Distinguishing Death from Disenrollment in Claims Data Using a Readily Implemented Machine Learning Algorithm

Background: The inability to identify dates of death in insurance claims data is a major limitation to retrospective claims based research. If not an outcome, death is a competing risk and poses a threat to validity when treated as non-informative right censoring. Objectives: We aim to develop a user-friendly public algorithm to predict death within the year of disenrollment using an administrative claims database. Methods: We identified adults (18+ years) with at least 2 years of continuous enrollment prior to disenrollment between 01/2007 and 01/2018. Leveraging unique linkages in addition to data that are typically unavailable in the publicly licensed data, we ascertained date of death from the Social Security Death Index, inpatient discharge status, and death indicators in the administrative data. Models including candidate predictors for age, sex, Census region, month of disenrollment, year of disenrollment, chronic condition indicators (components of the Elixhauser score), and prior healthcare utilization were estimated using used elastic net regression tuned by 5-fold cross-validation and final models evaluated in an independent testing set. Weighted analysis adjusts for rare outcome (i.e., class imbalance). Sensitivity, specificity, and ROC associated with various thresholds of predicted probability to classify death at disenrollment were calculated. Results: Overall, we identified 13,360,460 beneficiaries who disenrolled during the study period, with 5% of patients who died within the year of disenrollment. The strongest predictors of death were age at disenrollment, diagnosis of metastatic cancer in the year prior to death, and type of care received (e.g., inpatient stay, hospice care). Using a prediction threshold of 30%, the algorithm classified death at disenrollment with a sensitivity of 0.684 and specificity of 0.985 (ROC=0.97. At the same prediction threshold, the weighted algorithm classified death with a sensitivity of .947 and a specificity of 0.898 (ROC=.973). Conclusions: Our algorithm uses publicly defined chronic conditions and utilization patterns that are easy to implement in claims data and predicts death at disenrollment with high specificity and varying sensitivity depending on the chosen prediction threshold. Users can easily implement the algorithm and can choose the prediction threshold (balancing sensitivity and specificity) to meet the needs of the specific study at hand

External validation of a machine learning algorithm to distinguish death from disenrollment in claims data

Poster presentation from the 38th International Conference on Pharmacoepidemiology & Therapeutic Risk Managemen

Distinguishing Death from Disenrollment: Applying a Predictive Algorithm to Reduce Bias in Estimating the Risk of Rehospitalization

Background: The inability to identify dates of death in several insurance claims data sources can result in biased estimates when death is a competing event. To address this issue, an algorithm to predict when plan disenrollment is due to death was developed and validated using the MarketScan insurance claims data. Objectives: We illustrate the bias introduced when estimating the risk of rehospitalization within 90-days of acute myocardial infarction (AMI) if death is not accounted for as a competing event. We demonstrate how this validated algorithm can be used to reduce this bias. Methods: We use a 20% sample of Medicare claims (2007–2017) to identify patients with an incident admission for AMI. Patients were required to be 66+ years of age with employer-sponsored supplemental insurance. We compare 3 methods of estimating the risk of 90-day rehospitalization. The first method uses the true death data available in the Medicare enrollment data. We used cumulative incidence functions to estimate the risk of rehospitalization, accounting for death as a competing risk. The second method mimics scenarios where death data are unavailable, and patients are disenrolled from insurance coverage shortly after death. We used Kaplan Meier curves to estimate the risk of rehospitalization, treating death as non-informative censoring at the time of disenrollment. The third method applies the validated predictive algorithm to the Medicare claims where death date has been obscured. We used a predicted probability threshold of 0.99 to distinguish between plan disenrollment and death (sensitivity = 0.92, specificity = 0.90). We estimated the risk of rehospitalization accounting for predicted death as a competing risk. Results: We identified 12 753 patients with an index hospitalization for AMI (mean age = 77.8 years). When accounting for death as a competing risk using validated death dates, the estimated 90-day risk of rehospitalization was 21.6% (20.8%, 22.3%). When mimicking a scenario where death is treated as non-informative censoring at the time of disenrollment, the estimated 90-day risk was 24.8% (23.9%, 25.6%). When using the algorithm to distinguish between death and disenrollment and accounting for predicted death as a competing risk, the estimated 90-day risk was 21.7% (21.0%, 22.4%). Conclusions: When estimating the risk of rehospitalization following AMI in a cohort of Medicare patients, applying a claims-based algorithm to predict death resulted in estimates that closely mirrored the estimates using validated death data. Alternatively, failure to account for death as a competing risk resulted in an estimate that was biased upwards

