Independent Predictors for Hospitalization-Associated Radiotherapy Interruptions

Open AccessPublished:July 30, 2022DOI:



      Radiation treatment interruption associated with unplanned hospitalization remains understudied. The intent of this study was to benchmark frequency of hospitalization-associated radiotherapy interruptions (HARTI), characterize disease processes causing hospitalization during radiation, identify factors predictive for HARTI, and localize neighborhood environments associated with HARTI at our academic referral center.


      Retrospective review of electronic health records provided descriptive statistics of HARTI event rates in our institutional practice. Univariable and multivariable logistic regression models were developed to identify significant factors predictive for HARTI. Causes of hospitalization were established from primary discharge diagnoses. HARTI rates were mapped according to patient residence addresses.


      Between January 1, 2015, and December 31, 2017, 197 (5.3%) HARTI events were captured across 3,729 patients with 727 total missed treatments. The three most common causes of hospitalization were malnutrition/dehydration (n=28, 17.7%), respiratory distress/infection (n=24, 13.7%), and fever/sepsis (n=17, 9.7%). Factors predictive for HARTI included African American race (OR, 1.48; 95% CI, 1.07-2.06; P = 0.018), Medicaid/uninsured status (OR, 2.05; 95% CI, 1.32-3.15; P = 0.0013), Medicare coverage (OR 1.7; 95% CI, 1.21-2.39, P = 0.0022), lung (OR 5.97; 95% CI, 3.22-11.44; P < 0.0001) and head and neck (OR 5.6; 95% CI, 2.96-10.93; P < 0.0001) malignancies, and prescriptions greater than 20 fractions (OR 2.23; 95% CI, 1.51-3.34; P < 0.0001). HARTI events clustered among 1) Medicaid/uninsured patients living in urban, low-income, majority-African American neighborhoods, and 2) patients from middle-income suburban communities, independent of race and insurance status. Only the wealthiest residential areas demonstrated low HARTI rates.


      HARTI disproportionately impacted socioeconomically disadvantaged urban patients facing high treatment burden in our catchment population. Complementary geospatial analysis also captured risk experienced by middle-income suburban patients independent of race or insurance status. Confirmatory studies are warranted to provide scale and context to guide intervention strategies to equitably reduce HARTI events.


      Radiation therapy is an integral component of cancer care. Approximately 50% of all cancer patients will undergo at least one course of radiation treatment [1]. Optimal tumor control requires strict adherence to daily treatment scheduling. Unplanned interruptions are associated with inferior outcomes, including reduced overall survival [2-8].
      Hospitalization during radiotherapy is a severe, potentially preventable complication of treatment [9, 10]. Limited data are available to identify specific causes for hospitalization during radiotherapy [10]. The purpose of this study was to catalog hospitalization rates during radiation therapy and to identify patient-specific demographic, clinical, and treatment factors predictive for hospitalization-associated radiotherapy interruptions (HARTI) at our academic referral center. We also employed secondary geospatial analysis to identify residential neighborhood environments most closely associated with interruption events to localize potential need for interventional support to protect vulnerable populations [11].


      Patient Population

      Institutional IRB approval was obtained to examine electronic health records for patients receiving radiation treatment. Patients were included if they were scheduled to begin RT between January 1, 2015, and December 31, 2017. Only those who initiated therapy were included in the study.

      Outcome Measures

      The primary outcome of the study was frequency of hospitalization-associated radiotherapy interruption (HARTI). HARTI was defined as any unplanned cancellation of scheduled radiotherapy associated with hospitalization during radiotherapy. Hospitalization was defined as emergency department visits as well as inpatient admissions with levels of care ranging from observation to the intensive care unit.
      Secondary outcomes included causes of hospitalization defined by patient primary diagnoses at time of discharge, as well as identification of patient-specific demographic, clinical, and treatment factors significantly predictive for hospitalization-associated radiotherapy interruptions.

      Data Collection

      Patient demographic, clinical, and treatment information was compiled from electronic medical records. Patient predicted income (PPI) was categorized according to median household income from 2016 federal census information at the census tract level and was stratified into low (<$34,000), middle ($34,000-$67,000), and high (>$67,000) thirds for statistical analysis. Residence addresses were mapped at the zip code level for the purpose of geospatial analysis. The season in which patients began treatment was divided into winter (November through February) or non-winter (March through October). Travel distance to the treating facility for individual patients was measured from residence zip code centroids. Rurality of patients’ home addresses was defined according to the United States Department of Agriculture 2013 Rural-Urban Continuum Codes.

