Record Linkage Approaches Using Prescription Drug Monitoring Program and Mortality Data for Public Health Analyses and Epidemiologic Studies

Linking Trauma Registry Patients With Insurance Claims: Creating a Longitudinal Patient Record

INTRODUCTION

Trauma registries and their quality improvement programs only collect data from the acute hospital admission, and no additional information is captured once the patient is discharged. This lack of long-term data limits these programs' ability to affect change. The goal of this study was to create a longitudinal patient record by linking trauma registry data with third party payer claims data to allow the tracking of these patients after discharge.

METHODS

Trauma quality collaborative data (2018-2019) was utilized. Inclusion criteria were patients age ≥18, ISS ≥5 and a length of stay ≥1 d. In-hospital deaths were excluded. A deterministic match was performed with insurance claims records based on the hospital name, date of birth, sex, and dates of service (±1 d). The effect of payer type, ZIP code, International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis specificity and exact dates of service on the match rate was analyzed.

RESULTS

The overall match rate between these two patient record sources was 27.5%. There was a significantly higher match rate (42.8% versus 6.1%, P < 0.001) for patients with a payer that was contained in the insurance collaborative. In a subanalysis, exact dates of service did not substantially affect this match rate; however, specific International Classification of Diseases, Tenth Revision, Clinical Modification codes (i.e., all 7 characters) reduced this rate by almost half.

CONCLUSIONS

We demonstrated the successful linkage of patient records in a trauma registry with their insurance claims. This will allow us to the collect longitudinal information so that we can follow these patients' long-term outcomes and subsequently improve their care.

Overdose and mortality risk following a non-fatal opioid overdose treated by Emergency Medical Services in King County, Washington

BACKGROUND

Emergency Medical Services (EMS) agencies respond to hundreds of thousands of acute overdose events each year. We conducted a retrospective cohort study of EMS patients who survived a prior opioid overdose in 2019-2021 in King County, Washington.

METHODS

A novel record linkage algorithm was applied to EMS electronic health records and the state vital statistics registry to identify repeat overdoses and deaths that occurred up to 3 years following the index opioid overdose. We measured overdose incidence rates and applied survival analysis techniques to assess all-cause and overdose-specific mortality risks.

RESULTS

In the year following the index opioid overdose, the overdose (fatal or non-fatal) incidence rate was 23.3 per 100 person-year, overdose mortality rate was 2.7 per 100 person-year, and all-cause mortality rate was 5.2 per 100 person-year in this cohort of overdose survivors (n=4234). Overdose incidence was highest in the first 30 days following the index overdose (43 opioid overdoses and 4 fatal overdoses per 1000 person-months), declined precipitously, and then plateaued from the third month onwards (10-15 opioid overdoses and 1-2 fatal overdoses per 1000 person-months). Overdose incidence rates, measured at 30 days, were highest among overdose survivors who were young, male, and experienced a low severity index opioid overdose, but these differences diminished when measured at 12 months.

CONCLUSIONS

Among EMS patients who survived an opioid overdose, the risk of subsequent overdose is high, especially in the weeks following the index opioid overdose. Non-fatal overdose may represent a pivotal time to connect patients with harm-reduction, treatment, and other support services.

Virtual patient identifier (vPID): Improving patient traceability using anonymized identifiers in Japanese healthcare insurance claims database

Objective

Japan's national-level healthcare insurance claims database (NDB) is a collective database that contains the entire information on healthcare services being provided to all citizens. However, existing anonymized identifiers (ID1 and ID2) have a poor capability of tracing patients' claims in the database, hindering longitudinal analyses. This study presents a virtual patient identifier (vPID), which we have developed on top of these existing identifiers, to improve the patient traceability.

Methods

vPID is a new composite identifier that intensively consolidates ID1 and ID2 co-occurring in an identical claim to allow to collect claims of each patient even though its ID1 or ID2 may change due to life events or clerical errors. We conducted a verification test with prefecture-level datasets of healthcare insurance claims and enrollee history records, which allowed us to compare vPID with the ground truth, in terms of an identifiability score (indicating a capability of distinguishing a patient's claims from another patient's claims) and a traceability score (indicating a capability of collecting claims of an identical patient).

Results

The verification test has clarified that vPID offers significantly higher traceability scores (0.994, Mie; 0.997, Gifu) than ID1 (0.863, Mie; 0.884, Gifu) and ID2 (0.602, Mie; 0.839, Gifu), and comparable (0.996, Mie) and lower (0.979, Gifu) identifiability scores.

