 2023). The biases result in inaccuracies of the observed association between the exposure and the outcome and even exaggerate the association (Werner‐Seidler et al., 2023). These limitations again highlight the importance of careful planning and powerful strategies to prevent bias and maximize the credibility of findings in future prospective cohort studies.
Main Elements of the Study Design
Source Population
Adults aged 18-65 years in Canada over the last decade.
Exposed and Unexposed Definitions
Exposed: Individuals reporting high chronic stress levels assessed through a validated Stress Scale.
Unexposed: Individuals reporting low to moderate chronic stress levels.
Outcome Definition
Diagnosis of rheumatoid arthritis is based on clinical criteria and confirmed by medical records or physician diagnosis.
Data Sources
Subjective Measures: Self-reported stress levels through questionnaires administered at baseline and follow-up visits.
Objective Measures:Biomarkers of stress such as cortisol levels obtained from blood samples collected at baseline and follow-up visits.
A perfectly designed cohort study provides valuable implications for the issue at hand. The study involves determining the relationship of the population of adults aged 18-65 from Canada taken during the last decade to two groups: one which has been exposed to stress, and the other which is free from anxiety, measured on a validated stress scale. Additionally, the cohort will be monitored to check the progression of rheumatoid arthritis, and if clinically diagnosed, it will be confirmed by either medical records or a doctor’s diagnosis. This type of research design comprises two primary measures: subjective and objective. It is noteworthy that cohort researches have a temporal frame that enables the examination of causality between exposure and outcome (Andrade, 2022). These aggregate the solid scientific evidence due to the already exposed cases before the occurrence of the effect. Although cohort studies call for large study groups, the ability to detect the associations correctly depends on their size.
Potential Biases and Confounding
Selection Bias
One selection bias likely to emerge in this study design is due to the participant enrollment process. As Ramirez-Santana (2018) explains on selection bias, when the subjects of the study include people who have rheumatoid arthritis or chronically stressed patients, this could lead to an unnecessarily skewed sample, which does not fairly represent the whole population. The bias contributes to either an overestimation or an underestimation of the actual correlation between chronic stress and rheumatoid arthritis since the sample might need to be an accurate representation of the population of adults living in Canada between the ages of 18 and 65. Hence, the random sampling option should be used to ensure that everyone will be equally represented to give a holistic view of the population (Ramirez-Santana, 2018). One of the practicable areas of focus is, that the members should be selected from a diversity of groups to reduce sampling bias and therefore, improve the accuracy level.
Measurement Bias
The measurement bias, leading to inaccurate rheumatoid arthritis (RA) scales is probably due to subjective and objective misinterpretations of the chronic stress and the arthritis scales used in the study which contributes to measurement bias. The singular markers of emotions such as people self-reporting their level of stress may be completely untrue because of a bias perspective or their own individual perspective. As per the studies of Wang and Kattan (2020), the presence of subjective measures as stress biomarkers or that which better explain the nature of rheumatoid arthritis in the experiment can make some statistical errors. Inadequacy in measurements can lead to an overestimation of the role of chronic stress in the onset of rheumatoid arthritis (Wang & Kattan, 2020). Reducing measurement bias requires using consistent and standard instruments to measure chronic stress and rheumatoid arthritis. Calibration exercises, consistency checks, and regular training of assessors will help reduce measurement errors and produce high-quality data.
Confounding
Confounding variables like age, gender, socioeconomic status, and lifestyle factors contribute to the link between chronic stress and rheumatoid arthritis. For example, suppose people with high chronic stress present a tendency to unhealthy behaviors that enhance the risk of developing rheumatoid arthritis (Assimon, 2021). In that case, the cited association can be confounded with the factors mentioned above. Confusion of confounders undermine the mechanism of long-term stress on the gaining of rheumatoid arthritis (Assimon, 2021). The use of statistical analysis, such as multivariable regression analysis, helps control confounders’ impact in a study (Assimon, 2021). The adjustment of the analysis for covariates relevant to it allows researchers to detect the independent effect of chronic stress on the development of rheumatoid arthritis and reduce the impact of confounding bias.