      Statistical Analysis

      Descriptive statistical analyses were performed to classify the frequency of HARTI events across the demographic, clinical, and treatment variables. Chi-squared tests were performed to determine significance. Post-hoc pairwise chi-squared tests were utilized to further identify significant factors within each categorical variable. Univariable logistic regression models were developed to determine significant factors from among those previously identified for predicting any interruption event [12-14]. Subsequent stepwise logistic regression models identified those variables most predictive for HARTI, and a multivariable logistic regression model was created to determine significant independent predictors. P values were two sided, and P < 0.05 was considered statistically significant. All analyses were performed using RStudio version 1.3.959 (PBC, Boston, MA) and SAS version 9.4 (SAS Institute, Inc., Cary, NC).

      Geospatial Analysis

      Frequency of HARTI was mapped at the level of residence zip codes and stratified according to patient race and insurance status to identify HARTI hotspots. Geographic data was plotted with RStudio version 1.3.959 (PBC, Boston, MA) using the GIS package ggmap: Spatial Visualization was performed with ggplot2 (Kahle and Wickham, 2013).


      Cohort Characteristics

      A total of 3,729 patients received 72,964 fractions of EBRT between January 1, 2015, and December 31, 2017 with an average prescription of 20 fractions. Of the 3,729 patients, 3,487 (93.5%) completed the entire prescribed regimen. Patient characteristics are described in Table 1. Average patient age was 61.2 years. Two thousand sixty-five (55.4%) patients were female, 2,195 (58.9%) were below the age of 65, 2,032 (54.5%) were Caucasian, 1,577 (43.3%) African American, and 120 (3.2%) reported a race other than Caucasian or African American. Hispanic ethnicity was reported by 50 (1.3%) patients. One thousand nine-hundred and sixty-seven (52.7%) patients were married. Insurance status was recorded as commercial for 1,794 (48.1%) patients, Medicare for 1,503 (40.3%), and Medicaid/uninsured for 432 (11.6%) (221 Medicaid and 211 uninsured patients, 5.9% and 5.7%, respectively). One thousand twelve (27.1%) patients fell within the low, 1,532 (41.1%) within the medium, and 1,149 (30.8%) within the high PPI categories. The most common sites being treated were breast (n=974, 26.1%), metastases (n=490, 13.1%), and prostate (n=413, 11.1%) with 976 (26.1%) patients receiving most commonly 26-30 fractions, 689 (18.5%) >30 fractions, and 586 (15.7%) 16-20 fractions. Most patients (n=2,455, 65.8%) began treatment in non-winter months. The mean distance between patient residence and the treatment facility was 23.7 miles with a median of 9.1 miles (standard deviation 103.3, interquartile range 5.4-15.6).
      Table 1Study Cohort Characteristics and Hospitalization Associated RT Interruption Likelihood
      Number (%)Frequency of Hospitalization Associated Radiotherapy Interruption (%)
      Total3729197 (5.3)
       Male1664 (44.6)6.0
       Female2065 (55.4)4.7
      Age (Mean, years)61.2
       <652195 (58.9)5.1
       ≥651534 (41.1)5.6
       Caucasian2032 (54.5)4.4
      Denotes statistical significance
       African American1577 (42.3)6.3
      Denotes statistical significance
       Other120 (3.2)6.7
       Hispanic50 (1.3)6.0
       Non-Hispanic3540 (94.9)5.2
       Unknown139 (3.7)7.9
      Marital Status
       Married1967 (52.7)4.3
      Denotes statistical significance
       Unmarried1648 (44.2)6.4
      Denotes statistical significance
       Unknown114 (3.1)5.3
      Patient Predicted Income
       Low (<$34k)1012 (27.1)4.4
       Middle($34-67k)1532 (41.1)5.6
       High(>$67k)1149 (30.8)5.7
       Unknown36 (1.0)5.6
      Geography of residence
       Rural not by metro114 (3.0)0.9
       Rural by metro201 (5.4)3.0
       Metro3428 (91.6)5.6
      Distance from RT (miles)
       0-51108 (29.9)6.6
       6-101175 (31.7)5.6
       11-15503 (13.6)3.8
       16-20220 (5.9)6.9
       21-30253 (6.8)4.8
       31-40110 (3.0)1.9
       >40337 (9.1)3.3
      Insurance Type
       Commercial1794 (48.1)3.5
      Denotes statistical significance
       Medicare1503 (40.3)6.3
      Denotes statistical significance
       Medicaid/No Insurance432 (11.6)9.0
      Denotes statistical significance
       Medicaid221 (5.9)7.7
       No Insurance211 (5.7)10.4
      Denotes statistical significance
       Breast974 (26.1)1.7
      Denotes statistical significance
       Prostate413 (11.1)3.4
       Lung353 (9.5)10.8
      Denotes statistical significance
       GYN226 (6.1)6.2
       H&N402 (10.8)10.7
      Denotes statistical significance
       GI238 (6.4)5.0
       CNS148 (4.0)4.7
       Metastasis490 (13.1)4.1
       Skin123 (3.3)5.7
       Soft Tissue53 (1.4)3.8
       Hematologic146 (3.9)2.7
       Other163 (4.4)13.4
      Treatment Season
       Non-Winter (Mar-Oct)2455 (65.8)5.7
       Winter (Nov-Feb)1274 (34.2)4.6
      Prescribed Fractions
       1-5421 (11.3)1.2
      Denotes statistical significance
       6-10494 (13.2)5.7
       11-15195 (5.2)5.1
       16-20586 (15.7)2.2
      Denotes statistical significance
       21-25368 (9.9)4.6
       26-30976 (26.2)7.0
      Denotes statistical significance
       >30689 (18.5)8.1
      Denotes statistical significance
      low asterisk Denotes statistical significance

