Social determinants of health and their impact on postcolectomy surgery readmissions: a multistate analysis, 2009–2014

Patient Factors Associated with Missed Otolaryngology Appointments at an Urban Safety-Net Hospital

OBJECTIVE

To determine if patient factors related to ethnicity, socioeconomic status (SES), medical comorbidities, or appointment characteristics increase the risk of missing an initial adult otolaryngology appointment.

METHODS

This study is a retrospective case control study at Boston Medical Center (BMC) in Boston, Massachusetts, that took place in 2019. Patient demographic and medical comorbidity data as well as appointment characteristic data were collected and compared between those that attended their initial otolaryngology appointment versus those who missed their initial appointment. Chi-square and ANOVA tests were used to calculate differences between attendance outcomes. Multivariate analysis was used to compare the odds of missing an appointment based on various patient- and appointment-related factors.

RESULTS

Patients who were more likely to miss their appointments were more often female, of lower education, disabled, not employed, Black or Hispanic, and Spanish-speaking. Spring and Fall appointments were more likely to be missed. When a multivariate regression was conducted to control for social determinants of health (SDOH) such as race, insurance status, employment, and education status, the odds of females, Spanish-speaking, students, and disabled patients missing their appointment were no longer statistically significant.

CONCLUSION

A majority of patients at BMC come from lower SES backgrounds and have multiple medical comorbidities. Those who reside closer to BMC, often areas of lower average income, had higher rates of missed appointments. Interventions such as decreasing lag time, providing handicap-accessible free transportation, and increasing accessibility of telemedicine for patients could help improve attendance rates at BMC.

LEVEL OF EVIDENCE

IV Laryngoscope, 2024.

County health ranking: untangling social determinants of health and other factors associated with short-term bariatric surgery outcomes

BACKGROUND

The complex interplay of the social determinants of health, race/ethnicity, and traditional surgical risk factors on outcomes following metabolic surgery is poorly understood.

OBJECTIVE

To evaluate the relationship between the social determinants of health as measured by county health ranking (CHR) and short-term metabolic surgery outcomes.

SETTING

Five accredited bariatric program sites at a national academic health system.

METHODS

Data were collected from 5 sites of a single health system from 2010 to 2021. Current procedural terminology codes identified primary and revisional cases. Patient characteristics, procedural data, and 30-day occurrences were collected. CHRs for health factors were determined by ZIP Code and stratified into best, middle, and worst terciles. The primary outcome was 30-day complications, readmissions, or reinterventions/reoperations. Logistic regression assessed the correlation between CHR tercile and morbidity.

RESULTS

We analyzed 4,315 primary and 370 revisional metabolic surgery cases. Overall, 64.0%, 27.4%, and 8.6% of patients lived in the best, middle, and worst CHR terciles, respectively. Patients in the middle and worst CHR terciles were more commonly older; non-Hispanic Black or Hispanic; suffered from preexisting chronic obstructive pulmonary disease or hypertension, were dialysis dependence, were on therapeutic anticoagulation, or had inferior vena cava filters. Middle and worst CHR tercile patients were more likely to undergo index sleeve gastrectomy or robotic-assisted surgery and have surgery performed by a self-designated general surgeon. Thirty-day outcomes were similar across CHR terciles. Racial disparity in multiple short-term outcomes persisted despite adjustment for CHR tercile.

CONCLUSION

Higher-risk patients are more likely to be from counties with lower CHRs, but CHR was not independently associated with 30-day outcomes after metabolic surgery.

Food Insecurity in the Elective Enhanced Recovery After Surgery Colorectal Surgical Population: Prevalence and Implications for Surgical Outcomes

BACKGROUND

Food insecurity is defined as having limited or uncertain availability of nutritionally adequate food. Approximately 10.5% of U.S. households are food-insecure. Our study aimed to determine the prevalence and postoperative implications of food insecurity in a diverse group of colorectal surgery patients admitted to a hospital in an area with a higher-than-average median income.

METHODS

The 6-question Household Food Security Survey was added to the colorectal surgery ERAS program preoperative paperwork. Patient demographics, comorbidities, operative parameters, length of stay, and postoperative outcomes were collected by review of electronic medical records.

RESULTS

A total of 294 ERAS patients (88.8%) completed the survey over an 11-month period. Thirty-three patients (11.2%) were identified as food-insecure. Food-insecure patients were more likely to be non-white (P = .003), younger (P = .009), smokers (P = .004), chronic narcotic users (P < .001), unmarried (P = .007), and have more comorbidities (P = .004). The food-insecure population had more frequent postoperative ileus (P = .044). Hospital length of stay was significantly longer in food-insecure patients (8.6 days vs 5.4 days, P < .001). Food-insecure patients also had higher rates of >30-day mortality (P = .049).

