Impact of High-Deductible Health Plans on Emergency Department Patients With Nonspecific Chest Pain and Their Subsequent Care

Enrollment in High-Deductible Health Plans and Incident Diabetes Complications

Importance

Preventing diabetes complications requires monitoring and control of hyperglycemia and cardiovascular risk factors. Switching to high-deductible health plans (HDHPs) has been shown to hinder aspects of diabetes care; however, the association of HDHP enrollment with microvascular and macrovascular diabetes complications is unknown.

Objective

To examine the association between an employer-required switch to an HDHP and incident complications of diabetes.

Design, Setting, and Participants

This retrospective cohort study used deidentified administrative claims data for US adults with diabetes enrolled in employer-sponsored health plans between January 1, 2010, and December 31, 2019. Data analysis was performed from May 26, 2022, to January 2, 2024.

Exposures

Adults with a baseline year of non-HDHP enrollment who had to switch to an HDHP because their employer offered no non-HDHP alternative in that year were compared with adults who were continuously enrolled in a non-HDHP.

Main Outcomes and Measures

Mixed-effects logistic regression models examined the association between switching to an HDHP and, individually, the odds of myocardial infarction, stroke, hospitalization for heart failure, lower-extremity complication, end-stage kidney disease, proliferative retinopathy, treatment for retinopathy, and blindness. Models were adjusted for demographics, comorbidities, and medications, with inverse propensity score weighting used to account for potential selection bias.

Results

The study included 42 326 adults who switched to an HDHP (mean [SD] age, 52 [10] years; 19 752 [46.7%] female) and 202 729 adults who did not switch (mean [SD] age, 53 [10] years; 89 828 [44.3%] female). Those who switched to an HDHP had greater odds of experiencing all diabetes complications (odds ratio [OR], 1.11; 95% CI, 1.06-1.16 for myocardial infarction; OR, 1.15; 95% CI, 1.09-1.21 for stroke; OR, 1.35; 95% CI, 1.30-1.41 for hospitalization for heart failure; OR, 2.53; 95% CI, 2.38-2.70 for end-stage kidney disease; OR, 2.23; 95% CI, 2.17-2.29 for lower-extremity complication; OR, 1.17; 95% CI, 1.13-1.21 for proliferative retinopathy; OR, 2.35; 95% CI, 2.18-2.54 for blindness; and OR, 2.28; 95% CI, 2.15-2.41 for retinopathy treatment).

Conclusions and Relevance

This study found that an employer-driven switch to an HDHP was associated with increased odds of experiencing all diabetes complications. These findings reinforce the potential harm associated with HDHPs for people with diabetes and the importance of affordable and accessible chronic disease management, which is hindered by high out-of-pocket costs incurred by HDHPs.

How to Use Quasi-Experimental Methods in Cardiovascular Research: A Review of Current Practice

BACKGROUND

Quasi-experimental methods (QEMs) are a family of techniques used to estimate causal relationships when randomized controlled trials are unfeasible or unethical. They offer a powerful alternative to observational studies by introducing random assignment of individuals or groups into their design, thereby offering stronger means of establishing causation. The use of QEMs in cardiovascular research has not been systematically examined to determine steps toward improving and expanding their use.

METHODS

We identified 4 main techniques using a systematic search strategy from 2016 to 2021: instrumental variable analysis, interrupted time series analysis, difference-in-differences analysis, and regression discontinuity designs. QEMs are examined as alternatives to randomized controlled trials and traditional observational studies; as more observational data becomes available to researchers, there are more opportunities to apply these techniques. Eligible articles were selected based on publication in high-ranked journals. The quality of eligible articles was appraised using the Joanna Briggs Institute checklist for quasi-experimental studies.

RESULTS

Data from 380 studies were extracted based on our inclusion criteria. Forty-two of these studies were published in the top 10 medical or top 20 cardiovascular disease journals, and 25 studies were included after quality appraisal. The review identifies the main features and limitations associated with each technique, providing readers with practical guidance on how to apply these to their research. A graphical decision aid was developed to facilitate the routine use of QEMs.

CONCLUSIONS

The use of QEMs in cardiovascular research published in contemporary, high-impact articles was examined. Findings are biased toward this segment of literature, which represents the latest developments in this growing area of cardiovascular research. The decision aid is a novel schematic that researchers can adopt into practice.

The Effects of High Deductible Health Plans on Breast Cancer Treatment and Reconstruction

BACKGROUND

High-deductible health plans (HDHP) have expanded rapidly creating the potential for substantially increased out-of-pocket (OOP) costs. The associated financial strain has been associated with the decision to forego care, but the impact on patients undergoing breast cancer reconstruction is not known. We examined the impact of HDHPs vs. LDHPs and OOP maximums on breast reconstruction.