Impact Statement
This study considers the stress of long-lasting chronic stress and the resulting risk of rheumatoid arthritis in the adult population, which will have a significant impact on public health issues and individuals’ well-being. Establishing the possible link between chronic stress and rheumatoid arthritis among adults aged 18-65 in Canada over the past decade will close a crucial gap in knowledge regarding the genesis of this disabling autoimmune disease. Rheumatoid arthritis scourges a large cohort of the Canadian population and leads to many disadvantages, such as discomfort, functional limitations, joblessness, and poor quality of life (Lwin et al., 2020). Hence, research on chronic stress leading to the development of rheumatoid arthritis risk is significant since it can help in the formulation of targeted interventions to reduce stress and avert its consequences. Also, the combination of subjective and objective measures of stress with the overall data collection approach highlights the strength of the study design, contributing to the validity of the obtained results and their practical use (Vergne‐Salle et al., 2022). In the end, the findings will guide evidence-based public health strategies that aim to reduce the impact of chronic stress on rheumatoid arthritis incidence enhancement in the Canadian population’s health and wellness.
References
Andrade, C. (2022). Research design: cohort studies. Indian Journal of Psychological Medicine, 44(2), 189-191. DOI: https: 10.1177%2F02537176211073764
Assimon, M. M. (2021). Confounding in observational studies evaluating the safety and effectiveness of medical treatments. Kidney360, 2(7), 1156-1159. DOI: 10.34067/KID.0007022020
Hazlewood, G. S., Pardo, J. P., Barnabe, C., Schieir, O., Barber, C. E., Proulx, L., … & Pope, J. E. (2022). Canadian Rheumatology Association living guidelines for the pharmacological management of rheumatoid arthritis with disease-modifying antirheumatic drugs. The Journal of Rheumatology, 49(10), 1092-1099. https://www.jrheum.org/content/jrheum/early/2022/07/14/jrheum.220209.full.pdf
Hitchon, C. A., Khan, S., Elias, B., Lix, L. M., & Peschken, C. A. (2020). Prevalence and incidence of rheumatoid arthritis in Canadian First Nations and non–First Nations people: a population-based study. JCR: Journal of Clinical Rheumatology, 26(5), 169-175. DOI: 10.1097/RHU.0000000000001006
Liu, X., Barber, C. E., Katz, S., Homik, J., Bertazzon, S., Patel, A. B., … & Marshall, D. A. (2021). Geographic variation in the prevalence of rheumatoid arthritis in Alberta, Canada. ACR Open Rheumatology, 3(5), 324-332. DOI: 10.1002/acr2.11251
Lwin, M. N., Serhal, L., Holroyd, C., & Edwards, C. J. (2020). Rheumatoid arthritis: the impact of mental health on disease: a narrative review. Rheumatology and therapy, 7(3), 457-471. DOI: 10.1007/s40744-020-00217-4
Ramirez-Santana, M. (2018). Limitations and biases in cohort studies. DOI: 10.5772/intechopen.74324
Vergne‐Salle, P., Pouplin, S., Trouvin, A. P., Bera‐Louville, A., Soubrier, M., Richez, C., … & Bertin, P. (2020). The burden of pain in rheumatoid arthritis: Impact of disease activity and psychological factors. European Journal of Pain, 24(10), 1979-1989. DOI: 10.1002/ejp.1651
Wang, X., & Kattan, M. W. (2020). Cohort studies: design, analysis, and reporting. Chest, 158(1), S72-S78. DOI: 10.1016/j.chest.2020.03.014
Werner‐Seidler, A., Maston, K., Calear, A. L., Batterham, P. J., Larsen, M. E., Torok, M., … & Christensen, H. (2023). The Future Proofing Study: Design, methods and baseline characteristics of a prospective cohort study of the mental health of Australian adolescents. International Journal of Methods in Psychiatric Research, 32(3), e1954. DOI: 10.1002/mpr.1954
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