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

An Algorithm to Predict Out-of-Hospital Death Using Insurance Claims Data

Background: The inability to identify dates of death in insurance claims data is a major limitation to retrospective claims-based research. Deaths likely result in disenrollment; however, disenrollment may also reflect a change in insurance provider. We aim to develop a user-friendly public algorithm to predict death within the year of disenrollment using an administrative claims database. Methods: We identified adults (18+ years) with at least 1 year of continuous enrollment prior to disenrollment in 2007-2018. Using Social Security Death Index, inpatient discharge status, and death indicators in the administrative data as the gold standard, we used claims in the prior year to predict death. Models including candidate predictors for age, sex, Census region, month of disenrollment, chronic condition indicators (components of the Elixhauser score), and prior healthcare utilization were estimated using used elastic net regression tuned by 5-fold cross-validation and final models evaluated in an independent testing set. Weighted analysis adjusts for rare outcome (i.e., class imbalance). Sensitivity and specificity associated with various thresholds of predicted probability to classify death at disenrollment were calculated. Results: We identified 13,360,460 beneficiaries who disenrolled during the study period, with 5% of patients who died within the 61 days of disenrollment. The strongest predictors of death were age at disenrollment, diagnosis of metastatic cancer in the year prior to death, and type of care received (e.g., inpatient stay, hospice care). Using a prediction threshold of 30%, the algorithm classified death at disenrollment with a sensitivity of 0.684 and specificity of 0.985 (ROC=0.97). Conclusions: Our algorithm uses publicly defined chronic conditions and utilization patterns that are easy to implement in claims data and predicts death at disenrollment

An Algorithm to Predict Out-of-Hospital Death Using Insurance Claims Data

Slide presentation to accompany manuscript. Background: The inability to identify dates of death in insurance claims data is a major limitation to retrospective claims-based research. Deaths likely result in disenrollment; however, disenrollment may also reflect a change in insurance provider. We aim to develop a user-friendly public algorithm to predict death within the year of disenrollment using an administrative claims database. Methods: We identified adults (18+ years) with at least 1 year of continuous enrollment prior to disenrollment in 2007-2018. Using Social Security Death Index, inpatient discharge status, and death indicators in the administrative data as the gold standard, we used claims in the prior year to predict death. Models including candidate predictors for age, sex, Census region, month of disenrollment, chronic condition indicators (components of the Elixhauser score), and prior healthcare utilization were estimated using used elastic net regression tuned by 5-fold cross-validation and final models evaluated in an independent testing set. Weighted analysis adjusts for rare outcome (i.e., class imbalance). Sensitivity and specificity associated with various thresholds of predicted probability to classify death at disenrollment were calculated. Results: We identified 13,360,460 beneficiaries who disenrolled during the study period, with 5% of patients who died within the 61 days of disenrollment. The strongest predictors of death were age at disenrollment, diagnosis of metastatic cancer in the year prior to death, and type of care received (e.g., inpatient stay, hospice care). Using a prediction threshold of 30%, the algorithm classified death at disenrollment with a sensitivity of 0.684 and specificity of 0.985 (ROC=0.97). Conclusions: Our algorithm uses publicly defined chronic conditions and utilization patterns that are easy to implement in claims data and predicts death at disenrollment. Users can easily implement the algorithm and can choose the prediction threshold (balancing sensitivity and specificity) t

In-House Attending Trauma Surgeon Does Not Reduce Mortality in Patients Presented to a Level 1 Trauma Center

Abstract Background: Trauma is the leading cause of death in the Western world. Trauma systems have been paramount in opposing this problem. Commonly, Level 1 Trauma Centers are staffed by in-house (IH) attending trauma surgeons available 24/7, whereas other institutions function on an on-call (OC) basis with defined response times. There is on-going debate about the value of an IH attending trauma surgeon compared to OC trauma surgeons regarding clinical outcome. Methods: This study was performed at a tertiary care facility complying with all requirements to be a designated Level 1 Trauma Center as defined by the American College of Surgeons Committee on Trauma (ACSCOT). Inclusion occurred from January 1, 2012 through December 31, 2013. Patients were assigned an identifier for IH trauma surgeon attendance versus OC attendance. The primary outcome variable studied was overall mortality in relation to IH or OC attending trauma surgeons. Additionally, time to operating theater, hospital length-of-stay (HLOS), and intensive care unit (ICU) admittance were investigated. Results: A total of 1,287 unique trauma cases in 1,285 patients were presented to the trauma team. Of all cases, 712 (55.3%) occurred between 1700h and 0800h. These 712 cases were treated by an IH attending in 66.3% (n = 472) and an OC attending in 33.7% (n = 240). In the group of patients treated by an IH attending trauma surgeon, the overall mortality rate was 5.5% (n = 26); in the group treated by an OC attending, the overall mortality rate was 4.6% (n = 11; P = .599). Cause of death was traumatic brain injury (TBI) in 57.6%. No significant difference was found in the time between initial presentation at the trauma room and arrival in the operating theater. Conclusion: In terms of trauma-related mortality during non-office hours, no benefit was demonstrated through IH trauma surgeons compared to OC trauma surgeons