METHODS

Between January 2014 and 2020, patients who had breast reconstruction by the 2 senior authors were retrospectively evaluated. Information on patient's insurance contract was collected. Criteria for HDHP and LDHP were defined following section 223(c)(2)(A) of the Internal Revenue Code. All aspects of cancer diagnosis, cancer treatment, and surgical procedures were reviewed.

RESULTS

About 507 patients (262 in LDHPs and 245 in HDHPs) were reviewed. Patients treated with neoadjuvant chemotherapy were more likely to be enrolled in HDHPs (25.7% vs. 36.8%, P < .01). There was no significant difference in total operations, number of revisions, or length of reconstruction in days or calendar years. Additionally, no difference existed in the choice of autologous implant reconstruction.

CONCLUSION

The cost-sharing burden of HDHPs creates the potential for patients to forego care, and thus, effort should be directed toward increasing patient education concerning health plan benefits. Utilization of postdeductible spending, as well as resources of health savings accounts, may limit the adverse effects of HDHPs. This study also emphasizes the importance for providers to increase cost transparency.

Association of High-Deductible Health Plans With Health Care Use and Costs for Patients With Cardiovascular Disease

Background By increasing cost sharing, high-deductible health plans (HDHPs) aim to reduce low-value health care use. The association of HDHPs with health care use and costs in patients with chronic cardiovascular disease is unknown. Methods and Results This longitudinal cohort study analyzed 57 690 privately insured patients, aged 18 to 64 years, from a large commercial claims database with chronic cardiovascular disease from 2011 to 2019. Health care entities in which all or most beneficiaries switched from being in a traditional plan to an HDHP were identified. A difference-in-differences design was used to account for differences between individuals who remained in traditional plans and those who switched to HDHPs and to assess changes in health care use and costs. Among the 934 individuals in the HDHP group and the 56 756 in the traditional plan group, switching to an HDHP was not associated with statistically significant changes in annual outpatient visits, hospitalizations, or emergency department visits (-8.3% [95% CI, -16.8 to 1.1], -28.5% [95% CI, -62.1 to 34.6], and 11.2% [95% CI, -20.9 to 56.5], respectively). Switching to an HDHP was associated with an increase of $921 (95% CI, $743-$1099) in out-of-pocket costs but no statistically significant difference in total health care costs. Conclusions Among commercially insured patients with chronic cardiovascular disease, switching to an HDHP was not associated with a change in health care use but was associated with an increase in out-of-pocket costs. Although health care use by individuals with chronic cardiovascular disease may not be sensitive to higher cost sharing associated with HDHP enrollment, there may be a significant increase in patients' financial burden.

Transfer learning enables prediction of myocardial injury from continuous single-lead electrocardiography

OBJECTIVE

Chest pain is common, and current risk-stratification methods, requiring 12-lead electrocardiograms (ECGs) and serial biomarker assays, are static and restricted to highly resourced settings. Our objective was to predict myocardial injury using continuous single-lead ECG waveforms similar to those obtained from wearable devices and to evaluate the potential of transfer learning from labeled 12-lead ECGs to improve these predictions.

METHODS

We studied 10 874 Emergency Department (ED) patients who received continuous ECG monitoring and troponin testing from 2020 to 2021. We defined myocardial injury as newly elevated troponin in patients with chest pain or shortness of breath. We developed deep learning models of myocardial injury using continuous lead II ECG from bedside monitors as well as conventional 12-lead ECGs from triage. We pretrained single-lead models on a pre-existing corpus of labeled 12-lead ECGs. We compared model predictions to those of ED physicians.

RESULTS

A transfer learning strategy, whereby models for continuous single-lead ECGs were first pretrained on 12-lead ECGs from a separate cohort, predicted myocardial injury as accurately as models using patients' own 12-lead ECGs: area under the receiver operating characteristic curve 0.760 (95% confidence interval [CI], 0.721-0.799) and area under the precision-recall curve 0.321 (95% CI, 0.251-0.397). Models demonstrated a high negative predictive value for myocardial injury among patients with chest pain or shortness of breath, exceeding the predictive performance of ED physicians, while attending to known stigmata of myocardial injury.

CONCLUSIONS

Deep learning models pretrained on labeled 12-lead ECGs can predict myocardial injury from noisy, continuous monitor data early in a patient's presentation. The utility of continuous single-lead ECG in the risk stratification of chest pain has implications for wearable devices and preclinical settings, where external validation of the approach is needed.