Social Determinants of Health Influence Early Outcomes Following Lumbar Spine Surgery

How do we treat our male and female patients? - A primer on gender-based health care inequities

Health is a fundamental human right, yet disparities in healthcare, based on gender, persist for women. These inequities stem from a patriarchal society that has regarded men as the default standard, leading to women being treated merely as smaller men. Contributing to these disparities are the gender stereotypes that pervade our society. Women possess differences in anatomy, physiology, psychology and social experience than men. To achieve health equity, it is vital to understand and be open to consider and evaluate these aspects in each individual patient. This requires an understanding of our own biases and a commitment to valuing diversity in both patient and caregiver. Improving equity and diversity throughout all aspects of the medical system will be necessary to provide optimal patient care for all.

Impact of Racial and Socioeconomic Disparities on Access to Interspinous Spacer for Treatment of Lumbar Spinal Stenosis: A Nationwide Medicare Analysis

BACKGROUND

In mild to moderate lumbar spinal stenosis (LSS) where conservative care treatments fail, minimally invasive treatments, such as interspinous spacers without decompression or fusion (ISD), may be appropriate. While previous studies have demonstrated racial and socioeconomic disparities in the surgical treatment of LSS, there are limited data on how those factors impact accessibility to these procedures. This study explored demographic, socioeconomic, and geographic differences in the use of ISD.

METHODS

Using the Medicare 100% files from 2017 through 2022, this retrospective claims analysis identified when and if patients diagnosed with LSS received ISD implantation. Cox proportional hazards regression was used to examine the association between racial and socioeconomic characteristics and the rate of ISD implantation, stratified by geographic region.

RESULTS

A total of 1,316,622 individuals met the inclusion criteria; 4730 (0.4%) underwent ISD implantation, with a mean (standard deviation) time to treatment of 11.9 (13.2) months after diagnosis. The likelihood of ISD implantation was higher for older patients (except for the oldest group), males, those with lower disease burden, and White patients. Cox regression revealed that the associations of racial and socioeconomic factors with ISD implantation varied by U.S. region. In the Midwest and Northeast, lower median household income was associated with a decreased likelihood of ISD implantation regardless of race, while in the South, Black patients were less likely to undergo ISD implantation regardless of income.

CONCLUSIONS

The observed disparities in access to ISD implantation mirror existing trends in surgical interventions for LSS, suggesting further study and interventions are needed to address inequities.

Social determinants of health and disparities in spine surgery: a 10-year analysis of 8,565 cases using ensemble machine learning and multilayer perceptron

BACKGROUND CONTEXT

The influence of SDOH on spine surgery is poorly understood. Historically, researchers commonly focused on the isolated influences of race, insurance status, or income on healthcare outcomes. However, analysis of SDOH is becoming increasingly more nuanced as viewing social factors in aggregate rather than individually may offer more precise estimates of the impact of SDOH on healthcare delivery.

PURPOSE

The aim of this study was to evaluate the effects of patient social history on length of stay (LOS) and readmission within 90 days following spine surgery using ensemble machine learning and multilayer perceptron.

STUDY DESIGN

Retrospective chart review.

PATIENT SAMPLE

8,565 elective and emergency spine surgery cases performed from 2013 to 2023 using our institution's database of longitudinally collected electronic medical record information.

OUTCOMES MEASURES

Patient LOS, discharge disposition, and rate of 90-day readmission.

METHODS

Ensemble machine learning and multilayer perceptron were employed to predict LOS and readmission within 90 days following spine surgery. All other subsequent statistical analysis was performed using SPSS version 28. To further assess correlations among variables, Pearson's correlation tests and multivariate linear regression models were constructed. Independent sample t-tests, paired sample t-tests, one-way analysis of variance (ANOVA) with post-hoc Bonferroni and Tukey corrections, and Pearson's chi-squared test were applied where appropriate for analysis of continuous and categorical variables.