In-House Attending Trauma Surgeon Does Not Reduce Mortality in Patients Presented to a Level 1 Trauma Center

Abstract Background: Trauma is the leading cause of death in the Western world. Trauma systems have been paramount in opposing this problem. Commonly, Level 1 Trauma Centers are staffed by in-house (IH) attending trauma surgeons available 24/7, whereas other institutions function on an on-call (OC) basis with defined response times. There is on-going debate about the value of an IH attending trauma surgeon compared to OC trauma surgeons regarding clinical outcome. Methods: This study was performed at a tertiary care facility complying with all requirements to be a designated Level 1 Trauma Center as defined by the American College of Surgeons Committee on Trauma (ACSCOT). Inclusion occurred from January 1, 2012 through December 31, 2013. Patients were assigned an identifier for IH trauma surgeon attendance versus OC attendance. The primary outcome variable studied was overall mortality in relation to IH or OC attending trauma surgeons. Additionally, time to operating theater, hospital length-of-stay (HLOS), and intensive care unit (ICU) admittance were investigated. Results: A total of 1,287 unique trauma cases in 1,285 patients were presented to the trauma team. Of all cases, 712 (55.3%) occurred between 1700h and 0800h. These 712 cases were treated by an IH attending in 66.3% (n = 472) and an OC attending in 33.7% (n = 240). In the group of patients treated by an IH attending trauma surgeon, the overall mortality rate was 5.5% (n = 26); in the group treated by an OC attending, the overall mortality rate was 4.6% (n = 11; P = .599). Cause of death was traumatic brain injury (TBI) in 57.6%. No significant difference was found in the time between initial presentation at the trauma room and arrival in the operating theater. Conclusion: In terms of trauma-related mortality during non-office hours, no benefit was demonstrated through IH trauma surgeons compared to OC trauma surgeons

An Algorithm to Predict Death Using Insurance Claims Data

The inability to identify dates of death in insurance claims data is a major limitation to retrospective claims-based research. Deaths likely result in disenrollment; however, disenrollment may also reflect a change in insurance provider. We aim to develop a user-friendly public algorithm to predict death within the year of disenrollment using an administrative claims database

Excess Antibiotic Treatment Duration and Adverse Events in Patients Hospitalized With Pneumonia: A Multihospital Cohort Study

Background: Randomized trials demonstrate no benefit from antibiotic treatment exceeding the shortest effective duration. Objective: To examine predictors and outcomes associated with excess duration of antibiotic treatment. Design: Retrospective cohort study. Setting: 43 hospitals in the Michigan Hospital Medicine Safety Consortium. Patients: 6481 general care medical patients with pneumonia. Measurements: The primary outcome was the rate of excess antibiotic treatment duration (excess days per 30-day period). Excess days were calculated by subtracting each patient\u27s shortest effective (expected) treatment duration (based on time to clinical stability, pathogen, and pneumonia classification [community-acquired vs. health care-associated]) from the actual duration. Negative binomial generalized estimating equations (GEEs) were used to calculate rate ratios to assess predictors of 30-day rates of excess duration. Patient outcomes, assessed at 30 days via the medical record and telephone calls, were evaluated using logit GEEs that adjusted for patient characteristics and probability of treatment. Results: Two thirds (67.8% [4391 of 6481]) of patients received excess antibiotic therapy. Antibiotics prescribed at discharge accounted for 93.2% of excess duration. Patients who had respiratory cultures or nonculture diagnostic testing, had a longer stay, received a high-risk antibiotic in the prior 90 days, had community-acquired pneumonia, or did not have a total antibiotic treatment duration documented at discharge were more likely to receive excess treatment. Excess treatment was not associated with lower rates of any adverse outcomes, including death, readmission, emergency department visit, or Clostridioides difficile infection. Each excess day of treatment was associated with a 5% increase in the odds of antibiotic-associated adverse events reported by patients after discharge. Limitation: Retrospective design; not all patients could be contacted to report 30-day outcomes. Conclusion: Patients hospitalized with pneumonia often receive excess antibiotic therapy. Excess antibiotic treatment was associated with patient-reported adverse events. Future interventions should focus on whether reducing excess treatment and improving documentation at discharge improves outcomes. Primary Funding Source: Blue Cross Blue Shield of Michigan (BCBSM) and Blue Care Network as part of the BCBSM Value Partnerships program