DISCUSSION

Food insecurity was found to occur in patients that lived in communities deemed both affluent and distressed. These patients had longer hospital stays and higher mortality. A food insecurity questionnaire can easily identify patients at risk. Further investigations to mitigate these complications are warranted.

Social Determinants of Health and Preoperative Care

Preoperative care exists as part of perioperative continuum during which anesthesiologists and surgeons optimize patients for surgery. These multispecialty efforts are important, particularly for patients with complex medical histories and those requiring major surgery. Preoperative care improves planning and determines the clinical pathway and discharge disposition. The role of nonmedical social factors in the preoperative planning is not well described in anesthesiology. Research to improve outcomes based on social factors is not well described for anesthesiologists but could be instrumental in decreasing disparities and advancing health equity in surgical patients.

A socioecological qualitative analysis of barriers to care in colorectal surgery

BACKGROUND

Although specific social determinants of health have been associated with disparities in surgical outcomes, there exists a gap in knowledge regarding the mechanisms of these associations. Gaining perspectives from multiple socioecological levels can help elucidate these mechanisms. Our study aims to identify social determinants of health that act as barriers or facilitators to surgical care among colorectal surgery stakeholders.

METHODS

We recruited participants representing 5 socioecological levels: patients (individual); caregivers/surgeons (interpersonal); and leaders in hospitals (organizational), communities (community), and government (policy). Patients participated in focus groups, and the remaining participants underwent individual interviews. Semistructured interview guides were used to explore barriers and facilitators to surgical care at each socioecological level. Transcripts were analyzed by 3 coders in an inductive thematic approach with content analyses. The intercoder agreement was 93%.

RESULTS

Six patient focus groups (total n = 18) and 12 key stakeholder interviews were conducted. The mean age of patients was 54.7 years, 66% were Black, and 61% were female. The most common diseases were colorectal cancer (28%), inflammatory bowel disease (28%), and diverticulitis (22%). Key social determinants of health impacting surgical care emerged at each level: individual (clear communication, mental stress), interpersonal (provider communication and trust, COVID-related visitation restrictions), organizational (multiple forms of contact, quality educational materials, scheduling systems, discrimination), community (community and family support and transportation), and policy (charity care, patient advocacy organizations, insurance coverage).

CONCLUSION

Key social determinants of health-impacting care among colorectal surgery patients emerged at each socioecological level and may provide targets for interventions to reduce surgical disparities.

The Association Between Social Determinants of Health and Population Health Outcomes: Ecological Analysis

BACKGROUND

With the increased availability of data, a growing number of studies have been conducted to address the impact of social determinants of health (SDOH) factors on population health outcomes. However, such an impact is either examined at the county level or the state level in the United States. The results of analysis at lower administrative levels would be useful for local policy makers to make informed health policy decisions.

OBJECTIVE

This study aimed to investigate the ecological association between SDOH factors and population health outcomes at the census tract level and the city level. The findings of this study can be applied to support local policy makers in efforts to improve population health, enhance the quality of care, and reduce health inequity.

METHODS

This ecological analysis was conducted based on 29,126 census tracts in 499 cities across all 50 states in the United States. These cities were grouped into 5 categories based on their population density and political affiliation. Feature selection was applied to reduce the number of SDOH variables from 148 to 9. A linear mixed-effects model was then applied to account for the fixed effect and random effects of SDOH variables at both the census tract level and the city level.

RESULTS

The finding reveals that all 9 selected SDOH variables had a statistically significant impact on population health outcomes for ≥2 city groups classified by population density and political affiliation; however, the magnitude of the impact varied among the different groups. The results also show that 4 SDOH risk factors, namely, asthma, kidney disease, smoking, and food stamps, significantly affect population health outcomes in all groups (P<.01 or P<.001). The group differences in health outcomes for the 4 factors were further assessed using a predictive margin analysis.

CONCLUSIONS

The analysis reveals that population density and political affiliation are effective delineations for separating how the SDOH affects health outcomes. In addition, different SDOH risk factors have varied effects on health outcomes among different city groups but similar effects within city groups. Our study has 2 policy implications. First, cities in different groups should prioritize different resources for SDOH risk mitigation to maximize health outcomes. Second, cities in the same group can share knowledge and enable more effective SDOH-enabled policy transfers for population health.

