Patient Insurance Status Research Articles

Risk Factors for Death Among Hospitalized Patients Aged 21–64 Years Diagnosed with COVID-19—New York City, March 13–April 9, 2020

BackgroundCOVID-19 mortality studies have primarily focused on persons aged ≥ 65 years; less is known about decedents aged <65 years.MethodsWe conducted a case-control study among NYC residents aged 21–64 years hospitalized with COVID-19 diagnosed March 13–April 9, 2020, to determine risk factors for death. Case-patients (n=343) were hospitalized decedents with COVID-19 and control-patients (n=686) were discharged from hospitalization with COVID-19 and matched 2:1 to case-patients on age and residential neighborhood. Conditional logistic regression models were adjusted for patient sex, insurance status, and marital status. Matched adjusted odds ratios (aORs) were calculated for selected underlying conditions, combinations of conditions, and race/ethnic group.ResultsMedian age of both case-patients and control-patients was 56 years (range: 23–64 years). Having ≥ 1 selected underlying condition increased odds of death 4.45-fold (95% CI: 2.33–8.49). Patients with diabetes; morbid obesity; heart, kidney, or lung disease; cancer; neurologic/neurodevelopmental conditions; mental health conditions; or HIV had significantly increased odds of death. Compared with having neither condition, having both diabetes and obesity or diabetes and heart disease was associated with approximately threefold odds of death. Five select underlying conditions were more prevalent among non-Hispanic Black control-patients than among control-patients of other races/ethnicities.Conclusions and RelevanceSelected underlying conditions were risk factors for death, and most prevalent among racial/ethnic minorities. Social services; health care resources, including vaccination; and tailored public health messaging are important for COVID-19 prevention. Strengthening these strategies for racial/ethnic minority groups could minimize COVID-19 racial/ethnic disparities.

Area Deprivation Index and Magnitude of Scoliosis at Presentation to a Tertiary Referral Scoliosis Clinic in Massachusetts.

There is growing interest in identifying predictors of large scoliosis curves at initial presentation, but few data to guide such preventive efforts. The association of neighborhood socioeconomic deprivation with curve magnitude in this context has not been previously evaluated. The purpose of our study was to determine the correlation of socioeconomic deprivation with scoliosis curve magnitude at initial presentation. Secondarily, we assessed the correlation of body mass index (BMI) with curve severity. We retrospectively identified 202 patients presenting with adolescent idiopathic scoliosis to a single tertiary care center in Massachusetts from January 2015 to August 2018. The Area Deprivation Index (ADI), a validated composite measure of neighborhood socioeconomic deprivation, was calculated for each patient. Curve magnitude, age, sex, BMI, race, and insurance status were recorded. Pearson correlation was used to determine the association of the ADI and BMI with scoliosis severity. There was no correlation between the ADI and the magnitude of scoliosis at presentation (r=0.055; P=0.43). Greater BMI was moderately correlated with increased scoliosis curve magnitude (r=0.28; P<0.001). There was no association between curve magnitude and patient age, sex, race, or insurance status. The finding that neighborhood socioeconomic deprivation did not correlate with greater scoliosis severity at presentation may be suggestive of equitable access to specialized scoliosis care. Future research should determine whether this reassuring finding is unique to Massachusetts--a state with high rates of health insurance coverage--or generalizable to other US states. In addition, our study further corroborates the notion that greater BMI is associated with larger scoliosis curves, and calls for targeted interventions to facilitate early scoliosis detection in the growing childhood obese population. Level II-prognostic study and retrospective study.

Telemedicine Adoption during the COVID-19 Pandemic: Gaps and Inequalities.