Discussion

vPID is seemingly useful for a wide spectrum of analytic studies unless they focus on sensitive cases to the design limitation of vPID, such as patients experiencing marriage and job change, simultaneously, and same-sex twin children.

Conclusion

vPID successfully improves patient traceability, providing an opportunity for longitudinal analyses that used to be practically impossible for NDB. Further exploration is also necessary, in particular, for mitigating identification errors.

A framework for a consistent and reproducible evaluation of manual review for patient matching algorithms

Healthcare systems are hampered by incomplete and fragmented patient health records. Record linkage is widely accepted as a solution to improve the quality and completeness of patient records. However, there does not exist a systematic approach for manually reviewing patient records to create gold standard record linkage data sets. We propose a robust framework for creating and evaluating manually reviewed gold standard data sets for measuring the performance of patient matching algorithms. Our 8-point approach covers data preprocessing, blocking, record adjudication, linkage evaluation, and reviewer characteristics. This framework can help record linkage method developers provide necessary transparency when creating and validating gold standard reference matching data sets. In turn, this transparency will support both the internal and external validity of recording linkage studies and improve the robustness of new record linkage strategies.

Data integration of National Dose Registry and survey data using multivariate imputation by chained equations

Introduction

Data integration is the process of merging information from multiple datasets generated from different sources, which can obtain more information in comparison to to one data source. All diagnostic medical radiation workers were enrolled in National Dose Registry (NDR) from 1996 to 2011, linked with mortality and cancer registry data. (https://kdca.go.kr/) Survey was conducted during 2012-2013 using self-reported questionnaire on occupational radiation practices among diagnostic medical radiation workers.

Methods

Data integration of NDR and Survey was performed using the multivariate imputation by chained equations (MICE) algorithm.

Results

The results were compared by sex and type of job because characteristics of target variables for imputation depend on these variables. There was a difference between the observed and pooled mean for the frequency of interventional therapy for nurses due to different type of medical facility distribution between observed and completed data. Concerning the marital status of males and females, and status of pregnancy for females, there was a difference between observed and pooled mean because the distribution of the year of birth was different between the observed and completed data. For lifetime status of smoking, the percentage of smoking experience was higher in the completed data than in the observed data, which could be due to reasons, such as underreporting among females and the distribution difference in the frequency of drinking between the observed and completed data for males.

Conclusion

Data integration can allow us to obtain survey information of NDR units without additional surveys, saving us time and costs for the survey.

Trends, patterns, and maternal characteristics of opioid prescribing during pregnancy in a large population-based cohort study

BACKGROUND

Opioid use during pregnancy has been associated with adverse maternal and infant health outcomes. Prescription drug monitoring programs (PDMP) provide a population-based source of prescription data. We linked statewide PDMP and birth certificate data in Tennessee (TN) to determine patterns of prescription opioid and benzodiazepine use during pregnancy.

METHODS

We constructed a cohort of 311,217 live singleton births from 2013 to 2016 with prescription history from 90 days before pregnancy to birth. Descriptive statistics were used to describe opioid prescription patterns during pregnancy overall, by maternal characteristics and by year. Multivariable logistic regression models estimated adjusted odds ratios and 95% confidence intervals for factors associated with prescription use.

RESULTS

The prevalence of prescription use during pregnancy was 14.1% for opioid analgesics, 1.6% buprenorphine for medication-assisted treatment, and 2.6% for benzodiazepines. The prevalence of opioid analgesic use decreased from 16.6% (2013) to 11.8% (2016) (ptrend< 0.001). About 25% used for > 7 and 9.7% for > 30 days' supply. The most common types were hydrocodone (9.3%), codeine (3.4%), and oxycodone (2.9%). In adjusted models, lower education, lower income, pre-pregnancy obesity and smoking during pregnancy were associated with increased odds of any opioid and opioid analgesic use.

CONCLUSION(S)

Despite the encouraging trend of decreasing use of prescription opioid analgesics, the overall prevalence remained close to 12% with many women using for long durations. Use was associated with lower socioeconomic status, obesity, and prenatal smoking. Findings highlight the need for maternal education and resources, and provider support for implementation of evidence-based care.

Optimization of Prescription Drug Monitoring Program to Overcome Opioid Epidemic in West Virginia

The development of the Prescription Drug Monitoring Program (PDMP) led to an innovation in the healthcare organization system (HCOs). The PDMP system has been utilized in different states at various organizational levels in an effort to achieve improved health outcomes, reduce the number of prescription drug overdoses, and lighten the economic burden that follows. However, during the implementation of PDMP, there were several barriers and limitations that were discovered. Those barriers impeded the process of utilization of PDMP, such as the complex user interface and lack of training for healthcare providers. The purpose of this paper was to examine the advances and limitations in the utilization and implementation of PDMP in the US healthcare industry and develop strategies for effective use of PDMP in West Virginia.

