Abstract
Background: Inflammatory bowel disease has many predisposing factors. Genetics, lifestyle, socio-economic status may all play a role. The National Health and Nutrition Examination Survey (NHANES) combines data from interviews and physical examinations from approximately 5000 people each year in the United States. It is an excellent source for acquiring nationally representative data on known inflammatory bowel disease risk factors. By its nature, survey data, such as from NHANES, frequently has missing entries. Multiple imputation provides a statistically robust way to handle missingness. Rather than discarding partially complete entries in a listwise manner, multiple imputation uses a Bayesian model to produce multiple datasets that include uncertainty on the missing data. The datasets are then re-combined to provide a complete dataset with more accurate standard errors than would be obtained by other imputation methods.
Methods: We used the R statistical programming language to download and process anonymized NHANES data from the 2009-2010 data acquisition cycle. Several parameters known or suspected to have a bearing on bowel health were analyzed. Multiple imputation was used to handle missingness in the data. Logistic regression was carried out the parameters using the presence of inflammatory bowel disease as the dependent variable.
Results: Preliminary results in this study show that individuals with inflammatory bowel disease tend to have a higher C-reactive protein, body mass index, LDL-cholesterol, triglycerides, and A1C. They also tend to have a higher monthly income and smaller family size.
Conclusion: It is important to consider the impact of both health parameters and socioeconomic parameters on bowel health. However, the differences between those diagnosed with inflammatory bowel disease and those not diagnosed should not be taken as causative. For example, while it could be the case that the lifestyle habits of people with higher monthly income predispose them to develop inflammatory bowel disease, it is also possible that those with higher income are more likely to seek medical care for their symptoms and thus be diagnosed.
Methods: We used the R statistical programming language to download and process anonymized NHANES data from the 2009-2010 data acquisition cycle. Several parameters known or suspected to have a bearing on bowel health were analyzed. Multiple imputation was used to handle missingness in the data. Logistic regression was carried out the parameters using the presence of inflammatory bowel disease as the dependent variable.
Results: Preliminary results in this study show that individuals with inflammatory bowel disease tend to have a higher C-reactive protein, body mass index, LDL-cholesterol, triglycerides, and A1C. They also tend to have a higher monthly income and smaller family size.
Conclusion: It is important to consider the impact of both health parameters and socioeconomic parameters on bowel health. However, the differences between those diagnosed with inflammatory bowel disease and those not diagnosed should not be taken as causative. For example, while it could be the case that the lifestyle habits of people with higher monthly income predispose them to develop inflammatory bowel disease, it is also possible that those with higher income are more likely to seek medical care for their symptoms and thus be diagnosed.
Original language | American English |
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Pages | 29 |
State | Published - 16 Feb 2024 |
Event | Oklahoma State University Center for Health Sciences Research Week 2024 - Oklahoma State University Center for Health Sciences, Tulsa, United States Duration: 13 Feb 2024 → 17 Feb 2024 https://medicine.okstate.edu/research/research_days.html |
Conference
Conference | Oklahoma State University Center for Health Sciences Research Week 2024 |
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Country/Territory | United States |
City | Tulsa |
Period | 13/02/24 → 17/02/24 |
Internet address |
Keywords
- inflammatory bowel disease
- Crohn’s
- ulcerative colitis
- multiple imputation
- logistic regression
- NHANES
- R programming language