The telemedicine industry has been experiencing fast growth in recent years. The outbreak of coronavirus disease 2019 (COVID-19) further accelerated the deployment and utilization of telemedicine services. An analysis of the socioeconomic characteristics of telemedicine users to understand potential socioeconomic gaps and disparities is critical for improving the adoption of telemedicine services among patients. This study aims to measure the correlation of socioeconomic determinants with the use of telemedicine services in Milwaukee metropolitan area. Electronic health record review of patients using telemedicine services compared with those not using telemedicine services within an academic-community health system: patient demographics (e.g., age, gender, race, and ethnicity), insurance status, and socioeconomic determinants obtained through block-level census data in Milwaukee area. The telemedicine users were compared with all other patients using regression analysis. The telemedicine adoption rates were calculated across regional ZIP codes to analyze the geographic patterns of telemedicine adoption. A total of 104,139 patients used telemedicine services during the study period. Patients who used video visits were younger (median age 48.12), more likely to be White (odds ratio [OR] 1.34; 95% confidence interval [CI], 1.31-1.37), and have private insurance (OR 1.43; CI, 1.41-1.46); patients who used telephone visits were older (median age 57.58), more likely to be Black (OR 1.31; CI 1.28-1.35), and have public insurance (OR 1.30; CI 1.27-1.32). In general, Latino and Asian populations were less likely to use telemedicine; women used more telemedicine services in general than men. In the multiple regression analysis of social determinant factors across 126 ZIP codes, college education (coefficient 1.41, p = 0.01) had a strong correlation to video telemedicine adoption rate. Adoption of telemedicine services was significantly impacted by the social determinant factors of health, such as income, education level, race, and insurance type. The study reveals the potential inequities and disparities in telemedicine adoption.

The Impact of Race, Ethnicity and Insurance Status on Surgery Rates for Benign Prostatic Hyperplasia

ObjectiveTo determine whether patient race/ethnicity are associated with differences in likelihood of undergoing surgical treatment for LUTS/BPH. MethodsQueried hospital network database between 1/2011 and 10/2018. Men over age 40 on medical therapy for LUTS (selective alpha blockade and/or 5-alpha-reductase inhibitor), with 2+ provider visits, and without bladder/prostate malignancy were included. Ethnicity/race determined by self-identification. Insurance status classified as public (Medicare/Medicaid/Tricare), private, self-pay, or other.Multivariable backwards step-wise logistic regression was performed to compare odds of undergoing a surgical procedure by race/ethnicity, controlling for patient age, insurance status, comorbidities, and type of medical therapy. Results30,466 patients included, with White (n=24,443, 80.2%), Hispanic (n=2,715, 8.9%), Black (n=1,245, 4.1%), and other race/ethnicity (2,073, 6.8%) identified within the study population. After adjusting for age, insurance status, major comorbidities, and type of medical therapy, Black patients were less likely to undergo surgery than White patients (OR 0.57, 95% CI 0.37 – 0.88, P = .011), as were patients of other race/ethnicity (OR 0.67, 95% CI 0.49 – 0.92, P = .013). ConclusionsAdjusting for age, insurance status, major comorbidities and type of LUTS medication, men categorized as Black were significantly less likely to undergo surgical treatment for LUTS/BPH than White patients. It is unknown whether this difference results from differences in counseling, access, or other bias in therapy. Efforts to understand and respond to this disparity are necessary. Limitations include lack of IPSS data, additional comorbidity data, limited geographic area, and retrospective nature.

Race and Insurance Status Disparities in Post-discharge Disposition After Hospitalization for Major Trauma.