RESULTS

Black patients demonstrated a greater LOS compared to white patients, but race and ethnicity were not significantly associated with 90-day readmission rates. Insured patients had a shorter LOS and lower readmission rates compared to non-insured patients, as did privately insured patients compared to publicly insured patients. Patients discharged home had lower LOS and lower readmission rates, compared to patients discharged to other facilities. Marriage decreased both LOS and readmission rates, underweight patients showcased increased LOS and readmission rates, and religion was shown to impact LOS and readmission rates. When utilizing patient social history, lab values, and medical history, machine learning determined the top 5 most-important variables for prediction of LOS -along with their respective feature importances-to be insurance status (0.166), religion (0.100), ICU status (0.093), antibiotic use (0.061), and case status: elective or urgent (0.055). The top 5 most-important variables for prediction of 90-day readmission-along with their respective feature importances-were insurance status (0.177), religion (0.123), discharge location (0.096), emergency case status (0.064), and history of diabetes (0.041).

CONCLUSIONS

This study highlights that SDOH is influential in determining patient length of stay, discharge disposition, and likelihood of readmission following spine surgery. Machine learning was utilized to accurately predict LOS and 90-day readmission with patient medical history, lab values, and social history, as well as social history alone.

Social vulnerability and surgery outcomes: a cross-sectional analysis

BACKGROUND

Post-operative complications present a challenge to the healthcare system due to the high unpredictability of their incidence. Socioeconomic conditions have been established as social determinants of health. However, their contribution relating to postoperative complications is still unclear as it can be heterogeneous based on community, type of surgical services, and sex and gender. Uncovering these relations can enable improved public health policy to reduce such complications.

METHODS

In this study, we conducted a large population cross-sectional analysis of social vulnerability and the odds of various post-surgical complications. We collected electronic health records data from over 50,000 surgeries that happened between 2012 and 2018 at a quaternary health center in St. Louis, Missouri, United States and the corresponding zip code of the patients. We built statistical logistic regression models of postsurgical complications with the social vulnerability index of the tract consisting of the zip codes of the patient as the independent variable along with sex and race interaction.

RESULTS

Our sample from the St. Louis area exhibited high variance in social vulnerability with notable rapid increase in vulnerability from the south west to the north of the Mississippi river indicating high levels of inequality. Our sample had more females than males, and females had slightly higher social vulnerability index. Postoperative complication incidence ranged from 0.75% to 41% with lower incidence rate among females. We found that social vulnerability was associated with abnormal heart rhythm with socioeconomic status and housing status being the main association factors. We also found associations of the interaction of social vulnerability and female sex with an increase in odds of heart attack and surgical wound infection. Those associations disappeared when controlling for general health and comorbidities.

CONCLUSIONS

Our results indicate that social vulnerability measures such as socioeconomic status and housing conditions could affect postsurgical outcomes through preoperative health. This suggests that the domains of preventive medicine and public health should place social vulnerability as a priority to achieve better health outcomes of surgical interventions.

Development and Validation of Machine Learning-Based Predictive Model for Prolonged Hospital Stay after Decompression Surgery for Lumbar Spinal Canal Stenosis

Introduction

Precise prediction of hospital stay duration is essential for maximizing resource utilization during surgery. Existing lumbar spinal stenosis (LSS) surgery prediction models lack accuracy and generalizability. Machine learning can improve accuracy by considering preoperative factors. This study aimed to develop and validate a machine learning-based model for estimating hospital stay duration following decompression surgery for LSS.

Methods

Data from 848 patients who underwent decompression surgery for LSS at three hospitals were examined. Twelve prediction models, using 79 preoperative variables, were developed for postoperative hospital stay estimation. The top five models were chosen. Fourteen models predicted prolonged hospital stay (≥14 days), and the most accurate model was chosen. Models were validated using a randomly divided training sample (70%) and testing cohort (30%).

Results

The top five models showed moderate linear correlations (0.576-0.624) between predicted and measured values in the testing sample. The ensemble of these models had moderate prediction accuracy for final length of stay (linear correlation 0.626, absolute mean error 2.26 days, standard deviation 3.45 days). The c5.0 decision tree model was the top predictor for prolonged hospital stay, with accuracies of 89.63% (training) and 87.2% (testing). Key predictors for longer stay included JOABPEQ social life domain, facility, history of vertebral fracture, diagnosis, and Visual Analogue Scale (VAS) of low back pain.

Conclusions

A machine learning-based model was developed to predict postoperative hospital stay after LSS decompression surgery, using data from multiple hospital settings. Numerical prediction of length of stay was not very accurate, although favorable prediction of prolonged stay was accomplished using preoperative factors. The JOABPEQ social life domain score was the most important predictor.

