Abstract
There is a growing recognition that social determinants of health (SDOH) influence outcomes in patients with chronic diseases. This study aimed to investigate the influence of SDOH on outcomes in patients with inflammatory bowel disease (IBD). We conducted a retrospective cohort study of adult patients with IBD from 1996 to 2019. Patients were identified using ICD-10 codes for ulcerative colitis and Crohn’s disease, and chart review was performed to validate the diagnosis and extract clinical information. SDOH factors including food security, financial resources, and transportation were self-reported by the patient. Random forest models were trained and tested in R to predict either IBD-related hospitalization or surgery. A total of 175 patients were studied, and the majority reported no financial resource, food security, or transportation concerns. For the model using clinical predictors, the sensitivity was 0.68 and specificity was 0.77 with an area under the receiver operating characteristic curve (AUROC) of 0.77. The model’s performance did not significantly improve with the addition of SDOH information (AUROC of 0.78); however, model performance did vary by phenotype (AUROC of 0.86 for patients with Crohn’s disease and AUROC of 0.68 for patients with ulcerative colitis). Further research is needed to understand the role of SDOH factors and IBD-related outcomes.
Keywords: Crohn’s disease, inflammatory bowel disease, social determinants of health, ulcerative colitis
Inflammatory bowel disease (IBD), encompassing both Crohn’s disease (CD) and ulcerative colitis (UC), is a group of chronic inflammatory disorders of the intestines resulting in diarrhea, rectal bleeding, and abdominal pain. Patients with UC may experience severe flares that require hospitalization and have a 15.6% risk of surgery 10 years after their diagnosis.1,2 In patients with CD, persistent inflammation can lead to complications such as intestinal strictures and fistulas that commonly require hospitalization and surgery for management.3 Despite improved medical therapies for CD, the annual incidence of admissions to the hospital is around 20%, and within 10 years of their diagnosis nearly 50% of patients require surgery.4 Consequently, determining predictors of adverse outcomes such as hospitalizations and surgeries has been an active area of research.
Multiple clinical predictors have been identified to stratify patients into groups at low and high risk for adverse outcomes. In UC patients, young age of onset (<40 years), pancolitis, severe endoscopic disease, prior hospitalizations, need for steroids, and prior intestinal infections are risk factors for hospitalizations and colectomy.5 In CD patients, ileal, perianal, or upper gastrointestinal tract involvement, noninflammatory phenotypes such as strictures and fistulas, young age of onset, and use of tobacco are risk factors for complications.6 Current recommendations focus on using these clinical predictors to guide treatment decisions by stratifying patients according to risk for adverse outcomes.7 However, despite improved understanding of these predictors and advances in medical therapies, a significant proportion of patients continue to be hospitalized and require surgery to manage their disease.8
There is limited research regarding social determinants of health (SDOH) and adverse outcomes in patients with IBD. A growing body of literature has shown that SDOH can strongly influence a myriad of health outcomes in other chronic diseases.9 These factors can broadly be categorized as food security, housing stability, socioeconomic status (SES), and transportation access.10 Based on the National Health Interview Study in 2015, approximately 42% of patients with IBD were noted to have difficulty with either food security or social support. The presence of these factors was associated with increased financial difficulties, but interestingly not an increased use of the emergency room.11 In a separate study from Manitoba, patients with IBD and at least one marker of lower SES were noted to have worse outcomes (hospitalizations, intensive care unit admission, and death) compared to patients with no markers.12 However, these studies were based on population-level data with lack of granular clinical data, such as disease extent and phenotype, which are known to influence outcomes. Therefore, we sought to address this gap by examining both clinical and SDOH factors influencing adverse outcomes in patients with IBD.
METHODS
This retrospective cohort study identified adult patients with either UC or CD who received treatment within the Baylor Scott & White Health (BSW) system between 1996 and 2019. BSW is the largest hospital system in the state of Texas with a diverse population of patients. The system cares for approximately 2 million unique patients annually with about 7 million patient encounters per year. All patient data were extracted from the BSW electronic medical record (Epic), which contains comprehensive demographic and clinical information. The BSW institutional review board approved the study. All methods were performed in accordance with the relevant guidelines and regulations.