Social determinants of health and the prediction of 90-day mortality among brain tumor patients

OBJECTIVE

Within the neurosurgical oncology literature, the effect of structural and socioeconomic factors on postoperative outcomes remains unclear. In this study, the authors quantified the effects of social determinant of health (SDOH) disparities on hospital complications, length of stay (LOS), nonroutine discharge, 90-day readmission, and 90-day mortality following brain tumor surgery.

METHODS

The authors retrospectively reviewed the records of brain tumor patients who had undergone resection at a single institution in 2017-2019. The prevalence of SDOH disparities among patients was tracked using International Classification of Diseases Ninth and Tenth Revisions (ICD-9 and ICD-10) codes. Bivariate (Mann-Whitney U-test and Fisher's exact test) and multivariate (logistic and linear) regressions revealed whether there was an independent relationship between SDOH status and postoperative outcomes.

RESULTS

The patient cohort included 2519 patients (mean age 55.27 ± 15.14 years), 187 (7.4%) of whom experienced at least one SDOH disparity. Patients who experienced an SDOH disparity were significantly more likely to be female (OR 1.36, p = 0.048), Black (OR 1.91, p < 0.001), and unmarried (OR 1.55, p = 0.0049). Patients who experienced SDOH disparities also had significantly higher 5-item modified frailty index (mFI-5) scores (p < 0.001) and American Society of Anesthesiologists (ASA) classes (p = 0.0012). Experiencing an SDOH disparity was associated with a significantly longer hospital LOS (p = 0.0036), greater odds of a nonroutine discharge (OR 1.64, p = 0.0092), and greater odds of 90-day mortality (OR 2.82, p = 0.0016) in the bivariate analysis. When controlling for patient demographics, tumor diagnosis, mFI-5 score, ASA class, surgery number, and SDOH status, SDOHs independently predicted hospital LOS (coefficient = 1.22, p = 0.016) and increased odds of 90-day mortality (OR 2.12, p = 0.028).

CONCLUSIONS

SDOH disparities independently predicted a prolonged hospital LOS and 90-day mortality in brain tumor patients. Working to address these disparities offers a new avenue through which to reduce patient morbidity and mortality following brain tumor surgery.

Social Vulnerability Subtheme Analysis Improves Perioperative Risk Stratification in Hepatopancreatic Surgery

BACKGROUND

There has been increased interest in understanding how social determinants of health (SDH) may affect care both in the medical and surgical setting. We sought to define the impact of various aspects of social vulnerability on the ability of patients to achieve a "textbook outcome" (TO) following hepatopancreatic surgery.

METHODS

Medicare beneficiaries who underwent hepatopancreatic resection between 2013 and 2017 were identified using the Medicare database. Social vulnerability was defined using the Centers for Disease Control Social Vulnerability Index (SVI), which is comprised of four subthemes: socioeconomic (SE), household composition and disability (HCD), minority status and language (MSL), and housing type and transportation (HTT). TO was defined as the composite endpoint: absence of 90-day mortality or readmission, absence of an extended length of stay (LOS), and no complications during the index admission. Cluster analysis was used to identify vulnerability cohorts, and multivariable logistic regression was utilized to assess the impact of these SVI subthemes on the likelihood to achieve a textbook outcome.

RESULTS

Among 37,707 Medicare beneficiaries, 64.9% (n = 24,462) of patients underwent pancreatic resection while 35.1% (n = 13,245) underwent hepatic resection. Median patient age was 72 years (IQR: 68-77), just over one-half were male (51.9%; n = 19,558), and the median CCI was 3 (IQR: 2-8). Cluster analysis revealed five distinct SVI profiles with wide variability in the distribution of SVI subthemes, ranging from 15 (profile 1 IQR: 7-26) to 83 (profile 5 IQR: 66-93). The five profiles were grouped into 3 categories based on median composite SVI: "low vulnerability" (profile 1), "average vulnerability" (profiles 2 and 3), or "high vulnerability" (profiles 4 and 5). The rate of TO ranged from 44.6% in profile 5 (n = 4022) to 49.2% in profile 1 (n = 4836). Multivariable analyses comparing patients categorized into the two average SVI profiles revealed that despite having similar composite SVI scores, the risk of adverse postoperative outcomes was not similar. Specifically, patients from profile 5 had lower odds of achieving a TO (OR 0.89, 95%CI: 0.83-0.95) and higher odds of 90-day mortality (OR 1.29, 95%CI: 1.15-1.44) versus patients in profile 4.

