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  • 37th ICPE - International Society for Pharmacoepidemiology Conference Presentations

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    1. Electronic medical records vs insurance claims: Comparing the magnitude of opioid use prior, during, and following surgery

    Title Tesim:
    Electronic medical records vs insurance claims: Comparing the magnitude of opioid use prior, during, and following surgery
    Creator:
    Dasgupta, Nabarun, Hudgens, Michael, Pate, Virginia, Stürmer, Til, Young, Jessica, Funk, Michele, and Chidgey, Brooke
    Date of publication:
    August 28, 2022
    Abstract Tesim:
    Background: Pharmacoepidemiology studies often use insurance claims and electronic medical record (EMR) data. However, the implications of data source choice on key study design elements are not well understood. Objectives: Use linked claims-EMR data (separately and together) to characterize opioid use as it relates to study eligibility criteria, and exposure and outcome assessment. Methods: EMR data from a large healthcare system were linked to Medicare insurance claims for patients undergoing invasive surgery in an ongoing study on opioid use. Drug utilization based on order and fill dates came from 3 sources: EMR inpatient orders, EMR outpatient orders, and Medicare Part D claims. We evaluated 3 study design elements: a) Study selection - opioid use 182 days before surgery to identify opioid naïve patients; b) Exposure ascertainment- perioperative opioid use; c) Outcome assessment - prolonged opioid use 90 days post-op. Results: We identified 12,023 surgery patients in the linked claims-EMR data. Eligibility: For baseline opioid exposure, 30% (outpatient EMR) vs 28% (claims) had an opioid order (EMR) or fill (claims). Using both claims and outpatient EMR, 44% of patients had evidence of exposure in at least one data source. Perioperative Exposure: For use during surgical admission, combined inpatient and outpatient EMR orders documented much higher use of opioids (81%) compared to claims-only (32%). Combining the 3 data sources, 87% had evidence of opioids ordered or filled during the surgical admission. On the date of surgical discharge 56% (outpatient EMR), 31% (claims), and 65% (claims or outpatient EMR) had evidence of opioids ordered or filled. In the 7 days immediately after surgery, outpatient EMR found 3.0% with orders, claims found 9.2% with opioid fills, and combining the 2 data sources suggested 11% could have received opioids. Prolonged Use Outcome: Outpatient EMR found 4.7% with 90-day postsurgical opioid orders, claims found 6.0%, and combining claims and outpatient EMR found 9.4%. Conclusions: When characterizing opioid exposure, we found substantial non-overlap between EMR and claims depending on time window relative to surgery and care setting, driven by inpatient opioids only captured in EMR and primary nonadherence. In studies that straddle hospital and outpatient settings, the potential for misclassification of drug utilization must be evaluated carefully, and choice of data source may have large impacts on key study design elements. Both EMR and claims data are needed to provide a more complete picture of opioid exposure prior to, during and after surgery.
    Resource type:
    Presentation
    Affiliation Label Tesim:
    Injury Prevention Research Center, Department of Biostatistics, Department of Epidemiology, and Department of Anesthesiology
    Conference Name:
    ICPE 2020
    Type:
    http://purl.org/dc/dcmitype/Text
    DOI:
    https://doi.org/10.17615/q7w3-cj27
    Language Label:
    English
    ORCID:
    Other Affiliation:
    Person:
    Dasgupta, Nabarun, Hudgens, Michael, Pate, Virginia, Stürmer, Til, Young, Jessica, Funk, Michele, and Chidgey, Brooke
    Rights Statement Label:
    In Copyright

