One-shot distributed algorithms for addressing heterogeneity in competing risks data across clinical sites

OBJECTIVE

To characterize the interplay between multiple medical conditions across sites and account for the heterogeneity in patient population characteristics across sites within a distributed research network, we develop a one-shot algorithm that can efficiently utilize summary-level data from various institutions. By applying our proposed algorithm to a large pediatric cohort across four national Children's hospitals, we replicated a recently published prospective cohort, the RISK study, and quantified the impact of the risk factors associated with the penetrating or stricturing behaviors of pediatric Crohn's disease (PCD).

METHODS

In this study, we introduce the ODACoRH algorithm, a one-shot distributed algorithm designed for the competing risks model with heterogeneity. Our approach considers the variability in baseline hazard functions of multiple endpoints of interest across different sites. To accomplish this, we build a surrogate likelihood function by combining patient-level data from the local site with aggregated data from other external sites. We validated our method through extensive simulation studies and replication of the RISK study to investigate the impact of risk factors on the PCD for adolescents and children from four children's hospitals within the PEDSnet, A National Pediatric Learning Health System. To evaluate our ODACoRH algorithm, we compared results from the ODACoRH algorithms with those from meta-analysis as well as those derived from the pooled data.

RESULTS

The ODACoRH algorithm had the smallest relative bias to the gold standard method (-0.2%), outperforming the meta-analysis method (-11.4%). In the PCD association study, the estimated subdistribution hazard ratios obtained through the ODACoRH algorithms are identical on par with the results derived from pooled data, which demonstrates the high reliability of our federated learning algorithms. From a clinical standpoint, the identified risk factors for PCD align well with the RISK study published in the Lancet in 2017 and other published studies, supporting the validity of our findings.

CONCLUSION

With the ODACoRH algorithm, we demonstrate the capability of effectively integrating data from multiple sites in a decentralized data setting while accounting for between-site heterogeneity. Importantly, our study reveals several crucial clinical risk factors for PCD that merit further investigations.

Duration of medication treatment for opioid-use disorder and risk of overdose among Medicaid enrollees in 11 states: a retrospective cohort study

BACKGROUND AND AIMS

Medication for opioid use disorder (MOUD) reduces harms associated with opioid use disorder (OUD), including risk of overdose. Understanding how variation in MOUD duration influences overdose risk is important as health-care payers increasingly remove barriers to treatment continuation (e.g. prior authorization). This study measured the association between MOUD continuation, relative to discontinuation, and opioid-related overdose among Medicaid beneficiaries.

DESIGN

Retrospective cohort study using landmark survival analysis. We estimated the association between treatment continuation and overdose risk at 5 points after the index, or first, MOUD claim. Censoring events included death and disenrollment.

SETTING AND PARTICIPANTS

Medicaid programs in 11 US states: Delaware, Kentucky, Maryland, Maine, Michigan, North Carolina, Ohio, Pennsylvania, Virginia, West Virginia and Wisconsin. A total of 293 180 Medicaid beneficiaries aged 18-64 years with a diagnosis of OUD and had a first MOUD claim between 2016 and 2017.

MEASUREMENTS

MOUD formulations included methadone, buprenorphine and naltrexone. We measured medically treated opioid-related overdose within claims within 12 months of the index MOUD claim.

FINDINGS

Results were consistent across states. In pooled results, 5.1% of beneficiaries had an overdose, and 67% discontinued MOUD before an overdose or censoring event within 12 months. Beneficiaries who continued MOUD beyond 60 days had a lower relative overdose hazard ratio (HR) compared with those who discontinued by day 60 [HR = 0.39; 95% confidence interval (CI) = 0.36-0.42; P < 0.0001]. MOUD continuation was associated with lower overdose risk at 120 days (HR = 0.34; 95% CI = 0.31-0.37; P < 0.0001), 180 days (HR = 0.31; 95% CI = 0.29-0.34; P < 0.0001), 240 days (HR = 0.29; 95% CI = 0.26-0.31; P < 0.0001) and 300 days (HR = 0.28; 95% CI = 0.24-0.32; P < 0.0001). The hazard of overdose was 10% lower with each additional 60 days of MOUD (95% CI = 0.88-0.92; P < 0.0001).

CONCLUSIONS

Continuation of medication for opioid use disorder (MOUD) in US Medicaid beneficiaries was associated with a substantial reduction in overdose risk up to 12 months after the first claim for MOUD.

Factors affecting willingness to share electronic health data among California consumers

Background

Robust technology infrastructure is needed to enable learning health care systems to improve quality, access, and cost. Such infrastructure relies on the trust and confidence of individuals to share their health data for healthcare and research. Few studies have addressed consumers’ views on electronic data sharing and fewer still have explored the dual purposes of healthcare and research together. The objective of the study is to explore factors that affect consumers’ willingness to share electronic health information for healthcare and research.

Methods

This study involved a random-digit dial telephone survey of 800 adult Californians conducted in English and Spanish. Logistic regression was performed using backward selection to test for significant (p-value ≤ 0.05) associations of each explanatory variable with the outcome variable.

Results

The odds of consent for electronic data sharing for healthcare decreased as Likert scale ratings for EHR impact on privacy worsened, odds ratio (OR) = 0.74, 95% CI [0.60, 0.90]; security, OR = 0.80, 95% CI [0.66, 0.98]; and quality, OR = 0.59, 95% CI [0.46–0.75]. The odds of consent for sharing for research was greater for those who think EHR will improve research quality, OR = 11.26, 95% CI [4.13, 30.73]; those who value research benefit over privacy OR = 2.72, 95% CI [1.55, 4.78]; and those who value control over research benefit OR = 0.49, 95% CI [0.26, 0.94].

Conclusions

Consumers’ choices about electronically sharing health information are affected by their attitudes toward EHRs as well as beliefs about research benefit and individual control. Design of person-centered interventions utilizing electronically collected health information, and policies regarding data sharing should address these values of importance to people. Understanding of these perspectives is critical for leveraging health data to support learning health care systems.