Use of Linked Databases for Improved Confounding Control: Considerations for Potential Selection Bias

Combining meta-analysis with multiple imputation for one-step, privacy-protecting estimation of causal treatment effects in multi-site studies

Missing data complicates statistical analyses in multi-site studies, especially when it is not feasible to centrally pool individual-level data across sites. We combined meta-analysis with within-site multiple imputation for one-step estimation of the average causal effect (ACE) of a target population comprised of all individuals from all data-contributing sites within a multi-site distributed data network, without the need for sharing individual-level data to handle missing data. We considered two orders of combination and three choices of weights for meta-analysis, resulting in six approaches. The first three approaches, denoted as RR + metaF, RR + metaR and RR + std, first combined results from imputed data sets within each site using Rubin's rules and then meta-analyzed the combined results across sites using fixed-effect, random-effects and sample-standardization weights, respectively. The last three approaches, denoted as metaF + RR, metaR + RR and std + RR, first meta-analyzed results across sites separately for each imputation and then combined the meta-analysis results using Rubin's rules. Simulation results confirmed very good performance of RR + std and std + RR under various missing completely at random and missing at random settings. A direct application of the inverse-variance weighted meta-analysis based on site-specific ACEs can lead to biased results for the targeted network-wide ACE in the presence of treatment effect heterogeneity by site, demonstrating the need to clearly specify the target population and estimand and properly account for potential site heterogeneity in meta-analyses seeking to draw causal interpretations. An illustration using a large administrative claims database is presented.

Recurrent Atherosclerotic Cardiovascular Disease Events Potentially Prevented with Guideline-Recommended Cholesterol-Lowering Therapy following Myocardial Infarction

PURPOSE

Many adults with atherosclerotic cardiovascular disease (ASCVD) who are recommended to take a statin, ezetimibe and/or a proprotein convertase subtilisin/kexin type 9 inhibitor (PCSK9i) by the 2018 American Heart Association/American College of Cardiology cholesterol guideline do not receive these medications. We estimated the percentage of recurrent ASCVD events potentially prevented with guideline-recommended cholesterol-lowering therapy following a myocardial infarction (MI) hospitalization.

METHODS

We conducted simulations using data from US adults with government health insurance through Medicare or commercial health insurance in the MarketScan database. We used data from patients with an MI hospitalization in 2018-2019 to estimate the percentage receiving guideline-recommended therapy. We used data from patients with an MI hospitalization in 2013-2016 to estimate the 3-year cumulative incidence of recurrent ASCVD events (i.e., MI, coronary revascularization or ischemic stroke). The low-density lipoprotein cholesterol (LDL-C) reduction with guideline-recommended therapy was derived from trials of statins, ezetimibe and PCSK9i, and the associated ASCVD risk reduction was estimated from a meta-analysis by the Cholesterol-Lowering Treatment Trialists Collaboration.

RESULTS

Among 279,395 patients with an MI hospitalization in 2018-2019 (mean age 75 years, mean LDL-C 92 mg/dL), 27.3% were receiving guideline-recommended cholesterol-lowering therapy. With current cholesterol-lowering therapy use, 25.3% (95%CI: 25.2%-25.4%) of patients had an ASCVD event over 3 years. If all patients were to receive guideline-recommended therapy, 19.8% (95%CI: 19.5%-19.9%) were estimated to have an ASCVD event over 3 years, representing a 21.6% (95%CI: 20.5%-23.6%) relative risk reduction.

CONCLUSION

Implementation of guideline-recommended cholesterol-lowering therapy could prevent a substantial percentage of recurrent ASCVD events.

Driving Time to the Nearest Percutaneous Coronary Intervention-Capable Hospital and the Risk of Case Fatality in Patients with Acute Myocardial Infarction in Beijing

Timely arrival at a hospital capable of percutaneous coronary intervention (PCI) is critical in treating acute myocardial infarction (AMI). We examined the association between driving time to the nearest PCI-capable hospital and case fatality among AMI patients. A total of 142,474 AMI events during 2013-2019 from the Beijing Cardiovascular Disease Surveillance System were included in this cross-sectional study. The driving time from the residential address to the nearest PCI-capable hospital was calculated. Logistic regression was used to estimate the risk of AMI death associated with driving time. In 2019, 54.5% of patients lived within a 15-min drive to a PCI-capable hospital, with a higher proportion in urban than peri-urban areas (71.2% vs. 31.8%, p < 0.001). Compared with patients who had driving times ≤15 min, the adjusted odds ratios (95% CI, p value) for AMI fatality risk associated with driving times 16-30, 31-45, and >45 min were 1.068 (95% CI 1.033-1.104, p < 0.001), 1.189 (95% CI 1.127-1.255, p < 0.001), and 1.436 (95% CI 1.334-1.544, p < 0.001), respectively. Despite the high accessibility to PCI-capable hospitals for AMI patients in Beijing, inequality between urban and peri-urban areas exists. A longer driving time is associated with an elevated AMI fatality risk. These findings may help guide the allocation of health resources.

Continuity and Completeness of Electronic Health Record Data for Patients Treated With Oral Hypoglycemic Agents: Findings From Healthcare Delivery Systems in Taiwan

Objective: To evaluate the continuity and completeness of electronic health record (EHR) data, and the concordance of select clinical outcomes and baseline comorbidities between EHR and linked claims data, from three healthcare delivery systems in Taiwan.

Methods: We identified oral hypoglycemic agent (OHA) users from the Integrated Medical Database of National Taiwan University Hospital (NTUH-iMD), which was linked to the National Health Insurance Research Database (NHIRD), from June 2011 to December 2016. A secondary evaluation involved two additional EHR databases. We created consecutive 90-day periods before and after the first recorded OHA prescription and defined patients as having continuous EHR data if there was at least one encounter or prescription in a 90-day interval. EHR data completeness was measured by dividing the number of encounters in the NTUH-iMD by the number of encounters in the NHIRD. We assessed the concordance between EHR and claims data on three clinical outcomes (cardiovascular events, nephropathy-related events, and heart failure admission). We used individual comorbidities that comprised the Charlson comorbidity index to examine the concordance of select baseline comorbidities between EHRs and claims.

Results: We identified 39,268 OHA users in the NTUH-iMD. Thirty-one percent (n = 12,296) of these users contributed to the analysis that examined data continuity during the 6-month baseline and 24-month follow-up period; 31% (n = 3,845) of the 12,296 users had continuous data during this 30-month period and EHR data completeness was 52%. The concordance of major cardiovascular events, nephropathy-related events, and heart failure admission was moderate, with the NTU-iMD capturing 49–55% of the outcome events recorded in the NHIRD. The concordance of comorbidities was considerably different between the NTUH-iMD and NHIRD, with an absolute standardized difference >0.1 for most comorbidities examined. Across the three EHR databases studied, 29–55% of the OHA users had continuous records during the 6-month baseline and 24-month follow-up period.

Conclusion: EHR data continuity and data completeness may be suboptimal. A thorough evaluation of data continuity and completeness is recommended before conducting clinical and translational research using EHR data in Taiwan.