      Hospitalization-Associated RT Interruption

      HARTI was observed in 197 (5.3%) patients with a total of 727 scheduled treatments missed. Patients missed between 1 and 21 treatments with a median of 2 treatments and a mean of 3.69 treatments (standard deviation 4.13, interquartile range 1-5). Of the 197 patients, 83 (42.1%) missed only one treatment.

      Causes of Hospitalization

      Of the 197 patients to experience HARTI, an identifiable cause of hospitalization was found in 175 patients. Table 2 details the most common principal problems associated with hospitalization as determined by primary discharge diagnoses. The most common primary disease processes leading to hospitalization were malnutrition/dehydration (n = 28, 28.7%), respiratory distress or infection (n = 24, 13.7%), fever/sepsis (n = 17, 9.7%), inadequate pain control (n = 16, 9.1%), renal dysfunction (n = 15, 8.6%), and chest pain (n = 15, 8.6%).
      Table 2Primary Causes of Hospitalization
      Number (%)
      Total175 (100)
      Principal Problem
       Malnutrition/Dehydration28 (17.7)
       Respiratory Distress/Infection24 (13.7)
       Fever/Sepsis17 (9.7)
       Pain Control16 (9.1)
       Renal Dysfunction15 (8.6)
       Chest Pain15 (8.6)
       Neurological Dysfunction10 (5.7)
       PEG Tube Complication9 (5.1)
       Radiation Mucositis/Dermatitis9 (5.1)
       Acute Bleeding Episode7 (4)
       Urinary Tract Infection5 (2.1)
       Soft Tissue Infection5 (2.1)
       Other15 (8.6)

      Predictive Factors for HARTI

      Table 1 details the proportion of patients who experienced HARTI among several demographic, clinical, and treatment factors. Chi-squared analysis identified statistically significant differences in the proportion of patients experiencing HARTI among each factor. Increased likelihood of HARTI was seen among African American (6.3% vs 4.4% for Caucasian, P = 0.016) and unmarried (6.4% vs 4.3% for married, P = 0.007) patients. Pairwise chi-squared analysis further demonstrated that patients treated for lung (10.8%) and head and neck (10.7%) malignancies were significantly more likely to experience HARTI when compared to patients treated for breast (1.7%) malignancies. Patients treated for malignancies with regimens composed of 26-30 (7.0%) and >30 fractions (8.1%) were more likely to experience HARTI compared to those with treatment regimens composed of either 1-5 (1.2%) or 16-20 (2.2%) fractions. Medicare patients were almost twice as likely to experience HARTI (6.3% vs 3.5% for commercially insured, P = 0.0002), and Medicaid/uninsured patients almost three times as likely (9.0% vs 3.5%, P < 0.0001) with 10.4% of uninsured patients experiencing HARTI (P < 0.0001 vs commercial).