Investigations detailing predictive measures of hospital disposition after traumatic injury are scarce. We aim to examine the discharge practices among trauma centers in the US and to identify factors that may influence post-hospital disposition. A retrospective analysis of trauma patients using the American College of Surgeons-Trauma Quality Improvement Program dataset from 2007-2017. Primary study outcome was hospital disposition (including long term care facility [LTC], others). Secondary outcomes included: Intensive Care Unit (ICU)-length of stay (LOS), complications, others). 6899538 patients were analyzed. Odds of LTC discharge was significantly higher for Black patients (aOR = 1.30, 95% CI:1.24-1.37), abbreviated injury score (AIS) ≥3 (aOR = 4.22, 95% CI: 4.05-4.39), and higher injury severity score (ISS) (aOR = 9.41, 95% CI:9.03-9.80). Significantly more self-pay patients were discharged home compared to other insurance types (P < .0001). Significantly longer hospital- and ICU-LOS were experienced by those who had an AIS ≥3 (hospital: 4.8days (±7.1) vs. 7.9 (±10.1); ICU: 4.6 (±6.9) vs. 5.9 (±7.9), P < .0001) and had a high ISS (hospital: 4.5days (±5.9) vs. 16.8 (±17.9); ICU: 3.6 (±5.0) vs. 10.2 (±11.5), P < .0001). Patient race, insurance status, and injury severity were predictive of post-hospitalization care discharge. Self-pay and Black patients were less likely to be discharged to secondary care facilities. These findings have the potential to improve in-hospital patient management and predict discharge secondary care needs, and necessitate the need for future research to investigate the extent of inequalities in access to trauma care.

Impact of Insurance Expansion on Disposition for Pediatric Trauma Patients: A National Trauma Data Bank Cohort Study

The insurance status of pediatric trauma patients is associated with access to post-discharge resources, including inpatient rehabilitation. Our goal was to understand the impact of changes in insurance coverage on access to post-acute care resources for pediatric trauma patients. We utilized the National Trauma Data Bank from 2012 to 2016 for all pediatric trauma patients with a highest body region abbreviated injury score >2. Our primary outcome was hospital discharge disposition location. We used multivariable regression to adjust for salient patient and trauma center characteristics. Additionally, we performed a sensitivity analysis including only high-volume hospitals to examine the relationship between the magnitude of facility level expansion in coverage and changes in patient disposition. We identified 195,649 pediatric trauma patients meeting inclusion criteria. From 2012 to 2016 the proportion of patients with Medicaid (35% versus 39%, P < 0.001) and private insurance (39% versus 45%, P < 0.001) increased. Increased discharges with home health (HH) (+0.6% adjusted risk difference 2012 to 2016, P < 0.001) and decreased discharges to inpatient rehabilitation and skilled nursing facilities (-0.6% adjusted risk difference 2012 to 2016, P=0.01) were associated with changes in insurance coverage. After stratifying high volume facilities by magnitude of increase in Medicaid and private insurance, we found that, for all groups, discharge to inpatient rehabilitation and skilled nursing facilities decreased (P < 0.01). Expanded insurance coverage for children was paradoxically associated with decreased discharge to inpatient rehabilitation and skilled nursing facilities after severe traumatic injury. These findings suggest that additional barriers may drive limitations in access to pediatric post-discharge services.

Impact of COVID-19 on Pediatric Primary Care Visits at Four Academic Institutions in the Carolinas

We aimed to determine how COVID-19 affected the number and type of pediatric primary care visits in April 2020, compared to April 2019, and which characteristics were associated with obtaining care in 2020. We performed a retrospective chart review of patients receiving care in April 2019 and April 2020 from four large, academic institutions across two states. The subjects were included if they were aged 0–18 years and were seen in a pediatric clinic in April 2019 or April 2020. We extracted the number of visits, visit type, and visit diagnosis; and the patient characteristics, including age, race/ethnicity, and insurance status. Logistic regression analysis identified characteristics associated with obtaining care in April 2020. We included 120,230 visits. Participants were 50% white and half had Medicaid. In 2020 there were significantly fewer visits for both well and acute visits with 42,670 visits in 2020 compared to 77,560 in 2019; 6616 were telehealth visits in 2020. Visits for chronic conditions were significantly decreased in 2020. Attending a visit in 2020 was more likely if the participant was black or Hispanic, younger, attending an acute visit, or had private insurance. During the COVID-19 pandemic, pediatric primary care decreased substantially for both well visits and follow-up of chronic conditions.