The qualitative part of this paper was a literature review. The paper referred to several peer-reviewed studies and research articles from several reliable resources, which were reached by databases or Google Scholar. A total of 44 articles were reviewed for this study.

The implementation of the PDMP was influenced by benefits and barriers. This article reviewed several studies in general that demonstrated positive outcomes from the implementation of PDMP, including a reduced number of prescription drug overdoses, coordinated care for patients, and improved health outcomes. However, the barriers and limitations were not neglected, which mainly include integration of PDMP into the electronic health record (EHR) system, lack of training for the providers, and lack of basic standards for the use of PDMP.

Although the new health reforms encouraged the adaption of PDMP among providers, data reporting and data interpretation still remain major concerns for assessing the health outcomes of PDMP implementation.

Ensemble learning to predict opioid-related overdose using statewide prescription drug monitoring program and hospital discharge data in the state of Tennessee

Objective

To develop and validate algorithms for predicting 30-day fatal and nonfatal opioid-related overdose using statewide data sources including prescription drug monitoring program data, Hospital Discharge Data System data, and Tennessee (TN) vital records. Current overdose prevention efforts in TN rely on descriptive and retrospective analyses without prognostication.

Materials and Methods

Study data included 3 041 668 TN patients with 71 479 191 controlled substance prescriptions from 2012 to 2017. Statewide data and socioeconomic indicators were used to train, ensemble, and calibrate 10 nonparametric “weak learner” models. Validation was performed using area under the receiver operating curve (AUROC), area under the precision recall curve, risk concentration, and Spiegelhalter z-test statistic.

Results

Within 30 days, 2574 fatal overdoses occurred after 4912 prescriptions (0.0069%) and 8455 nonfatal overdoses occurred after 19 460 prescriptions (0.027%). Discrimination and calibration improved after ensembling (AUROC: 0.79–0.83; Spiegelhalter P value: 0–.12). Risk concentration captured 47–52% of cases in the top quantiles of predicted probabilities.

Discussion

Partitioning and ensembling enabled all study data to be used given computational limits and helped mediate case imbalance. Predicting risk at the prescription level can aggregate risk to the patient, provider, pharmacy, county, and regional levels. Implementing these models into Tennessee Department of Health systems might enable more granular risk quantification. Prospective validation with more recent data is needed.

Conclusion

Predicting opioid-related overdose risk at statewide scales remains difficult and models like these, which required a partnership between an academic institution and state health agency to develop, may complement traditional epidemiological methods of risk identification and inform public health decisions.

Prescription Opioid Characteristics and Nonfatal Overdose Among Patients Discharged from Tennessee Emergency Departments

BACKGROUND

Despite increasing trends of nonfatal opioid overdoses in emergency departments (EDs), population-based studies comparing prescription opioid dosing patterns before and after nonfatal opioid overdoses are limited.

OBJECTIVES

To evaluate characteristics of prescribing behaviors before and after nonfatal overdoses, with a focus on opioid dosage.

METHODS

Included were 5,395 adult residents of Tennessee discharged from hospital EDs after a first nonfatal opioid overdose (2016-2017). Patients were linked to eligible prescription records in the Tennessee Controlled Substance Monitoring Database. We estimated odds ratios (OR) and 95% confidence intervals (CI) to evaluate characteristics associated with filling opioid prescriptions 90 days before overdose and with high daily dose (≥ 90 morphine milligram equivalents) 90 days after overdose.

RESULTS

Among patients who filled a prescription both before and after an overdose, the percentage filling a low, medium, and high dose was 33.7%, 31.9%, and 34.4%, respectively, after an opioid overdose (n = 1,516). Most high-dose users before an overdose (>70%) remained high-dose users with the same prescriber after the overdose. Male gender, ages ≥ 35 years, and medium metro residence were associated with increased odds of high-dose filling after an opioid overdose. Patients filling overlapping opioid-benzodiazepine prescriptions and with > 7 days' supply had increased odds of filling high dose after an opioid overdose (OR 1.4, 95% CI 1.08-1.70 and OR 3.7, 95% CI 2.28-5.84, respectively).

CONCLUSIONS

In Tennessee, many patients treated in the ED for an overdose are still prescribed high-dose opioid analgesics after an overdose, highlighting a missed opportunity for intervention and coordination of care between ED and non-ED providers.