Social vulnerability adversely affects emergency-department utilization but not patient-reported outcomes after total joint arthroplasty

INTRODUCTION

Multiple studies demonstrate social deprivation is associated with inferior outcomes after total hip (THA) and total knee (TKA) arthroplasty; its effect on patient-reported outcomes is debated. The primary objective of this study evaluated the relationship between social vulnerability and the PROMIS-PF measure in patients undergoing THA and TKA. A secondary aim compared social vulnerability between patients who required increased resource utilization or experienced complications and those who didn't.

MATERIALS AND METHODS

A retrospective review of 537 patients from March 2020 to February 2022 was performed. The Centers for Disease Control Social Vulnerability Index (SVI) were used to quantify socioeconomic disadvantage. The cohort was split into THA and TKA populations; univariate and multivariate analyses were performed to evaluate primary and secondary outcomes. Statistical significance was assessed at p < 0.05.

RESULTS

48.6% of patients achieved PROMIS-PF MCID at 1-year postoperatively. Higher levels of overall social vulnerability (0.40 vs. 0.28, p = 0.03) were observed in TKA patients returning to the ED within 90-days of discharge. Increased overall SVI (OR = 9.18, p = 0.027) and household characteristics SVI (OR = 9.57, p = 0.015) were independent risk factors for 90-day ED returns after TKA. In THA patients, increased vulnerability in the household type and transportation dimension was observed in patients requiring 90-day ED returns (0.51 vs. 0.37, p = 0.04).

CONCLUSION

Despite an increased risk for 90-day ED returns, patients with increased social vulnerability still obtain good 1-year functional outcomes. Initiatives seeking to mitigate the effect of social deprivation on TJA outcomes should aim to provide safe alternatives to ED care during early recovery.

The impact of social determinants of health on early outcomes after adult Chiari surgery

We sought to identify social determinants of health (SDoH) for adult patients undergoing Chiari decompression surgery and to analyze their association with postoperative outcomes, including length of stay (LOS), return to the system within 30 days, and the Chicago Chiari Outcomes Score (CCOS). This is a retrospective study of adult patients who underwent Chiari decompression surgery between June 2021 and January 2023. Data was gathered through electronic medical record review and telephone surveys. Descriptive statistics were used to evaluate demographics of all patients meeting inclusion criteria. Fisher's exact tests and logistic regression were used for data analysis. A total of 37 patients underwent Chiari decompression (23 CCOS/SDoH survey respondents): 48% bony decompression only, 30% bony decompression plus intradural exploration, and 22% occipitocervical fusion. Seven patients (30%) had a LOS > 2 days, 1 patient (4%) required inpatient rehabilitation postoperatively, 4 patients (17%) returned to the system within 30 days, 10 patients (43%) had an extremely favorable CCOS (15-16), and 11 patients (48%) reported interaction with a Chiari support group. Mean follow-up was 9.5 months. Patients with occipitocervical fusion were more likely to have a LOS > 2 days (p = 0.03), patients who exercised ≥ 3 days per week were more likely to have a favorable CCOS (p = 0.04), and patients who participated in a Chiari support group were less likely to have a favorable CCOS (p = 0.03). Chiari decompression plus occipitocervical fusion may be associated with increased LOS. While more frequent exercise may be associated with better post-surgical outcomes, participation in a Chiari support group may be correlated with worse outcomes.

Enhanced recovery after elective spinal surgery: an Australian pilot study

Background

The principles of enhanced recovery after surgery (ERAS) aim to reduce the physiological stress of surgery which in turn improve clinical and health economic outcomes. There is ample evidence in literature supporting ERAS methodologies in other surgical specialties, but its adoption in spinal surgery, especially in Australia remains in infancy. The aim of this project is to describe the early experience with an evidence-based ERAS pathway for simple spine surgery, a first of its kind in Australia.

Methods

An ERAS protocol was designed using an evidenced-based review of the literature. The authors then conducted a prospective cohort analysis looking at outcome of patients undergoing elective spinal (lumbar and cervical) decompression surgery under ERAS principles by a single surgeon on the Westmead Hospital Campus between March 2021 to May 2023. Primary outcomes were patient length of stay (LOS), patient reported pain and disability scores and complications (including readmissions within 30 days and re-operation within 6 months). Secondary outcomes included predictors of failure for same-day discharge.