CONCLUSION

Distinct profiles of SVI subtheme characteristics were independently associated with postoperative outcomes among Medicare beneficiaries undergoing HP surgery, even among patients with similar overall composite SVI scores.

Profiles in social vulnerability: The association of social determinants of health with postoperative surgical outcomes

BACKGROUND

The association of different social vulnerability subthemes (ie, socioeconomic status, household composition and disability, minority status and language, and housing and transportation) with surgical outcomes remains poorly defined. The current study aimed to identify distinct profiles of social vulnerability among Medicare beneficiaries and define the association of these profiles with postoperative outcomes.

METHODS

The Medicare 100% Standard Analytic Files were used to identify patients undergoing lung resection, coronary artery bypass grafting, abdominal aortic aneurysm repair, and colectomy between 2013 and 2017. A cluster analysis was performed based on ranked scores across the 4 subthemes of the Centers for Disease Control and Prevention social vulnerability index. The likelihood of complications, extended length of stay, readmission, and mortality were assessed relative to cluster vulnerability profiles.

RESULTS

Among 852,449 Medicare beneficiaries undergoing surgery, median social vulnerability index among patients in the cohort was 49 (interquartile range: 24-74); cluster analysis revealed 5 vulnerability profiles that had heterogeneity in the vulnerability subthemes, even among patients with similar overall social vulnerability index scores. Postoperative outcomes differed across the 5 vulnerability profiles, with patients in the profiles characterized by higher overall vulnerability having worse postoperative outcomes. In particular, risk of complications (profile 1, 31.9% vs profile 5, 34.0%), extended length of stay (profile 1, 21.7% vs profile 5, 24.3%), 30-day readmission (profile 1, 12.6% vs profile 5, 13.2%), and 30-day mortality (profile 1, 4.0% vs profile 5, 4.7%) was greater among patients with the highest vulnerability (all P < .01). Of note, surgical outcomes varied among patients who resided in communities with similar average social vulnerability index scores (social vulnerability index 49-54). In particular, patients in social vulnerability profile 4 had 26% increased odds of 30-day mortality compared to social vulnerability profile 2 patients (odds ratio 1.26, 95% confidence interval 1.21-1.30). Additionally, profile 3 patients had 15% higher odds of 30-day mortality versus profile 2 patients (odds ratio 1.15, 95% confidence interval 1.10-1.20).

CONCLUSION

Postoperative outcomes differed across patients based on cluster vulnerability profiles. Despite similar overall aggregate social vulnerability index scores, cluster analysis was able to discriminate various social determinants of health subthemes among patients who resided in "average" vulnerability communities that stratified patients relative to risk of adverse postoperative events.

Use of a standardized discharge checklist with daily post-operative C-reactive protein monitoring does not impact readmission rates after colon and rectal surgery

PURPOSE

Elevated CRP has been associated with infectious complications after colorectal surgery but has not been evaluated in a prospective fashion as part of a discharge checklist. The objective of this study was to evaluate the effectiveness of a multi-component "discharge criteria checklist" that included daily use of CRP in decreasing hospital readmission rates after colorectal surgery.

METHODS

This is a prospective before and after study design that included consecutive patients undergoing major colorectal operations at a single university-affiliated community hospital over a 2-year period. The primary outcome was inpatient or emergency department readmission after 30 days. Selected pre- and peri-operative factors associated with readmissions were then examined in a multivariate analysis model.

RESULTS

The study included a total of 1546 patients. Surgical indications were inflammatory bowel disease (15%), colorectal cancer (24%), and benign disease (60%); 9.5% were emergencies. The readmission rates for each group were similar, 17.3% and 17.0%, for the control and discharge checklist groups, respectively (p=0.88). On multivariate analysis of the discharge checklist group dataset, only age, sex, surgical acuity and operating time were statistically significant risk factors. The difference of median CRP values on the day of discharge of those readmitted compared to those not readmitted (35 vs 32 mg/L) was not statistically significant (p=0.28).

CONCLUSIONS

The institution of a "discharge checklist" did not impact post-operative hospital readmissions. Not only were readmissions unchanged by the use of a CRP threshold at discharge, but CRP levels at the time of discharge were not associated with readmissions.

Adding a Social Risk Adjustment Into the Estimation of Efficiency: The Case of Chilean Hospitals

BACKGROUND AND OBJECTIVES

There is much interest in adding social variables to hospital performance assessments. Many of the existing analyses, however, already include patients' diagnosis data, and it is not clear that adding a social adjustment variable would improve the quality of the results: the growing literature on this issue provides mixed results. The purpose in this study was to add evidence from a developing country into this discussion.