Characteristics of fatal opioid overdoses with stimulant involvement in Tennessee: A descriptive study using 2018 State Unintentional Drug Overdose Reporting System Data

PURPOSE

Opioid overdose deaths involving stimulants are on the rise. Demographic characteristics for these deaths to be used in prevention efforts have not been established.

METHODS

We conducted a statewide retrospective study to evaluate the characteristics of fatal opioid overdoses with stimulant involvement using 2018 Tennessee State Unintentional Drug Overdose Reporting System data. Data sources included death certificates, autopsy reports, toxicology, and prescription drug monitoring program data. Frequencies were generated to compare demographics, circumstances, opioid history, death scene information, bystander intervention, and toxicology between fatal opioid overdoses with and without stimulant involvement.

RESULTS

A total of 1183 SUDORS opioid overdose deaths occurred in Tennessee in 2018 of which 434 (36.7%) involved a stimulant. Fatal opioid overdoses involving stimulants had higher frequencies of illicit drugs on toxicology specifically marijuana, fentanyl, and heroin compared to fatal opioid overdoses without stimulants. Fatal opioid overdoses involving stimulants had higher frequencies of scene indications of injection drug use compared to fatal opioid overdoses without stimulant involvement.

CONCLUSIONS

Fatal overdoses are shifting from mainly opioid to multidrug involvement and over one-third include use of stimulants. This analysis can help public health practitioners understand the circumstances around fatal opioid overdoses involving stimulants to inform tailored prevention strategies.

Sexually dimorphic neuroimmune response to chronic opioid treatment and withdrawal

Opioid use disorder is a leading cause of morbidity and mortality in the United States. Increasing pre-clinical and clinical evidence demonstrates sex differences in opioid use and dependence. However, the underlying molecular mechanisms contributing to these effects, including neuroinflammation, are still obscure. Therefore, in this study, we investigated the effect of oxycodone exposure and withdrawal on sex- and region-specific neuroimmune response. Real-time PCR and multiplex cytokine array analysis demonstrated elevated neuroinflammation with increased pro-inflammatory cytokine levels, and aberrant oligodendroglial response in reward neurocircuitry, following withdrawal from chronic oxycodone treatment. Chronic oxycodone and withdrawal treated male mice had lower mRNA expression of TMEM119 along with elevated protein levels of pro-inflammatory cytokines/chemokines and growth factors (IL-1β, IL-2, IL-7, IL-9, IL-12, IL-15, IL17, M-CSF, VEGF) in the prefrontal cortex (PFC) as compared to their female counterparts. In contrast, reduced levels of pro-inflammatory cytokines/chemokines (IL-1β, IL-6, IL-9, IL-12, CCL11) was observed in the nucleus accumbens (NAc) of oxycodone and withdrawal-treated males as compared to female mice. No treatment specific effects were observed on the mRNA expression of putative microglial activation markers (Iba1, CD68), but an overall sex specific decrease in the mRNA expression of Iba1 and CD68 was found in the PFC and NAc of male mice as compared to females. Moreover, a sex and region-specific increase in the mRNA levels of oligodendrocyte lineage markers (NG2, Sox10) was also observed in oxycodone and withdrawal treated animals. These findings may open a new avenue for the development of sex-specific precision therapeutics for opioid dependence by targeting region-specific neuroimmune signaling.

Advances in prescription drug monitoring program research: a literature synthesis (June 2018 to December 2019)

PURPOSE OF REVIEW

Nearly every U.S. state operates a prescription drug monitoring program (PDMP) to monitor dispensing of controlled substances. These programs are often considered key policy levers in the ongoing polydrug epidemic. Recent years have seen rapid growth of peer-reviewed literature examining PDMP consultation and the impacts of these programs on diverse patient populations and health outcomes. This literature synthesis presents a review of studies published from June 2018 to December 2019 and provides relevant updates from the perspective of three researchers in this field.

RECENT FINDINGS

The analyzed studies were primarily distributed across three overarching research focus areas: outcome evaluations (n = 29 studies), user surveys (n = 23), and surveillance (n = 22). Identified themes included growing awareness of the unintended consequences of PDMPs on access to opioids, effects on benzodiazepines and stimulant prescribing, challenges with workflow integration across multiple specialties, and new opportunities for applied data science.

SUMMARY

There is a critical gap in existing PDMP literature assessing how these programs have impacted psychiatrists, their prescribing behaviors, and their patients. Although PDMPs have improved population-level monitoring of controlled substances from medical sources, their role in responding to a drug epidemic shifting to illicitly manufactured drugs is under scrutiny.