Inclusion criteria were an age ≥18 years, ICD-10 codes for UC (K51.*) or CD (K50.*), and disease confirmation via endoscopy, pathology, or imaging verified by manual chart review. Exclusion criteria were patients with UC and a prior total proctocolectomy, incomplete SDOH data, incomplete follow-up, or inability to accurately assess the outcomes of interest.
The BSW Epic EMR contains both clinical information (disease phenotype, disease extent, disease behavior) and demographic information (age at index, sex, race/ethnicity). Manual chart review was used to confirm the diagnosis of either UC or CD, as well as determine disease extent and behavior. Disease extent and disease behavior were categorized using the Montreal Classification System. Tobacco and alcohol use are updated at each encounter, and the entry immediately before either the outcome of interest for cases or first visit for controls was obtained. Tobacco use was classified as either yes/current, no, or former, and alcohol use as either yes or no.
SDOH information was entered either by the patient through the online check-in system or by a medical assistant on the day of the visit. SDOH variables included financial resource strain, depression assessment (Patient Health Questionnaire [PHQ]-2), food security (worried or scared), and transportation (medical or for activities of daily living [ADL]). Financial resource strain was categorized as very hard, somewhat hard, not very hard, or not hard at all. Depression assessment with the PHQ-2 score ranged from 0 to 6, with 6 being at higher risk for a depression diagnosis. Food security was stratified into two categories (worried or scared) with responses being either never true, sometimes true, often true, patient refused, or not asked. Finally, transportation concerns for either medical or ADL purposes were categorized either yes or no.
Data were stored in a password-protected file behind a firewall maintained by the BSW system to maintain patient confidentiality. Two data files were created, with one containing only clinical and demographic information and the other containing this information, in addition to the SDOH information.
The outcome of interest was a composite of either IBD-related hospitalization or surgery after the patient’s initial clinic visit. IBD-related hospitalizations were identified based on coding of the hospitalization (either primary or secondary diagnosis) for UC or CD. IBD-related surgeries were identified based on CPT codes for IBD-related surgeries (Supplementary Table 1). Manual chart review was used to confirm IBD-related hospitalizations and surgeries.
Models were trained and tested using a random forest algorithm, a supervised machine learning method. A stepwise approach was taken with training and testing the performance of the models. The first model included only the clinical and demographic information. The second model added the SDOH information. The final models were then divided into patients with either UC or CD. The data were inspected prior to model construction to determine if imputation was needed for missing values or class imbalance. To decide which model would have the best predictive performance between the outcome of interest and predictors, three separate models were built with the following train and test splits: 60/40, 70/30, and 80/20. The two main parameters used when constructing the model were the number of trees (ntree = 500) and the number of variables for the split of each node when building the trees (mtry = 3). All statistical analyses were done in R version 4.0.3, with the packages dataexplorer, dplyr, missForest, skimr, ggplot2, randomForest, caret, ROCR, tidyr, and correlationfunnel.
RESULTS
Baseline characteristics
Over the study period, 311 unique patients were identified who had complete clinical information and provided SDOH information. After applying our inclusion and exclusion criteria, 175 patients were included in our analysis, and 75 patients experienced the outcome of interest. The baseline characteristics of the population are summarized in Table 1. The mean age of patients with an IBD-related hospitalization or surgery was 44.99 years, and they were predominantly female (69%). Most patients were non-Hispanic White. The predominant phenotype was inflammatory CD in both groups. The majority of patients did not report any financial resource strain or screen as having a high risk of depression (low PHQ-2 score). Additionally, most patients did not report being worried or scared about food security or have any transportation issues related to ADLs or medical related visits.
Table 1.