      Univariable and Multivariable Analyses of HARTI

      Findings for both univariable analysis (UVA) and multivariable analysis (MVA) models predicting HARTI are described in Table 3. On MVA, African American patients had an almost 50% greater odds to experience HARTI (OR, 1.48; 95% CI, 1.07-2.06; P = 0.018) compared to Caucasian patients. Additionally, both Medicare (OR 1.7; 95% CI, 1.21-2.39, P = 0.0022) and Medicaid/uninsured patients (OR, 2.05; 95% CI, 1.32-3.15; P = 0.0013) had greater odds of HARTI compared to patients with commercial insurance. Those treated more than 20 fractions were more likely (OR 2.23; 95% CI, 1.51-3.34; P < 0.0001) to experience HARTI than those receiving fewer than 20 fractions. Compared to patients treated for breast cancer, significantly higher odds of HARTI were seen among patients treated for lung (OR 5.97; 95% CI, 3.22-11.44; P < 0.0001), head and neck (OR 5.6; 95% CI, 2.96-10.93; P < 0.0001), gynecologic (OR 2.57; 95% CI, 1.20-5.40; P = 0.013), gastrointestinal (OR 2.55; 95% CI, 1.13-5.56; P = 0.02), CNS (OR 2.71; 95% CI 1.00-6.60, P = 0.036), metastatic (OR 3.46; 95% CI, 1.69-7.13; P = 0.0007), and skin malignancies (OR 4.37; 95% CI, 1.58-11.01; P = 0.0026).
      Table 3Analysis of Hospitalization Associated Radiotherapy Interruptions by Study Cohort Characteristics
      Univariable ModelMultivariable Model
      Unadjusted OR (95% CI)PAdjusted OR (95% CI)P
       Female0.77 (0.58-1.03)0.0761.33 (0.93-1.88)0.12
      Age (Mean, years)
       <650.9 (0.67-1.20)0.461.1 (0.71-1.70)0.68
       African American1.48 (1.10-1.98)0.0091.48 (1.07-2.06)0.018
       Other1.56 (0.68-3.11)0.241.49 (0.64-3.06)0.32
      Marital Status
       Unmarried1.38 (1.03-1.85)0.0320.4 (0.02-4.36)0.48
      Patient Predicted Income
       High (>$67k)ReferenceReference
       Middle ($34-67k)0.98 (0.71-1.37)0.920.97 (0.69-1.38)0.88
       Low (<$34k)0.77 (0.52-1.13)0.190.85 (0.56-1.28)0.43
      Geography of residence
       Rural not by metro0.15 (0.01-0.68)0.0610.15 (0.008-0.67)0.058
       Rural by metro0.52 (0.20-1.09)0.120.51 (0.20-1.09)0.12
      Distance from RT in miles
       6-100.83 (0.59-1.17)0.290.82 (0.57-1.17)0.27
       11-150.56 (0.32-0.91)0.0260.7 (0.40-1.16)0.18
       16-201.04 (0.57-1.81)0.881.26 (0.67-2.24)0.46
       21-300.71 (0.36-1.27)0.280.76 (0.38-1.40)0.4
       31-400.27 (0.043-0.86)0.0680.32 (0.05-1.09)0.13
       >400.48 (0.24-0.87)0.0251.32 (0.46-3.21)0.58
      Insurance Type
       Medicare1.85 (1.34-2.58)0.00021.7 (1.21-2.39)0.0022
       Medicaid/No Insurance2.73 (1.79-4.11)<0.00012.05 (1.32-3.15)0.0013
       Medicaid2.29 (1.28-3.90)0.00341.56 (0.85-2.73)0.13
       No Insurance3.20 (1.89-5.24)<0.00012.64 (1.53-4.43)0.0003
       Prostate1.98 (0.95-4.06)0.0611.73 (0.76-3.89)0.19
       Lung6.82 (3.86-12.55)<0.00015.97 (3.22-11.44)<0.0001
       GYN3.73 (1.79-7.69)0.00042.57 (1.20-5.40)0.013
       H&N6.77 (3.88-12.34)<0.00015.6 (2.96-10.93)<0.0001
       GI3.00 (1.38-6.33)0.00422.55 (1.13-5.56)0.02
       CNS2.81 (1.07-6.63)0.0242.71 (1.00-6.60)0.036
       Metastasis2.41 (1.25-4.69)0.00873.46 (1.69-7.13)0.0007
       Skin3.41 (1.30-8.09)0.00764.37 (1.58-11.01)0.0026
       Soft Tissue2.22 (0.35-8.02)0.31.94 (0.30-7.23)0.39
       Hematologic1.59 (0.45-4.37)0.412.28 (0.63-6.57)0.16
       Other7.67 (3.89-15.25)<0.00018.4 (4.07-17.45)<0.0001
      Treatment Season
       Non-Winter (Mar-Oct)ReferenceReference
       Winter (Nov-Feb)0.77 (0.56-1.06)0.110.81 (0.58-1.12)0.22
      Prescribed Fractions
       >202.18 (1.60-3.02)<0.00012.23 (1.51-3.34)<0.0001
      * Denotes statistical significance
      Variables predictive for HARTI on UVA that did not reach statistical significance on MVA included marriage status and travel distance to treatment facility.