Predictors of early hospice or death in patients with inoperable lung cancer treated with curative intent.

e20525 Background: Treatment for inoperable stage II-III non-small cell lung cancer (NSCLC) involves aggressive chemo-radiotherapy (CRT). While outcomes have improved with immunotherapy, some patients transition to hospice or die early in their treatment course. To help identify these patients, we developed a predictive model for early poor outcomes in NSCLC patients treated with curative intent. Methods: In a statewide consortium involving 27 sites, information was collected prospectively on stage II-III NSCLC patients who received curative CRT from April 2012 to November 2019. We defined an early poor outcome as termination of treatment due to hospice enrollment or death within 5 months of initiating radiation therapy. Potential predictors included clinical characteristics and patient reported outcomes (PROs) from validated questionnaires. Logistic regression models were used to assess potential predictors and build predictive models. Multiple imputation was used to handle missing data. We used Lasso regularized logistic regression to build a predictive model with multiple predictor variables. Results: Of the total of 2267 included patients, 128 patients discontinued treatment early due to hospice enrollment or death. The mean age of the 128 patients was 71 years old (range 48-91) and 59% received concurrent chemotherapy. Significant uni-variable predictors of early hospice or death were advanced age, worse ECOG performance status, high PTV volume, short distance to normal tissue critical structures, high mean heart dose, uninsured status, lower scores on the Functional and Physical Well-Being scale and the Lung Cancer Symptoms sub-scale of the FACT-L quality of life instrument, as well as higher levels of patient-reported lack of energy, cough, and shortness of breath. The best predictive model included age, ECOG performance status, PTV volume, mean heart dose, patient insurance status, and patient-reported lack of energy and cough. The pooled estimate of area under the curve (AUC) for this multivariable model was 0.71, with a negative predictive value of 95%, specificity of 97%, positive predictive value of 23%, and sensitivity of 16% at a predicted risk threshold of 20%. Conclusions: Our models identified a combination of clinical variables and PROs that may help identify individuals with inoperable NSCLC undergoing curative intent chemo-radiotherapy who are at a high risk of early hospice enrollment or death. These preliminary results are encouraging and warrant further evaluation in a larger cohort of patients.

Examining the Impacts of the Built Environment on Quality of Life in Cancer Patients Using Machine Learning

Despite accumulative evidence regarding the impact of the physical environment on health-related outcomes, very little is known about the relationships between built environment characteristics and the quality of life (QoL) of cancer patients. This study aims to investigate the association between the built environment and QoL by using survey data collected from cancer patients within the United States in 2019. To better understand the associations, we controlled the effects from sociodemographic attributes and health-related factors along with the residential built environment, including density, diversity, design, and distance to transit and hospitals on the self-reported QoL in cancer patients after treatment. Furthermore, machine learning models, i.e., logistic regression, decision tree, random forest, and multilayer perceptron neural network, were employed to evaluate the contribution of these features in predicting the QoL. The results from machine learning models indicated that the travel distance to the closest large hospital, perceived accessibility, distance to transit, and population density were among the most significant predictors of the cancer patients’ QoL. Additionally, the health insurance status, age, and education of patients are associated with QoL. The adverse effects of density on the self-reported QoL in this study can be addressed by individuals’ emotions towards negative aspects of density. Given the strong association between QoL and urban sustainability, consideration should be given to the side effects of urban density on cancer patients’ perceived wellbeing.

Impaired Family Functioning Affects 6-Month and 12-Month Postoperative Weight Loss.

Prior cross-sectional research details the high rate of impaired family functioning, a measure of the overall family environment, among adult bariatric surgery patients; however, family functioning has not been explored in relation to adult patient's postoperative outcomes. The objective of this study was to determine how family functioning affects postoperative patient outcomes including readmission rates, early complications, and 6- and 12-month percent total weight loss (%TWL). An observational design at a single-academic medical center was employed. The sample comprised 98 patients, living with ≥1 family member, who enrolled in one of two concurrent studies at the Center. Patients were followed from their surgical intake through 12 months postsurgery; family functioning was assessed within 2 months of their date of surgery. Chi-square and independent t tests determined significant associations between family functioning with readmission and complication rates. Average family functioning was an independent variable in multivariate linear regression models to determine significant correlates of %TWL at 6 and 12 months postsurgery. Patient age, race, and insurance status were included as covariates. Patients with higher impaired family functioning had significantly less %TWL at 6 (p=.004) and 12 months (p=.030). Black patients also had significantly lower %TWL at 6 (p=.003) and 12 months (p=.009). Family functioning and patient race were both correlates of weight loss at 6 months and 12 months. Future research should explore additional family factors as correlates of patient outcomes following bariatric surgery.

