Discussion response in 12 hours or less

  

Respond to the Classmate’s Discussion (below) to the above question as you would in a face-to-face class by stating why you agree as pertaining to the discussion and by probing questions. Your response posts need to be at least 200 words each. Please be sure to validate your opinions and ideas.

                                                              CLASSMATE’S DISCUSSION

Investigating the correlation between the number of children a woman bears and her risk of stroke can uncover vital insights for public health. Since stroke is a major cause of morbidity and mortality globally, recognizing such risk factors is instrumental in crafting effective prevention and management strategies (Alene et al., 2020). The study, however, might grapple with several biases and confounders. Connelley (2023) depicts that anticipating and controlling these biases and confounders can lead to more valid research outcomes. Selection bias could result in an unrepresentative sample due to non-random participant selection. Participants may be more readily accessible, willing, or responsive, leading to skewed results. To counter this, employing random sampling strategies ensures equal chances of selection for all eligible individuals. Another concern is information bias, which manifests as a systematic error in data collection. For instance, recall bias could pose a problem if participants’ memories are the source for reporting the number of childbirths. Therefore, reliable data sources, such as medical records, should be utilized for accuracy.

Beyond biases, confounders may distort the true relationship between childbirth and stroke risk. Age, for instance, is a common confounder as older women are more likely to have had more children and have a higher risk for stroke. Data stratification by age groups or multivariate analysis helps adjust for this age effect. Socio-economic status could also confound the study as it influences the number of children a woman has and her health status, including the risk of stroke. Lower socio-economic status may be associated with having more children and higher stroke risk due to stress, poor nutrition, and lack of healthcare. Therefore, it is essential to collect data on socio-economic indicators and adjust for these during analysis. Lastly, lifestyle factors like smoking, diet, and physical activity can impact fertility and stroke risk. For instance, a woman who smokes may have fewer children and a higher risk for stroke. Collecting data on these lifestyle factors and adjusting for them can help minimize confounding. 

                                                           References

Alene, M., Assemie, M. A., Yismaw, L., & Ketema, D. B. (2020). The magnitude of risk factors and in-hospital mortality of stroke in Ethiopia: A systematic review and meta-analysis. BMC Neurology, 20(1), 1-10.

Connelley, D. (2023, April 18). Unit V study guide [PDF Document]. University of Southern Columbia.

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