    2. Estimating the impact of prescribing limits on prolonged opioid use following surgery

    Title Tesim:
    Estimating the impact of prescribing limits on prolonged opioid use following surgery
    Creator:
    Chidgey, Brooke, Stürmer, Til, Funk, Michele, Pate, Virginia, Hudgens, Michael, Young, Jessica, and Dasgupta, Nabarun
    Date of publication:
    August 28, 2020
    Abstract Tesim:
    Background: In response to concerns about opioid addiction, some states now limit the days supplied (DS) for initial postoperative prescriptions. However, few studies have examined the impact of these policy changes on prolonged opioid use in the population. Objectives: To a) examine the gradient of risk of prolonged postsurgical opioid use based on the initial prescription duration, and b) estimate the potential impact of varying prescribing limits on risk of prolonged postsurgical opioid use. Methods: We used a 20% random sample of Medicare claims (2007-2016) to identify opioid naive patients undergoing invasive surgery. Prolonged use was defined as at least 1 Rx in each of 3 consecutive 30-day windows immediately following surgery. We calculated 90-day risk of prolonged use comparing patients at a given DS level to patients receiving longer initial prescriptions. Adjusted risk differences (aRD) were obtained via standardized mortality ratio weights (adjusted for demographics, surgical characteristics, baseline medication use and comorbidities), comparing patients with a Rx greater than a given prescribing limit versus those at the limit. Estimated number of averted cases was also quantified. Results: We identified 749,269 patients who received a perioperative opioid (median DS=5, mean age=73yr, 44% male). The overall risk of prolonged use was 14.0 (95%CI: 13.7, 14.3) per 1000 exposed patients, increasing with days supplied (14.7 for >2 days to 34.4 for >15 days). Among patients with >2 DS (n=615,490; 92%), we estimated 3.3 (1.0, 5.6)/1000 additional cases of prolonged opioid use compared to those receiving 2 DS. Among patients receiving >7 days (n=143,408; 22%), a common limit in state laws, the aRD was nearly null (-0.1/1000). At 15+ days (n=21,382; 3%), we estimated 3.5 (-0.6, 7.6)/1,000 additional cases compared to those receiving exactly 15 days. The estimated number of potentially averted cases of prolonged opioid use ranged from 10,144 (2 days) to 373 (15 days). Conclusions: We illustrate a method to examine potential impacts of prescribing limits. While risk of prolonged postsurgical opioid use increased as patients received larger initial DS, the number of prolonged use cases theoretically preventable by prescribing limits differed by orders of magnitude depending on the number of patients above the proposed limit. While most laws focus on DS limits, results for quantity and dosage dispensed show similar trends and will also be presented, along with procedure specific (knee arthroplasty, hernia repair, cholecystectomy) results.
    Affiliation Label Tesim:
    Department of Anesthesiology, Department of Epidemiology, Department of Biostatistics, and Injury Prevention Research Center
    Conference Name:
    ICPE 2020
    Type:
    http://purl.org/dc/dcmitype/Text
    DOI:
    https://doi.org/10.17615/hfek-4r62
    Language Label:
    English
    ORCID:
    Other Affiliation:
    Person:
    Chidgey, Brooke, Stürmer, Til, Funk, Michele, Pate, Virginia, Hudgens, Michael, Young, Jessica, and Dasgupta, Nabarun
    Rights Statement Label:
    In Copyright

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

    Title Tesim:
    Distinguishing Death from Disenrollment: Applying a Predictive Algorithm to Reduce Bias in Estimating the Risk of Rehospitalization
    Creator:
    Stürmer, Til, Irwin, Debra, DiPrete, Bethany, Funk, Michele, Bloemers, Sarah, Gibson, Teresa, Pate, Virginia, Dasgupta, Nabarun, Pack, Kenneth, Lund, Jennifer, Young, Jessica, and Cooper, Toska
    Date of publication:
    August 24, 2022
    Abstract Tesim:
    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.
    Resource type:
    Poster
    Affiliation Label Tesim:
    Department of Epidemiology and Injury Prevention Research Center
    Conference Name:
    ICPE 2022: Advancing Pharmacoepidemiology and Real-World Evidence for the Global Community
    Type:
    http://purl.org/dc/dcmitype/Text
    DOI:
    https://doi.org/10.17615/1xbh-es77
    Language Label:
    English
    ORCID:
    Other Affiliation:
    and IBM Watson Health
    Person:
    Stürmer, Til, Irwin, Debra, DiPrete, Bethany, Funk, Michele, Bloemers, Sarah, Gibson, Teresa, Pate, Virginia, Dasgupta, Nabarun, Pack, Kenneth, Lund, Jennifer, Young, Jessica, and Cooper, Toska
    Rights Statement Label:
    In Copyright

    4. Patterns of Buprenorphine Initiation Treatment for Opioid Use Disorder and Association with Opioid-related Overdose Deaths

    Title Tesim:
    Patterns of Buprenorphine Initiation Treatment for Opioid Use Disorder and Association with Opioid-related Overdose Deaths
    Creator:
    Hammerslag, Lindsey, Slavova, Svetla, Freeman, Patricia R, DiPrete, Bethany , Slade, Emily, Lofwall, Michelle, Lei, Feitong, Dasgupta, Nabarun, and Moga, Daniela
    Date of publication:
    August 24, 2022
    Abstract Tesim:
    Oral presentation at the 38th International Society for Pharmacoepidemiology
    Resource type:
    Presentation
    Affiliation Label Tesim:
    Gillings School of Global Public Health and Injury Prevention Research Center
    Conference Name:
    38th International Conference on Pharmacoepidemiology [ICPE]
    Type:
    http://purl.org/dc/dcmitype/Text
    DOI:
    https://doi.org/10.17615/agje-sb21
    Language Label:
    English
    ORCID:
    Other Affiliation:
    University of Kentucky and
    Person:
    Hammerslag, Lindsey, Slavova, Svetla, Freeman, Patricia R, DiPrete, Bethany , Slade, Emily, Lofwall, Michelle, Lei, Feitong, Dasgupta, Nabarun, and Moga, Daniela
    Rights Statement Label:
    In Copyright