Trends in Cochlear Implantation in Texas: An Exploration of Outpatient Discharge Data, 2010 to 2017.

To describe trends in cochlear implantation (CI) disparities in Texas using an all-payer database from 2010 to 2017. Texas Outpatient Surgical and Radiological Procedure Data, a public use data file, was accessed to analyze outpatient CI cases for Texas. Variables analyzed include patient age, sex, race/ethnicity, and insurance status. Population data from the American Community Survey generated CI utilization rates by patient demographic characteristics. There were 6158 CI cases identified during the study period. The number of CI per year nearly doubled from 497 in 2010 to 961 in 2017. The majority of CI recipients were white (59.5%), male (51.9%), and privately insured (47.9%). All sub-populations statewide had more CI in 2017 compared to 2010, with the overall CI per 100 000 population increasing from 1.98 to 3.50 per 100 000 population. Patients over 75 demonstrated the greatest increase in the CI rate per 100 000 population, increasing from 4.60 in 2010 to 14.30 in 2017. Regarding race/ethnicity, all sub-populations noted an increase in the CI per 100 000 population, with white patients demonstrating the highest rate in 2017, at 4.36 CI per 100 000 population. Asian patients had a 502% increase in the CI rate (from 0.42 to 2.53), compared with 87.9%, 84.4%, and 69.2% increases for white, Black, and Hispanic populations, respectively. CI became more widespread between 2010 and 2017, benefiting certain populations more than others. Black and Hispanic populations had lower CI per 100 000 population than their white peers, while patients >65 years of age accounted for the greatest increase in CI.

The Cost of Representation: Insurance Status, Gender, and Cardiac Outcomes in Emergency Department Care

AbstractRecent innovations in representative bureaucracy push the theory toward the micro‐foundations of who represents and who receives representation. Contributing to the micro theory, I draw from street‐level bureaucracy which recognizes how certain client characteristics beyond a shared identity may make representation too costly. Using data on emergency department visits to Florida hospitals, I explore how the impact of physician‐patient gender matching on client outcomes is moderated by a patient's insurance status. While emergency departments offer universal, public access, services performed on publicly insured and uninsured individuals are reimbursed at a lower rate than the privately insured. These features present an opportunity to test how responsive representation is to different client costs and benefits. The findings suggest that public insurance status is not a barrier for women's representation. However, uninsured women do not see any improvement in outcomes when receiving representation.