Characteristics of study population based on presence of inflammatory bowel disease–related hospitalization or surgery
| Outcome of interest |
||
|---|---|---|
| Yes (n = 75) | No (n = 100) | |
| Age (years): mean (SD) | 44.99 (17.36) | 49.90 (18.35) |
| Sex | ||
| Male | 23 (31%) | 34 (34%) |
| Female | 52 (69%) | 66 (66%) |
| Race | ||
| White | 58 (77%) | 95 (95%) |
| Black | 16 (21%) | 5 (5%) |
| Asian | 1 (1%) | 0 |
| Hispanic | 8 (11%) | 9 (9%) |
| Phenotype | ||
| UC | 34 (45%) | 37 (37%) |
| CD | 41 (55%) | 63 (63%) |
| UC disease extent | ||
| E1 | 0 | 4 (4%) |
| E2 | 2 (3%) | 13 (13%) |
| E3 | 32 (43%) | 20 (20%) |
| CD disease extent | ||
| L1 | 15 (20%) | 26 (26%) |
| L2 | 9 (12%) | 17 (17%) |
| L3 | 16 (21%) | 20 (20%) |
| CD disease behavior | ||
| B1 | 22 (29%) | 43 (43%) |
| B2 | 6 (8%) | 11 (11%) |
| B3 | 13 (17%) | 9 (9%) |
| Alcohol use | 19 (25%) | 41 (41%) |
| Tobacco use | ||
| Former | 0 | 33 (33%) |
| Never | 51 (68%) | 59 (59%) |
| Current | 24 (32%) | 8 (8%) |
| Financial resource strain | ||
| Not hard at all | 65 (87%) | 81 (81%) |
| Not very hard | 3 (4%) | 9 (9%) |
| Somewhat had | 3 (4%) | 8 (8%) |
| Hard | 1 (1%) | 1 (1%) |
| Very hard | 3 (4%) | 1 (1%) |
| PHQ-2 | ||
| 0 | 62 (87%) | 85 (85%) |
| 1 | 5 (7%) | 6 (6%) |
| 2 | 7 (9%) | 9 (9%) |
| 3 | 1 (1%) | 0 |
| Food insecurity (worried) | ||
| Never true | 67 (89%) | 96 (96%) |
| Sometimes true | 7 (9%) | 3 (3%) |
| Often true | 1 (1%) | 1 (1%) |
| Food insecurity (scared) | ||
| Never true | 68 (91%) | 98 (98%) |
| Sometimes true | 6 (8%) | 1 (1%) |
| Often true | 1 (1%) | 1 (1%) |
| Lack of medical transportation | 3 (4%) | 3 (3%) |
| Lack of ADL transportation | 3 (4%) | 4 (4%) |
ADL indicates activities of daily living; CD, Crohn’s disease; PHQ2, Patient Health Questionnaire-2; SD, standard deviation; UC, ulcerative colitis.
Accuracy of clinical and clinical with SDOH variables
The area under the receiver operating characteristic (AUROC) results for the models utilizing clinical only and clinical plus SDOH variables are shown in Figure 1a. The AUROC for the 70/30 split model using clinical data was 0.77 with a sensitivity of 0.68 and specificity of 0.77. For the 70/30 model using both clinical and SDOH data, the AUROC was 0.78 with a sensitivity of 0.73 and specificity of 0.70. For the clinical only model, the top five variables of importance were age at the time of outcome, tobacco use (current and never), disease extent (pancolitis), and current alcohol use. After adding the SDOH data, the top five variables included age at the time of outcome, current tobacco use, disease extent (pancolitis), White race, and male sex.
Figure 1.
Receiver operating characteristic curve for clinical data and clinical data with social determinants of health information for (a) all patients with inflammatory bowel disease; (b) patients with Crohn’s disease; and (c) patients with ulcerative colitis showing clinical and clinical with social determinants of health information.