Results

A total of 52 patients underwent spinal decompression surgeries under the ERAS protocol. Overall 43 out of 52 patients (83.7%) were successfully discharged on the same day as their surgery. Patient reported outcomes were improved at 6 weeks and 6 months confirming durability of intervention. The rates of complications were similar to literature reported rates for simple lumbar or cervical decompression procedures and there were no readmissions within 30 days or re-operations within 6 months of surgery. Being of non-English speaking background [odds ratio (OR) =6.08, P=0.04] and from home alone (OR =10.25, P=0.03) were predictors of failure of same day discharge in this small cohort.

Conclusions

Implementation of ERAS protocols for simple spinal decompression surgeries is feasible and produces durable improved patient outcomes while reducing LOS in hospitals. Patient social factors can be predictive of lack of compliance.

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Background

Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality.

Methods

We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.

Results

We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes.

Discussion

Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

A retrospective analysis of the social determinants of health affecting stroke outcomes in a small hospital situated in a health professional shortage area (HPSA)

Where someone lives is a major determinant of population health. In the United States, people who live in Health Professional Shortage Areas are considered medically underserved and have a higher propensity for conditions such as stroke, hypertension, and diabetes. Our goal was to better understand the diverse needs of patients presenting to the Crouse Hospital emergency department with stroke symptoms. Crouse Hospital is a small community hospital located in a shortage area serving both urban and rural populations in and around Syracuse, New York. Despite its small size, Crouse Hospital quickly became a major comprehensive stroke center in Central New York. With this study we assessed the social factors affecting the stroke patient population in the community and compared these characteristics between those living in served and underserved areas. Informed by the social determinants of health framework, we analyzed 1731 incidents of stroke that occurred between January 2019 and January 2021, and observed that the circumstances associated with stroke varied by service category and race, with White patients and those from served areas having better stroke outcomes compared to those residing in underserved areas and those that were not White. Our analyses help us to understand the underlying factors influencing the observed disparities and allow us to move forward by implementing informed community-based interventions to decrease stroke incidence and improve post-stroke care. Using our example other small hospitals can enact similar strategies to address the social determinants affecting their patients to improve stroke outcomes in their region.

Are We Considering the Whole Patient? The Impact of Physical and Mental Health on the Outcomes of Spine Care

STUDY DESIGN

Retrospective, observational.

OBJECTIVE

To evaluate the influence of baseline health status on the physical and mental health (MH) outcomes of spine patients.

SUMMARY OF BACKGROUND DATA

Spine conditions can have a significant burden on both the physical and MH of patients. To date, few studies have evaluated the outcomes of both dimensions of health, particularly in nonoperative populations.

MATERIALS AND METHODS

At their first visit to a multidisciplinary spine clinic, 2668 nonoperative patients completed the Patient-reported Outcomes Measurement Information System-Global Health (PROMIS-GH) instrument and a questionnaire evaluating symptoms and goals of care. Patients were stratified by their baseline percentile score of the MH and physical health (PH) components of the PROMIS-GH. Four groups of patients were compared based on the presence or absence of bottom quartile PH or MH scores. The primary end point was the achievement of a minimal clinically important difference (MCID) on the MH or PH components at follow-up. Multivariate regression assessed the predictors of MCID achievement.

RESULTS

After controlling for demographics, symptoms, and goals, each 1-point increase in baseline PROMIS-GH mental score reduced the odds of achieving MH MCID by 9.0% ( P <0.001). Conversely, each 1-point increase in baseline GH-physical score increased the odds of achieving MCID by 4.5% ( P =0.005). Each 1-point increase in baseline GH-physical score reduced the odds of achieving PH MCID by 12.5% ( P <0.001), whereas each 1-point increase in baseline GH-mental score increased the odds of achieving MCID by 5.0% ( P <0.001).

CONCLUSIONS

Spine patients presenting with the lowest levels of physical or MH were most likely to experience clinically significant improvement in those domains. However, lower levels of physical or mental health made it less likely that patients would experience significant improvement in the alternative domain. Physicians should evaluate and address the complex spine population holistically to maximize improvement in both physical and mental health status.