METHODS

We estimate the efficiency of hospitals controlling for casemix, with and without adjusting the hospital's casemix for the patients' sociodemographic variables. The magnitude of the adjustment is based on the observed impact of age, sex, and income on length of stay, conditional on the diagnosis related group (DRG). We use a data envelopment analysis (DEA) to assess the efficiency of 50 Chilean hospitals' discharges, including 780 DRGs and covering about 60% of total discharges in Chile from 2013 to 2015.

RESULTS

We found that the sociodemographic adjustment introduces very small changes in the DEA estimation of efficiency. The underlying reason is the relatively low influence of sociodemographics on hospital costs, conditional on DRG, and the changing pattern of sociodemographics across DRGs for any given hospital.

CONCLUSION

We conclude that the casemix-adjusted estimation of hospital efficiency is robust to the heterogeneity of patients' sociodemographic heterogeneity across hospitals. These results confirm, in a developing country, what has been observed in developed countries. For management purposes, then, the processing costs of adding social variables into hospitals' performance assessments might not be justified.

Association of Socioeconomic Area Deprivation Index with Hospital Readmissions After Colon and Rectal Surgery

BACKGROUND

Risk adjustment for reimbursement and quality measures omits social risk factors despite adversely affecting health outcomes. Social risk factors are not usually available in electronic health records (EHR) or administrative data. Socioeconomic status can be assessed by using US Census data. Distressed Communities Index (DCI) is based upon zip codes, and the Area Deprivation Index (ADI) provides more granular estimates at the block group level. We examined the association of neighborhood disadvantage using the ADI, DCI, and patient-level insurance status on 30-day readmission risk after colorectal surgery.

METHODS

Our 677 patient cohort was derived from the 2013-2017 National Surgical Quality Improvement Program at a safety net hospital augmented with EHR data to determine insurance status and 30-day readmissions. Patients' home addresses were linked to the ADI and DCI.

RESULTS

Our cohort consisted of 53.9% males and 63.8% Hispanics with a 22.9% 30-day readmission rate from the date of discharge; > 50% lived in highly deprived neighborhoods. Controlling for medical comorbidities and complications, ADI was associated with increased risk of 30 days from the date of discharge readmissions among patients living in medium (OR = 2.15, p = .02) or high (OR = 1.88, p = .03) deprived areas compared to less-deprived neighborhoods, but not insurance status or DCI.

CONCLUSIONS

The ADI identified patients living in deprived communities with increased readmission risk. Our results show that block-group level ADI can potentially be used in risk adjustment, to identify high-risk patients and to design better care pathways that improve health outcomes.

Defining the relative contribution of health care environmental components to patient outcomes in the model of 30-day readmission after coronary artery bypass graft (CABG)

BACKGROUND

Patient outcomes following health care interventions may be dependent on a variety of factors: patient, surgeon, hospital, information technology, and temporal, cultural, and socioeconomic factors, among others. In this study, we characterize the relative contribution of each of these factors using a model of 30-day readmission following coronary artery bypass graft.

METHODS

The Healthcare Cost and Utilization Project, the American Hospital Association Annual Health Survey Databases, the Healthcare Information and Management Systems Society, and the Distressed Communities Index from 2010 to 2013 were linked for Florida, Iowa, Massachusetts, Maryland, New York, and Washington. Logistic regression, random forest, decision tree, gradient boosting, k-nearest-neighbors classification, and XGBoost tree models were implemented. Modeling results were compared on the basis of predictive accuracy, sensitivity, specificity, and area under the curve. Decision tree performed best and was selected for further analysis. A gradient-boosted model was used to quantify factor contribution.

RESULTS

The model had 45,352 patients, 54,096 admissions, and a 16.2% 30-day readmission rate after coronary artery bypass graft. The top 10 predictors were disposition at discharge, number of chronic conditions, total procedures, median household income, adults without high school diplomas, primary payer method, Agency for Healthcare Research and Quality comorbidity: renal failure, patient location (urban-rural), admission type, and age categories. The top 3 socioeconomic predictors were estimated state median household income, adults without high school diplomas, and patient location (urban versus rural designation). The relative contribution of patient/temporal, socioeconomic, hospital information technology, and hospital factors to readmission is 83.45%, 5.71%, 6.34%, and 4.31%, respectively.

CONCLUSION

In this model, the contribution of socioeconomic factors is substantive but lags significantly behind patient/temporal factors. With ever increasing availability of data, identification of contributors to patient outcomes within the overall health care macroenvironment will allow prioritization of interventions.