    5. Pharmacist's Experience Dispensing Abuse Deterrent Formulation Opioids: A Multistate Survey of Dispensing Pharmacists

    Title Tesim:
    Pharmacist's Experience Dispensing Abuse Deterrent Formulation Opioids: A Multistate Survey of Dispensing Pharmacists
    Creator:
    Slavova, Svetla, Miracle, Dustin K, Freeman, Patricia R, Dasgupta, Nabarun, Brown, John, and Harris, Sarah
    Date of publication:
    March 18, 2022
    Abstract Tesim:
    Poster presented at American Pharmacists Association 2022 Annual Meeting & Exposition (APhA2022)
    Resource type:
    Poster
    Affiliation Label Tesim:
    Injury Prevention Research Center
    Conference Name:
    American Pharmacists Association 2022 Annual Meeting & Exposition (APhA2022)
    Type:
    http://purl.org/dc/dcmitype/Text
    DOI:
    https://doi.org/10.17615/42sh-xc33
    Language Label:
    English
    ORCID:
    Other Affiliation:
    University of Kentucky and
    Person:
    Slavova, Svetla, Miracle, Dustin K, Freeman, Patricia R, Dasgupta, Nabarun, Brown, John, and Harris, Sarah
    Rights Statement Label:
    In Copyright

    6. ACCURACY AND VALIDITY OF REPORTED OPIOID PRESCRIPTION DAYS’ SUPPLY

    Title Tesim:
    ACCURACY AND VALIDITY OF REPORTED OPIOID PRESCRIPTION DAYS’ SUPPLY
    Creator:
    Dasgupta, Nabarun, Freeman, Patricia R, Miracle, Dustin K, Harris, Sarah, Brown, John R, and Slavova, Svetla
    Date of publication:
    August 24, 2022
    Abstract Tesim:
    Poster presented at the 38th International Conference for Pharmacoepidemiology [ICPE]
    Resource type:
    Poster
    Affiliation Label Tesim:
    Injury Prevention Research Center
    Conference Name:
    38th International Conference on Pharmacoepidemiology [ICPE]
    Type:
    http://purl.org/dc/dcmitype/Text
    DOI:
    https://doi.org/10.17615/nrjd-q233
    Language Label:
    English
    ORCID:
    Other Affiliation:
    and University of Kentucky
    Person:
    Dasgupta, Nabarun, Freeman, Patricia R, Miracle, Dustin K, Harris, Sarah, Brown, John R, and Slavova, Svetla
    Rights Statement Label:
    In Copyright

    7. Intended and unintended consequences: Changes in opioid prescribing practices following two policies in North Carolina, 2012–2018 – A controlled interrupted time series analysis

    Title Tesim:
    Intended and unintended consequences: Changes in opioid prescribing practices following two policies in North Carolina, 2012–2018 – A controlled interrupted time series analysis
    Creator:
    Fulcher, Naoko, Ives, Timothy J, Ranapurwala, Shabbar I, Maierhofer, Courtney N, Go, Vivian F, Pence, Brian W, Ringwalt, Christopher L, DiPrete, Bethany L, Dasgupta, Nabarun, and Chelminski, Paul R
    Date of publication:
    August 24, 2022
    Abstract Tesim:
    Poster presented at the 38th International Conference on Pharmacoepidemiology & Therapeutic Risk Management. Objective: To understand the extent to which unintended prescribing consequences followed implementation of two statewide opioid prescribing policies among privately insured, opioid-naïve individuals in North Carolina between 2012 and 2018.
    Resource type:
    Poster
    Affiliation Label Tesim:
    Injury Prevention Research Center, School of Medicine, Department of Epidemiology, Gillings School of Global Public Health, and Department of Health Behavior
    Conference Name:
    38th International Conference on Pharmacoepidemiology [ICPE]
    Type:
    http://purl.org/dc/dcmitype/Text
    DOI:
    https://doi.org/10.17615/v0x7-j031
    Language Label:
    English
    ORCID:
    Other Affiliation:
    Person:
    Fulcher, Naoko, Ives, Timothy J, Ranapurwala, Shabbar I, Maierhofer, Courtney N, Go, Vivian F, Pence, Brian W, Ringwalt, Christopher L, DiPrete, Bethany L, Dasgupta, Nabarun, and Chelminski, Paul R
    Rights Statement Label:
    In Copyright

