Post a brief description of the article that you selected,

including an explanation of the relationship between exposure and disease.

This study was designed to explore the association between Covid -19 exposure and adverse outcomes during pregnancy and birth. The authors used a control group to compare non covid and covid-19 pregnancies to find out if there is an association with health outcomes.The study was conducted in a Hospital over 3 months with 61 cases of covid pregnancies and a matching 2:1 ratio for the control group for a total of 122 pregnant women assessed from week 16 to 41 of gestation. The maternal outcomes observed included preeclampsia, thromboembolism, Hospital Admission before the due date, ICU admission, mechanical ventilation, need for oxygen and maternal death. The newborn outcomes included respiratory distress, internal hemorrhage , enterocolitis , low Apgar score <5 ,low fetal heart rate tracing m, and neonatal death. The authors also explored risk factors associated with maternal and neonatal outcomes in terms of illness severity and compared positive cases with the control group and concluded that mild covid cases had similar outcomes to the matched negative cases while severe covid 19 illness had the worse outcomes after controlling for previous comorbidities (Diabetes, Hypertension, renal disease) , race , age, Obesity, and preterm birth (Brandt et al., 2021).

Describe the confounders that the authors identified.

The Analysis was completed using a conditional Logistic Regression and it was adjusted for confounders which were described as advanced maternal age, obesity, maternal race, and comorbid medical problems. These factors have been previously described as possible influencers of negative outcome during pregnancy and birth (Brandt et al., 2021).

Describe a variable not measured in the article that might also confound the relationship between exposure and disease and explain why. Explain one potential effect that the non-measured variable might have on the relationship and explain how.

Explain one way that you could counteract the effects of that non-measured variable.

Depression during pregnancy is a variable not measured in the study, and it can contribute to adverse outcomes during pregnancy(Khoury et al., 2021). I would add the question of the presence of depression for all individuals as a nominal variable to the study.This could be able to prove that depression is another possible factor influencing the health outcomes of the pregnancies, when controlling for depression in both groups.

References

Brandt, J. S., Hill, J., Reddy, A., Schuster, M., Patrick, H. S., Rosen, T., Sauer, M. V., Boyle, C., & Ananth, C. V. (2021). Epidemiology of coronavirus disease 2019 in pregnancy: risk factors and associations with adverse maternal and neonatal outcomes. American Journal of Obstetrics and Gynecology, 224(4), 389.e1-389.e9. https://doi-org.ezp.waldenulibrary.org/10.1016/j.a…

Khoury, J. E., Atkinson, L., Bennett, T., Jack, S. M., & Gonzalez, A. (2021). COVID-19 and mental health during pregnancy: The importance of cognitive appraisal and social support. Journal of Affective Disorders, 282, 1161–1169. https://doi-org.ezp.waldenulibrary.org/10.1016/j.j…

2 days ago

Carlin Nelson
RE: Discussion 1 – Week 6
COLLAPSE
Post a brief description of the article that you selected, including an explanation of the relationship between exposure and disease.
Jones-Smith et al. conducted a cross-sectional research design to evaluate whether an association existed between neighborhood food environment and obesity and if there were a difference in those associations by income and race/ethnicity. The total sample size was 20,188 members in Northern California who were apart of the Kaiser Permanente healthcare system that were diagnosed as diabetic. In this study the dependent variable was obesity which was measured and categorized by the parameters of the Body Mass Index (BMI), calculated as a ratio of weight to height with obesity being identified as having a BMI 30 or over (Jones-Smith et al., 2013). The independent variable was food net score which was a net score calculated as the difference between healthful and unhealthful vendors. Utilizing logistic regression, it was reported that healthful food environments were associated with lower obesity in the highest income groups amongst those who identified as Whites, Asians and Latinos. Additionally, there was a non-significant negative association about healthful food environments and lower obesity rates among African Americans/Blacks who earn a high income. Furthermore, it was reported that in low-income groups there was an association between more healthful food environments and higher obesity but statistically significant in black participants.
Diabetes mellitus is a group of chronic diseases describing the body’s inability to produce insulin (type 1), utilize insulin (type 2) or both resulting in hyperglycemia and abnormal metabolism (American Diabetes Association,2009). Insulin is a hormone secreted by the pancreas that assist in energizing the body with glucose (Roder et al., 2016). Chronic high glucose levels in the blood can affect the body by reducing blood flow which serves as nutrients for tissues, organs and organ systems (National Kidney Function, 2020). A major risk factor for diabetes is obesity which an be caused by socioeconomic status (SES) and accessibility to healthy food. There has been plenty of research which highlights those affluent neighborhoods often have access to healthier food options, parks/recreation centers, sidewalks and lower crime rates whereas neighborhoods with low SES are often plagued with many fast-food options, liquor stores and other unhealthy tactics.
Describe the confounders that the authors identified. Describe a variable not measured in the article that might also confound the relationship between exposure and disease and explain why.
The authors identified variables that distorted their selection of neighborhoods and obesity status as confounders which they hypothesized from literature reviews and directed acyclic graphs (DAGs). The reported confounders were race/ethnicity, family income, neighborhood deprivation, nativity, age, sex, education, marital status, and baseline comorbidity score (Jones-Smith, 2013). A variable that the article did not take into consideration was personal preference. This could be a confounder because it can influence both food choices as well as the choice of neighborhood to live in.
Explain one potential effect that the non-measured variable might have on the relationship and explain how. Explain one way that you could counteract the effects of that non-measured variable.
One potential effect that not including this variable in the analysis may have on the relationship is not providing a holistic understanding of the relationship. Public Health often emphasizes the consideration of the social determinants of health and their influences on health outcomes. Not realizing the impact that personal preference has on “both the selection into neighborhoods with a certain type of food environment and the food choices with that environment “ as well as the social factors that may influence personal preferences may assist explaining the relationship/association reported as noncausal (Jones-Smith, 2013). To counteract the effects of that non-measured variable, it may be possible to find a scale that measures preference such as the Edwards Personal Preference Schedule (EPPS) to have included in the analysis.
References
American Diabetes Association (2009). Diagnosis and classification of diabetes mellitus. Diabetes care, 32 Suppl 1(Suppl 1), S62–S67. https://doi.org/10.2337/dc09-S062
Jones-Smith, J. C., Karter, A. J., Warton, E. M., Kelly, M., Kersten, E., Moffet, H. H., Adler, N., Schillinger, D., & Laraia, B. A. (2013). Obesity and the food environment: income and ethnicity differences among people with diabetes: the Diabetes Study of Northern California (DISTANCE). Diabetes care, 36(9), 2697–2705. https://doi.org/10.2337/dc12-2190
National Kidney Foundation . (2020, October 30). Diabetes and your eyes, heart, nerves, feet, and kidneys. National Kidney Foundation. https://www.kidney.org/atoz/content/Diabetes-and-Your-Eyes-Heart-Nerves-Feet-and-Kidneys.
Röder, P. V., Wu, B., Liu, Y., & Han, W. (2016). Pancreatic regulation of glucose homeostasis. Experimental & molecular medicine, 48(3), e219. https://doi.org/10.1038/emm.2016.6