77 Insurance Status Plays a Role in Burn Patient Disposition Following Burn Injury

Abstract Introduction According to the Emergency Medical Treatment and Labor Act (EMTALA), patient insurance status has no role in stabilization and treatment of patients brought into the emergency department. However, uninsured patients with burn injuries are more likely than insured patients to face early hospital discharge, and less likely to receive care in skilled facilities for post-hospital rehabilitation. Following trauma, insured patients are more likely to have longer hospital lengths of stay (LOS) compared to uninsured patients, resulting in a loss of clinical equipoise. The purpose of this study was to evaluate whether insurance status affects morbidity, mortality, LOS, and post-discharge care for patients with burn injuries. We hypothesized that insured patients would experience fewer complications but would more frequently be referred to skilled facilities for extended post-discharge care. Methods We retrospectively queried our prospectively collected burn database for all patients suffering from burn trauma and admitted to our burn facility from July 2015-December 2015. Patients under the age of 18 years and those without documented insurance status were excluded. Our primary outcome was any complication. Secondary outcomes included LOS, ICU days, mortality, and disposition. Chi-square and multivariate logistic regression analyses were performed using Stata. Results A total of 451 patients were included (74% male, median age 51 years, TBSA 20% ± 1.1, 27% were uninsured). Univariate analysis showed that uninsured patients were less likely to have complications following burn injury, as well as less likely to have any comorbidities (28.5% vs. 40.6%, p=0.02; 55.3% vs. 72.3%, p=0.001, respectively). After controlling for confounding variables (Baux score, comorbidities, gender, and smoking) we found there was no association between complications and insurance status (OR 1.45, 95% CI 0.91–2.33). Using the same regression model, we secondarily found that there was a significant association between disposition after hospital admission and insurance status (OR 1.94, 95% CI 1.04–3.62) as well as an association between mortality and insurance status (OR 0.35, 95% CI 0.17–0.72). Conclusions At our institution, insurance status for burn patients was not associated with differences in length of stay or complications. Despite this, insured patients were more likely to be discharged to skilled facilities and less likely to die from burn injury. Disparate outcomes between insured and un-insured burn patients remain a significant concern, and more studies are needed to understand underlying etiologies.

What drives different treatment choices? Investigation of hospital ownership, system membership and competition

BackgroundDifferences in ownership types have attracted considerable interest because of policy implications. Moreover, competition in hospital markets is promoted to reduce health care spending. However, the effects of system membership and competition on treatment choices of hospitals have not been considered in studying hospital ownership types. We examine the treatment choices of hospitals considering ownership types (not-for-profit, for-profit, and government), system membership, patient insurance status (insured, and uninsured) and hospital competition in the United States.MethodsWe estimate the probability of according the procedure as the treatment employing logistic regression. We consider all procedures accorded at hospitals, controlling for procedure type and diagnosis as well as relevant patient and hospital characteristics. Competition faced by hospitals is measured using a distance-weighted approach separately for procedural groups. Patient records are obtained from State Inpatient Databases for 11 states and hospital characteristics come from American Hospital Association Annual Survey.ResultsNot-for-profit hospitals facing low for-profit competition that are nonmembers of hospital systems, act like government hospitals, whereas not-for-profits facing high for-profit competition and system member not-for-profits facing low for-profit competition are not statistically significantly different from their for-profit counterparts in terms of treatment choices. Uninsured patients are on average 7% less likely to be accorded the procedure as the treatment at system member not-for-profit hospitals facing high for-profit competition than insured patients. System member not-for-profit hospitals, which account for over half of the observations in the analysis, are on average 16% more likely to accord the procedure as the treatment when facing high for-profit competition than low-for-profit competition.ConclusionsWe show that treatment choices of hospitals differ by system membership and the level of for-profit competition faced by the hospitals in addition to hospital ownership type and health insurance status of patients. Our results support that hospital system member not-for-profits and not-for-profits facing high for-profit competition are for-profits in disguise. Therefore, system membership is an important characteristic to consider in addition to market competitiveness when tax exemption of not-for-profits are revisited. Moreover, higher competition may lead to increasing health care costs due to more aggressive treatment choices, which should be taken into account while regulating hospital markets.

Race and Insurance Status Association With Receiving Orthopedic Surgeon-Prescribed Foot Orthoses.