      Geospatial Analysis of HARTI

      The greater XXX metropolitan region has been historically shaped by racial and socioeconomic segregation. Central XXX is comprised predominantly of African American neighborhoods, clustered into areas with limited social resources apart from smaller majority White neighborhoods. Suburban/exurban XXX has gradually become more racially diverse but remains majority Caucasian with affluent regions interspersed with middle and low-income rural zip codes. Geospatial analysis of our patient's reported home addresses mapped at the zip code level (Figures 1 and 2) identified associations between HARTI and patient home location. The highest rates were observed in Medicaid/uninsured patients living in urban, low-income, majority-African American neighborhoods. Moving outward from downtown, elevated HARTI rates were observed in middle-income suburban zip codes independent of patient race and insurance coverage. Only the wealthiest zip codes (the so-called “Poplar Corridor” of East XXX) demonstrated low rates of HARTI events.
      Figure 1
      Figure 1Geospatial analysis of hospitalization-associated radiotherapy interruption (HARTI) rates stratified according to patient race. Median household income is mapped at the census tract level according to pre-specified categories. Greater HARTI rates are denoted by larger bubbles plotted at zip code centroids.
      Figure 2
      Figure 2Geospatial analysis of hospitalization-associated radiotherapy interruption (HARTI) rates stratified according to patient insurance. Median household income is mapped at the census tract level according to pre-specified categories. Greater HARTI rates are denoted by larger bubbles plotted at zip code centroids.