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

    Title Tesim:
    External validation of a machine learning algorithm to distinguish death from disenrollment in claims data
    Creator:
    Pack, Kenneth, Pence, Brian W, Dasgupta, Nabarun, Bloemers, Sarah, Diprete, Bethany L., Young, Jessica C, Cooper, Toska, Irwin, Debra E, and Gibson, Teresa
    Date of publication:
    August 24, 2022
    Abstract Tesim:
    Poster presentation from the 38th International Conference on Pharmacoepidemiology & Therapeutic Risk Management
    Resource type:
    Poster
    Affiliation Label Tesim:
    Department of Epidemiology, Injury Prevention Research Center, Gillings School of Global Public Health, and Cecil G. Sheps Center for Health Services Research
    Conference Name:
    38th International Conference on Pharmacoepidemiology & Therapeutic Risk Management
    Type:
    http://purl.org/dc/dcmitype/Text
    DOI:
    https://doi.org/10.17615/6a5a-by77
    Language Label:
    English
    ORCID:
    Other Affiliation:
    IBM Watson Health and
    Person:
    Pack, Kenneth, Pence, Brian W, Dasgupta, Nabarun, Bloemers, Sarah, Diprete, Bethany L., Young, Jessica C, Cooper, Toska, Irwin, Debra E, and Gibson, Teresa
    Rights Statement Label:
    In Copyright

    9. Association of opioid dose reduction with opioid overdose and opioid use disorder among patients on high-dose long-term opioid therapy in North Carolina

    Title Tesim:
    Association of opioid dose reduction with opioid overdose and opioid use disorder among patients on high-dose long-term opioid therapy in North Carolina
    Creator:
    Chekminski, Paul R, Fulcher, Naoko, Maierhofer, Courtney N, Ranapurwala, Shabbar I, Go, Vivian F, Pence, Brian W, Ives, Timothy J, Diprete, Bethany L., and Dasgupta, Nabarun
    Date of publication:
    August 27, 2022
    Abstract Tesim:
    Presentation at the 38th International Conference on Pharmacoepidemiology [ICPE]. Objective: to characterize the association between rapid reduction or abrupt discontinuation of opioid therapy* and incidence of opioid overdose and OUD among patients receiving high-dose long-term opioid therapy (HDLTOT). [* vs maintained or gradual reduction]
    Resource type:
    Presentation
    Affiliation Label Tesim:
    School of Medicine, Injury Prevention Research Center, Gillings School of Global Public Health, Department of Epidemiology, and Department of Health Behavior
    Conference Name:
    38th International Conference on Pharmacoepidemiology [ICPE]
    Type:
    http://purl.org/dc/dcmitype/Text
    DOI:
    https://doi.org/10.17615/77gz-he02
    Language Label:
    English
    ORCID:
    Other Affiliation:
    Person:
    Chekminski, Paul R, Fulcher, Naoko, Maierhofer, Courtney N, Ranapurwala, Shabbar I, Go, Vivian F, Pence, Brian W, Ives, Timothy J, Diprete, Bethany L., and Dasgupta, Nabarun
    Rights Statement Label:
    In Copyright

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

    Title Tesim:
    An Algorithm to Predict Out-of-Hospital Death Using Insurance Claims Data
    Creator:
    Cooper, Toska, Yoon, Frank, Dasgupta, Nabarun, Pack, Kenneth, Gibson, Teresa, Irwin, Debra, Young, Jessica, DiPrete, Bethany, and Bloemers, Sarah
    Date of publication:
    April 26, 2022
    Abstract Tesim:
    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.
    Resource type:
    Presentation
    Affiliation Label Tesim:
    Injury Prevention Research Center and Gillings School of Global Public Health
    Type:
    http://purl.org/dc/dcmitype/Text
    DOI:
    https://doi.org/10.17615/2rp6-hv24
    Identifier:
    https://doi.org/10.17615/wkr7-fy11
    Keyword:
    epidemiology, mortality, machine learning, claims data, pharmacoepidemiology, IBM MarketScan, and death
    Language Label:
    English
    ORCID:
    , https://orcid.org/0000-0002-4098-605X, and https://orcid.org/0000-0003-2655-192X
    Other Affiliation:
    and IBM Watson Health
    Person:
    Cooper, Toska, Yoon, Frank, Dasgupta, Nabarun, Pack, Kenneth, Gibson, Teresa, Irwin, Debra, Young, Jessica, DiPrete, Bethany, and Bloemers, Sarah
    Rights Statement Label:
    In Copyright