This study looked at the effect of patient demographics, insurance status, education, and patient opinion on whether various orthotic footwear prescribed for a variety of diagnoses were received by the patient. The study also assessed the effect of the orthoses on relief of symptoms. Chart review documented patient demographics, diagnoses, and medical comorbidities. Eligible patients completed a survey either while in the clinic or by phone after their clinic visit. Of the 382 patients prescribed orthoses, 235 (61.5%) received their orthoses; 186 (48.7%) filled out the survey. Race and whether or not the patient received the orthosis were found to be significant predictors of survey completion. Race, type of insurance, and amount of orthotic cost covered by insurance were significant predictors of whether or not patients received their prescribed orthoses. Type of orthosis, diabetes as a comorbidity, education, income, sex, and diagnosis were not significant predictors of whether the patient received the orthosis. Qualitative results from the survey revealed that among those receiving their orthoses, 87% experienced improvement in symptoms: 21% felt completely relieved, 66% felt better, 10% felt no different, and 3% felt worse. We found that white patients had almost 3 times the odds of receiving prescribed orthoses as black patients, even after controlling for type of insurance, suggesting race to be the primary driver of discrepancies, raising the question of what can be done to address these inequalities. While large, systematic change will be necessary, some strategies can be employed by those working directly in patient care, such as informing primary care practices of their ability to see patients with limited insurance, limiting blanket refusal policies for government insurance, and educating office staff on how to efficiently work with Medicare and Medicaid. Level III, comparative study.

The impact of urgent care centers on nonemergent emergency department visits.

To estimate the impact of urgent care centers on emergency department (ED) use. Secondary data from a novel urgent care center database, linked to the Healthcare Cost and Utilization Project State Emergency Department Databases (SEDD) from six states. We used a difference-in-differences design to examine ZIP code-level changes in the acuity mix of emergency department visits when local urgent care centers were open versus closed. ZIP codes with no urgent care centers served as a control group. We tested for differential impacts of urgent care centers according to ED wait time and patient insurance status. Urgent care center daily operating times were determined via the urgent care center database. Emergency department visit acuity was assessed by applying the NYU ED algorithm to the SEDD data. Urgent care locations and nearby emergency department encounters were linked via zip code. We found that having an open urgent care center in a ZIP code reduced the total number of ED visits by residents in that ZIP code by 17.2% (P<0.05), due largely to decreases in visits for less emergent conditions. This effect was concentrated among visits to EDs with the longest wait times. We found that urgent care centers reduced the total number of uninsured and Medicaid visits to the ED by 21% (P<0.05) and 29.1% (P<0.05), respectively. During the hours they are open, urgent care centers appear to be treating patients who otherwise would have visited the ED. This suggests that urgent care centers have the potential to reduce health care expenditures, though questions remain about their net cost impact. Future work should assess whether urgent care centers can improve health care access among populations that often experience barriers to receiving timely care.

529 The impact of insurance on antenatal and postnatal maternal transfers

To determine an association between patient insurance status and time of obstetrical transport. We assembled a retrospective cohort of pregnant and postpartum women transferred to an academic tertiary care center in Massachusetts between 2018 and 2020. We included pregnant women at or beyond 22 weeks and those delivering within 6 weeks prior to transport. Women transferred from a neighboring tertiary care center due to a facility emergency were excluded. Our primary exposure of interest was public insurance status during transport. Our primary outcome of interest was antenatal, as opposed to postnatal, transport status due to improved neonatal outcomes and decreased fragmentation of care. We used univariate analysis and logistic regression to examine associations between insurance status and the primary outcome while controlling for patient and hospital-level covariates. We identified 549 eligible patients, 210 (38%) of whom were publicly insured and 339 (62%) of whom were privately insured. Publicly insured women were less likely to be white and speak English as a primary language (Figure 1). Preterm labor or rupture of membranes was the most common indication for antepartum transfer (n=146, 37.0%) with neonatal condition as the most common postpartum indication (n=125, 81%). Indications for transport did not differ according to insurance status (Figure 2). Publicly insured women were more likely to be transported still pregnant as opposed to postpartum compared to their privately insured counterparts (79.1% v. 67.5%, p<0.01). In an adjusted analysis, publicly insured women were more likely to be transported pregnant compared to those with private insurance (aOR 1.69, 95% CI 1.06-2.69). These findings were unchanged in a sensitivity analysis limited to Massachusetts residents (n=477). Publicly insured obstetric patients are more likely to be transferred prior to delivery compared to their privately insured counterparts. The interaction between these associations and payment models should be explored to ensure equitable access to regionalized maternity care.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Internal Medicine Trainee Understanding and Reaction to Out of Pocket Costs.