Accuracy of clinical with SDOH variables by phenotype
The models performed well overall when evaluating the subset of patients with CD. The model using only clinical data with a 70/30 split had an AUROC of 0.79 with a sensitivity of 0.75 and specificity of 0.78. The top five variables of importance were age at the time of the outcome, current tobacco use, White race, never tobacco use, and disease extent (small and large intestine). When the SDOH data were added, the AUROC increased to 0.86 with a sensitivity of 0.67 and specificity of 0.89. Age at the time of the outcome, current tobacco use, and White race remained, with the addition of current alcohol use and fistulizing disease behavior (Figure 1b). No SDOH factors strongly influenced model performance.
For the patients with UC, the model trained using only clinical data performed with a sensitivity of 0.70 and specificity of 0.55, yielding an AUROC of 0.55 (Figure 1c). For the clinical and SDOH data, the variable of lack of transport for ADLs had no variance, so it was removed from the model. After removing this, the model had a sensitivity of 0.90, specificity of 0.45, and AUROC of 0.68. The top variables for both models were age at the time of the outcome, disease extent (E2 or E3), current alcohol use, non-Hispanic ethnicity (clinical only model), and male sex (combined model).
DISCUSSION
This study demonstrated that the addition of SDOH variables to prediction models for IBD-related hospitalizations and surgeries can influence their performance, particularly when stratified by phenotype. Generally, our models performed better in patients with CD compared to those with UC, and the models performed better with SDOH data compared with only clinical information. Established risk factors for adverse outcomes were identified in this study, including tobacco use and disease extent (patients with UC), but SDOH factors may influence outcomes based on phenotype.
Multiple studies have established clinical predictors and risk factors for adverse outcomes in CD and UC. In patients with CD, younger age at diagnosis, ileal or perianal involvement, tobacco use, and noninflammatory phenotypes are associated with adverse outcomes such as need for surgery.13 For patients with UC, greater disease extent, younger age at diagnosis, deep ulcers on endoscopy, high C-reactive protein/erythrocyte sedimentation rate, need for steroids, history of hospitalizations, and infections (Clostridium difficile or cytomegalovirus) are associated with increased risk for surgery. When restricted to clinical predictors for patients with CD, we noted tobacco use and disease extent were the variables with the strongest influence on model performance. Additionally, we did appreciate a weaker influence of previously reported risk factors, including disease behavior. Similar results were seen in patients with UC, where disease extent and tobacco use were the variables most influencing performance of the final model. Therefore, these results are consistent with well-established risk factors for adverse outcomes in patients with CD or UC.
There is a complex and incompletely understood relationship between diet and disease activity in patients with IBD. Multiple studies have shown that exclusive enteral nutrition can influence disease activity, particularly in pediatric patients with IBD.14 Diet is thought to influence disease activity, at least in part, by modifying the intestinal microbiome.15 Recent dietary recommendations from the International Organization for the Study of Inflammatory Bowel Diseases encouraged an increased intake of fruits and vegetables, while limiting intake of red or processed meats, trans and saturated fats, artificial sweeteners, and emulsifiers.16 Specifically, low-fiber diets have been associated with a greater risk of flares in patients with CD, presumably through lack of fiber supporting anti-inflammatory bacterial communities in the colon.17 Unfortunately, patients with IBD report problems with food security and likely access to nutritionally rich foods. Nguyen et al found that up to 14% of patients with IBD in the US may be classified as food insecure.11 They also showed that food insecurity was associated with increased financial hardship. We found food insecurity was infrequently reported and had limited impact on the performance of our models. Study of the complex interaction between diet, disease activity, and food security warrants larger patient populations to clarify potential links with adverse outcomes.
