Insurance Plans Research Articles

Analyzing single-action bias in dynamic climate change environments: insights from feedback and probability

“Single action bias” (SAB) characterizes individuals’ inclination to undertake only one preventive measure against climate change, disregarding potentially more effective alternatives. This bias poses a significant obstacle to comprehensive responses to climate change. While dynamic climate simulators have been developed to raise awareness of climate change and encourage pro-environmental behaviors, the prevalence of SAB within these tools remains unexplored. This study introduces the “Single Action Bias-Interactive Climate Change Simulator” (SAB-ICCS) to investigate SAB’s manifestation in dynamic scenarios. Utilizing the framework of the Interactive Climate Change Simulator (ICCS), known for its efficacy in fostering pro-environmental actions, the SAB-ICCS explores how feedback, probability, and their interplay influence SAB prevalence during climate mitigation and adaptation decision-making. A total of 160 participants were randomly assigned to four conditions in the SAB-ICCS, varying feedback presence and climate change probability. Participants engaged in climate mitigation and adaptation actions, simulating the repercussions of climate change through investment choices in climate mitigation and adaptation (consisting of three insurance plans). The study’s dependent variables were the participants’ actions towards climate mitigation and adaptation. Results revealed a substantial prevalence of single action proportion (42%) compared to other action proportions. Furthermore, the total monetary investment was significantly higher when taking optimal actions than when exhibiting SAB. Moreover, a higher probability of climate change resulted in a higher prevalence of SAB (49%) than a lower probability (35%). Interestingly, feedback availability did not significantly impact SAB prevalence. Though both feedback and the probability of climate change influenced how participants exhibited SAB, and the absolute monetary investment was also significantly affected. This research enhances our comprehension of SAB within educational climate simulations, which is vital for informing climate education and policymaking. It offers insights for policymakers and educators to develop interventions addressing SAB, enhancing climate action strategies by understanding probability and feedback influences.

Out-of-pocket cost modeling of adjuvant radiation therapy duration in standard-of-care treatment of glioblastoma across Medicaid and Medicare plans.

2045 Background: The optimal radiation treatment (RT) length (6 weeks of 60 Gy in 30 fractions versus 3 weeks of 40 Gy in 15 fractions) for older adults with glioblastoma is debatable, with Level 1 evidence (PMID 15051755) revealing no difference in survival between RT regimens. However, there remains a paucity of evidence describing the role of insurance on out-of-pocket (OOP) costs for patients undergoing varying lengths of RT. This project aims to quantify expenses by insurance plans, enhancing transparency in treatment cost understanding. Methods: We utilized the National Comprehensive Cancer Network guidelines to determine the standard treatment protocol, including 30-fraction or 15-fraction RT with concurrent and post-radiation temozolomide for 12 cycles. The model assumes a Medicare- and/or Medicaid-eligible patient ≥ 65 years of age with glioblastoma. The total aggregate out-of-pocket (OOP) costs were determined from the sum of treatment costs, deductibles, and copays/coinsurance based on Medicaid, Original Medicare, Medigap Plan G, and Medicare Part D Rx plan over a two-year time horizon (not adjusted for inflation). All procedures were assumed to take place within the premises of an Ohio hospital. Results: Treatment charges include neurosurgery, neuro-oncology, and radiation oncology initial consultation, follow-up visits, diagnostic head CT, MRI, craniotomy with maximum safe resection, surveillance brain MRI, temozolomide, antiemetics, prophylactic TMP-SMX, weekly CBC, and tumor treating fields. RT-specific treatment charges include treatment planning, simulation and verification, RT delivery, and on-treatment visits. Original Medicare beneficiaries face an OOP cost of 20% for Medicare Part B claims with no cost cap for approved procedures after the deductible. Medications are covered under Medicare Part D with copays. This results in a total OOP treatment charge of $4,454.01 for 30 fractions and $3,913.86 for 15 fractions. Medigap Plan G beneficiaries face a total OOP charge of $2,387.22 for 30 fractions and $2,369.61 for 15 fractions. For Medicaid beneficiaries (assuming all treatments are approved by Medicaid), all expenses are covered without limit, resulting in no OOP expense for either treatment plan. Conclusions: Three-week versus six-week RT for GBM reduces OOP costs facing patients by 10%. By understanding the financial implications, healthcare providers, policymakers, and patients can make informed decisions about treatment options, and healthcare systems can develop strategies to mitigate the economic burden associated with GBM care.