Background:Out-of-pocket costs are a serious barrier to care and drive suboptimal medical therapy. Understanding of these costs can lead to care oriented around the limits they generate. Despite this, there is minimal attention paid to these costs in post-graduate education.Objective:To define a potential knowledge gap regarding costs experienced by patients by surveying Internal Medicine residents at our large academic institution.Methods:We surveyed Internal Medicine residents in spring 2019 about knowledge and practices surrounding patient out-of-pocket costs. Participants answered questions considering their most recent inpatient panel and their clinic patient panel. Familiarity was ranked on a 5-point Likert scale, and for the purposes of presentation, was divided into “Poor” and “Moderate or Better.” Non-parametric analysis was used to test differences between outpatients and inpatients and by year of training.Results:Of 159 residents, 109 (67%) responded. Familiarity with patient insurance status was moderate or better in 85%. Reported understanding of costs associated with medications, testing, and clinic visits was less common. Respondents had higher familiarity with out-of-pocket costs for clinic patients compared with inpatients. Knowledge of cost of care was not an often-considered factor in decision making. There was no significant difference in response by year of training.Conclusion:Patient out-of-pocket costs are an important dimension of patient care which Internal Medicine Trainees at our institution do not confidently understand or utilize. Improvements in education around this topic may enable more patient-centered care.

Social determinants of health affect unplanned readmissions following acute myocardial infarction.

Background: Low socioeconomic status predicts inferior clinical outcomes in many patient populations. The effects of patient insurance status and hospital safety-net status on readmission rates following acute myocardial infarctionare unclear. Materials & methods: A retrospective review of State Inpatient Databases for New York, California, Florida and Maryland, 2007-2014. Results:A total of1,055,162 patients were included. Medicaid status was associated with 37.7 and 44.0% increases in risk-adjusted readmission odds at 30 and 90 days (p<0.0001). Uninsured status was associated with reduced odds of readmission at both time points. High-burden safety-net status was associated with 9.6 and 9.5% increased odds of readmission at 30 and 90 days (p<0.0003). Conclusion: Insurance status and hospital safety-net burden affect readmission odds following acutemyocardial infarction.

Compliance with Short-Interval Follow-up MRI after Benign Concordant MRI-guided Breast Biopsy.

To evaluate compliance with short-interval follow-up MRI after benign concordant MRI-guided breast biopsy. This institutional review board-approved retrospective study included all benign concordant MRI-guided biopsies performed between January 1, 2010, and May 1, 2018. The following was collected from the electronic medical record: patient and lesion characteristics, short-interval follow-up MRI recommendation, communication to referring physician, follow-up imaging, repeat biopsies, biopsy outcome, and patient insurance status. Compliance with recommendations was defined as follow-up MRI within 9 months of biopsy. Among 98 patients, there were 107 lesions with benign concordant MRI-guided biopsy results that met study criteria. After excluding 7 patients who underwent subsequent mastectomy, 50/91 (54.9%) patients were recommended short-interval follow-up MRI. Of these, 33/50 (66.0%) had a short-interval follow-up MRI. Direct communication of the short-interval follow-up recommendation was documented in the biopsy report for 4/50 (8%) patients. Subsequent MRI was available for 77/107 (72%) lesions at a median of 29 months following MRI-guided biopsy (range, 3-96 months). Subsequent mammography was available for 21/30 (70%) remaining lesions at a median of 47 months following MRI-guided biopsy (range, 23-88 months). There were two repeat biopsies, with one subsequent malignancy, resulting in a false-negative rate of 0.9% (1/107). When short-interval follow-up MRI was recommended following benign concordant MRI-guided breast biopsy, compliance was 66.0%. Lack of communication of the recommendation may at least partially explain the low compliance. The low false-negative rate (0.9%) suggests routine short-interval follow-up MRI may be unnecessary following benign concordant MRI-guided biopsy.