Patients with IBD and limited financial resources may have an increased risk of adverse outcomes. Early studies used proxy measures for financial resources, such as insurance type and employment status; however, more recent studies have used alternative and more direct measures. A recent study by Bernstein et al showed that IBD patients of lower SES had higher rates of outpatient physician visits, hospitalizations, intensive care unit admissions, steroid use, and death.12 Similar findings were noted when using Medicaid insurance as a proxy for SES. Patients with IBD and Medicaid were noted to have higher rates of hospital admission and emergency room visits compared to patients with IBD and other forms of insurance.18 Though not clearly linking adverse outcomes with financial hardship, Nguyen et al showed a link between food insecurity, financial hardship, and medical adherence.11 Overall, patients in our study reported a low concern for limited financial resources. Consistent with this, financial resource concerns were not a predictive variable in the model for the combined cohort but were pertinent when examining just patients with CD. These results suggest that limited financial resources may play a role in IBD-related outcomes, as noted by prior studies, but additional unmeasured confounders likely influence the occurrence of these outcomes.
Transportation of patients to appointments is a critical component of IBD disease management. Transportation difficulty is a systemic issue, with Wolfe et al reporting that 5.8 million persons in the United States delayed medical care because they did not have transportation in 2017.19 The form of transportation may also significantly impact the ability to attend appointments. Silver et al demonstrated among a population with low SES that transportation difficulties were present in 18.2% of patients who had reliable access to a car compared to 40% of patients who relied on public transportation.20 The frequency of access to care is also adversely affected by transportation. Arcury et al showed that patients with a driver’s license had 2.29 times more health care visits for chronic care and 1.92 times more visits for regular checkup care than those without a license. Respondents who had family or friends who could provide transportation had 1.58 times more visits for chronic care than those who did not.21 This is of particular importance in management of chronic health conditions such as IBD with high healthcare utilization. Our study showed a limited number of patients reporting difficulties with transportation related to ADLs or medical visits, and consequently transportation had a limited impact on the final models predicting IBD-related hospitalizations and surgeries. However, given the known importance of transportation to health outcomes, this should remain an area of importance in future studies, particularly examining forms of transportation and access.
The impact of both major depressive and generalized anxiety disorder has been studied in IBD patients extensively. Systematic reviews have shown that the pooled mean prevalence of anxiety and depression in patients with IBD is 19.1% and 21.2%, respectively, which is significantly higher than healthy controls.22 Several groups have linked the presence of anxiety or depression with an increased risk of flares. The largest study comes from an analysis of the Swiss IBD cohort, which found significant associations between depression and risk of recurrence in both patients with UC and CD, and an association between risk of recurrence and anxiety in patients with CD.23 Similar results were found in smaller single-center studies using separate populations demonstrating the robustness of this association.24–26 One proposed mechanism driving this association is adherence to medications. Shale and Riley found that depression was an independent predictor of complete noncompliance to treatment with mesalamine in a population of patients with UC.27 Though it was not one of the top five explanatory variables in our models, PHQ-2 scores were within the top 10 explanatory variables for the combined cohort as well as the subgroups of patients with CD and UC. This supports prior studies demonstrating an association between presence of depression and increased risk of flares in patients with IBD.
Our study has several strengths, including the ability to review patient-level data, such as disease phenotype, but we acknowledge limitations. First, the SDOH information was self-reported or obtained during routine clinic visits by staff, which raises the possibility of inaccurate reporting. Additionally, we attempted to account for several potential confounding factors, such as disease extent and behavior, but could not accurately account for other factors such as severity of mucosal inflammation, disease activity, and medications prior to admission. Also, patients may have experienced our outcome of interest in a facility outside the BSW hospital system, which we were not able to correct for. Finally, given the design of our study, we cannot establish a clear causal relationship between SDOH factors and IBD-related hospitalizations or surgeries.
We found that addition of SDOH information to clinical predictors of IBD-related hospitalizations and surgeries incrementally improved prediction model performance. These findings suggest that interventions designed to modify these factors may reduce adverse outcomes for patients with IBD and underscore the role of social determinants in influencing health outcomes. Further studies are needed to confirm these findings and begin exploring interventional studies designed to improve SDOH and the resulting impact on IBD-related adverse events.
Supplementary Material
Disclosure statement/Funding
The authors report no funding or conflicts of interest. The data that support the findings of this study are available from the corresponding author, RS, upon reasonable request.
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