      The Southeastern United States experiences some of the worst cancer outcomes in the country [15], attributable in part to socioeconomic burdens endemic to the region such as increased rurality and poverty, which are predictive for increased cancer mortality burden [16-18]. In our Mid-Southern academic referral practice, we have identified candidate risk factors for radiotherapy interruption associated with unplanned hospitalization. African American race, Medicaid/uninsured status, Medicare coverage, longer treatment regimens >20 fractions, and disease sites associated with high radiation toxicity were predictive for HARTI. Government-based coverage or lack of insurance was associated with up to 200% greater risk of HARTI compared to commercial insurance. African American patients faced nearly 50% increased risk, while patients with head and neck or lung cancer experienced almost 6 times the risk experienced by patients with breast malignancies.
      The mechanistic pathways by which upstream social risk and health status factors impact radiation treatment quality are complex and difficult to disentangle. Various theories have been proposed to simplify explanation of persistent associations between social risk factors and health disparities in the face of ongoing improvement in public health and medical interventions over time [19]. The Fundamental Cause Theory is specifically relevant to our current study. As proposed and tested by Link and Phelan [20], this theory in simplest terms postulates that improvements in disease control fuel paradoxical health shortfalls in disadvantaged groups, since advantaged individuals enjoy preferential access to such improvements. Privileged populations are less exposed to the causes of preventable disease and, when impacted, are better treated by virtue of better access to resources. Empirical data has supported the explanatory value of this model across numerous infectious, chronic disease, and mortality rate case examples [21-25], including race/ethnicity-specific COVID-19 transmission patterns observed in the United States [26]. In the case of HARTI, disadvantaged groups (e.g. minority race and/or those without commercial insurance) potentially face greatest risk exposure to preventable chronic disease, including cancer. When faced with the need for full-course radiotherapy for high-burden cancer diagnoses (e.g. head and neck, lung), these patients lack social, financial, and medical support to manage toxicity and comorbidities at home. Hospitalization and RT interruptions ensue, leading to preventable financial cost, morbidity, and outcomes disparities.
      Although identification of specific root causes responsible for HARTI events in this study resided outside our scope of work, secondary geospatial analysis provided insight into residential environments associated with risk. HARTI impacted more patients living in urban low income, majority-minority neighborhoods, as well as suburban low-middle income areas. Expected income appeared to be more tightly associated with HARTI risk than race or insurance in the suburban setting. This echoes data demonstrating tight geographic association between entrenched county/zip code-level poverty and cancer mortality [17], even in the cooperative group trial setting [27]. Access to local assets and social networks intertwine with patient-level socioeconomic factors to determine individual vulnerability [11-13, 28]. Validated identification of specific social, financial, environmental, and health risks mechanistically responsible for HARTI risk in specific patients will be required to effectively triage supportive intervention strategies. Automated warehousing and linkage of high dimensional population-level social risk data to individual-level electronic health record data is a realistic, testable strategy to achieve this [29, 30].
      Previous studies have focused on Medicaid status or lack of insurance as predictive risk factors for radiotherapy interruption [12, 13]. We identified a novel risk for HARTI in our Medicare patient population, potentially attributable to co-existing health issues in this older population. Up to 70% of Medicare beneficiaries have at least two chronic conditions and 14% have six or more comorbidities requiring treatment [31]. It is reasonable to presume that such comorbidities predispose Medicare patients to increased radiation-related toxicity, thereby increasing their risk of hospitalization [32-34]; this would be a testable hypothesis in confirmatory studies.
      Progression of toxicity to the point of unplanned hospitalization may signal inadequacies in supportive management and/or coordination with primary care providers. Individualized supportive care strategies employing real-time, automated collection of patient-reported toxicities and responsive supportive care have been shown to be effective during chemotherapy [35-37] and could be formally investigated in the radiation treatment setting. Many of the causes for hospitalization we found were preventable and could be identified by upfront patient risk stratification. Many hospitalizations could be preempted by primary care teams already familiar with patients. Formal coordination pathways between cancer and primary providers unfortunately remains relatively understudied and is a straightforward path towards holistic care [38-40]. Other institutions have investigated implementation of other interventions, such as patient symptom inventories and intensified visit schedules, and have found these to significantly reduce hospitalizations during cancer treatment [9, 41].
      Reducing hospitalization during radiotherapy would potentially provide significant value. Patients would benefit from optimized cancer treatment outcomes and reduced suffering. All stakeholders, notably provider systems and insurers, would directly benefit from cost savings and improved capacity. From the perspective of Medicare, the average cancer diagnosis-related hospitalization can generate costs totaling more than twice the expected charges for a standard-fractionated radiation treatment course [42, 43]. All patients, insured or uninsured, share direct out-of-pocket expenses from hospitalizations. Stressed families and caregivers are additionally impacted by indirect costs and lost income opportunities.
      Our study has limitations which impact interpretation and generalizability of our findings. First, this is relatively small study population sampled from a single metropolitan region, managed by one provider system, with only 197 HARTI events being captured. It is important to note that the full XXX region surrounding our academic care center is served by several hospital systems, each with siloed electronic health record platforms. If any patients were admitted to outside hospitals in the area, their respective HARTI events would not have been captured. Second, causes for hospitalization were cataloged retrospectively from discharge diagnoses; conclusive association (or lack of association) of hospitalization events with cancer-specific treatment was not possible to establish. Third, impact specific to hospitalization on downstream cancer outcomes relative to RT interruption was not addressed by this data. Finally, it is important to recognize that some zip codes in outlying suburban areas contained small total numbers of captured patients, so any single event would disproportionately impact our risk metric. Full regional sampling of all cancer patients treated with radiotherapy in the region would be required to correct for this. Prompted by COVID-19, we are creating a unified public health observatory for the full XXX region to achieve this goal in future studies [44]. We expect our baseline results to focus more definitive work toward candidate neighborhoods and patient populations most in need.


      In our academic referral practice, we found RT interruptions during unplanned hospitalizations to be associated with Medicaid/uninsured or Medicare coverage, African American race, prolonged treatment course, and treatment of sites with high symptomatic burden requiring intensive treatment. Complementary geospatial analysis identified risk hotspots in low income, urban, majority African American neighborhoods, as well as suburban low-middle income areas independent of race or insurance coverage. These findings are hypothesis-generating and will require additional context via scaled-up sampling from a wider assortment of U.S. cities. Nonetheless, this work promises to guide design and validation of individualized social interventions to meaningfully reduce RT outcome disparities.


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