Assessment of the utilization of real-time prescription benefits for patient cost savings within an outpatient setting

BackgroundThis study evaluates the impact of Real-Time Prescription Benefits (RTPB), a tool integrated into electronic health records (EHRs), on patient out-of-pocket costs in an academic institution. RTPB provides prescribers with alternative, less expensive medications based on insurance plans. The primary measure was cost-savings, defined as the difference between the out-of-pocket cost of the prescribed medication and its alternative. MethodsA retrospective analysis of prescriptions from outpatient clinics in a university-based health system was conducted between May 2020 and July 2021. Prescriptions were analyzed at the 2nd level of the Anatomical Therapeutic Chemical (ATC) classification system. Costs were standardized to a 30-day supply. Standardized cost and total cost per prescription, and overall savings for the top 20 medication classes at the 2nd ATC level were calculated. The overall impact of RTPB was estimated based on selecting the least expensive alternative suggested by RTPB. ResultsThe study found that RTPB information was provided for 22% of prescriptions, with suggested alternatives for 1.26%. Among prescriptions with an alternative selected, the standardized average cost saving was $38.83. The study realized $15,416 in patient total cost savings. If the least expensive RTPB-suggested alternative were chosen for all prescriptions, an estimated $276,386 could have been saved. Psychoanaleptic and psycholeptic medications were the most prescribed with an alternative, with most savings in specialty drugs like anthelmintic and immunostimulant medications. ConclusionThe study highlights the importance of RTPB in reducing patient costs. It reports patient cost-savings with RTPB in prescribing decisions. Future research could explore the impact of RTPB on medication adherence using pharmacy claims data.

Impact of lung biomarker testing on out-of-pocket costs for metastatic non-small cell lung cancer.

11087 Background: Biomarker testing in metastatic non-small lung cancer (NSCLC) is critical for appropriate selection of therapy options. Claims-based datasets offer real-world information on the use and cost of biomarker testing across the United States. Methods: We used de-identified administrative claims data from Optum’s Clinformatics Data Mart Database (CDM) from 2013 to 2021 to assess for trends in biomarker testing. Eligible patients were adults with two or more lung cancer diagnosis codes and with two or more claims of a secondary malignant neoplasm. Patients were excluded if they had another primary cancer or did not have continuous insurance coverage twelve months prior and six months after diagnosis. We assessed claims-based out-of-pocket (OOP) costs associated with testing and treatment. Descriptive statistics were used to assess biomarker testing rates, and multivariable analyses (MVA) were performed to assess factors associated with testing. Results: A total of 4377 patients with metastatic NSCLC were eligible (mean age 60 years (SD: 8.33 years), 49.6% female, 76.7% former smokers). Testing rates within two months of diagnosis increased from 58.15% in 2013 to 69.96% in 2021. On MVA, biomarker testing was associated with younger age, being non-smokers, living in Mountain geographic region, or having point-of-service insurance plans. Biomarker testing was associated with a median OOP cost of $98 (IQR: $43.87-$306.58). Patients who underwent biomarker testing had a median total OOP cost of all services within 6 months of diagnosis of $3560.20 (IQR: $1538.37-$6199.44) compared to $1979.58 (IQR: $725.75-$4003.06) for those who did not undergo biomarker testing. Conclusions: Using claims data, we find that 70% of patients with metastatic NSCLC undergo biomarker testing within two months of diagnosis with metastatic disease. Most patients undergo biomarker testing early in their treatment course (0-60 days), suggesting that testing is appropriately being obtained early on in their treatment course, but this testing is associated with substantially higher overall OOP costs to patients.

Claims and cost responsibility trends for high-deductible insurance plan participants with breast and prostate cancer in the New Hampshire Limited Use Commercial Health Care Claims data.

e23148 Background: High-deductible health plans (HDHPs) are popular in the US due to their potential to curb rising healthcare costs and involve higher out-of-pocket costs for consumers which are associated with lower utilization of health services. Data on longitudinal trends for claims and cost responsibility for adult HDHP participants with cancer is scarce, which was the motivation for our study. Methods: We used multivariable Poisson regression models with the Huber/White/Sandwich linearized estimator of variance and predictive margins [reported as counts with 95% confidence intervals (CI)] to examine the effect of HDHP status on select stratified estimates of (a) the average number of claims per enrollee (claims), and (b) the average total participant responsibility per enrollee visit (cost in $; i.e., the sum of in-network and out-of-network values), for adult (age > / = 18 years) participants in the New Hampshire Limited Use Commercial Health Care Claims data, with breast and prostate cancer (BC/PC) diagnoses for the 10-year period 2010-19. Final models were fully adjusted for other plan types, year, age, and quintile of ecologic income, and our final dataset consisted of 945,281 claim records. Results: Overall, BC and PC HDHP participants had significantly fewer claims over the 10-year period [average counts: (BC: -242.2; -244.6, -239.8); (PC: -445.3; -450.3, -440.4)]. In addition, BC and PC HDHP participants had significantly lower participant responsibility over the 10-year period [average $: (BC: -85.4; -87.5, -83.2); (PC: -104.9; -115.5, -94.2)]. Overall trends for claims across the entire time period for HDHP vs. non-HDHP BC/PC participants were consistently lower, and flat trends were observed for BC from 2014-19 and for PC from 2012-19; however, there was steady increase in claims for both BC/PC non-HDHP participants across the same late periods. The trend for participant responsibility for BC HDHP participants was also lower compared with non-HDHP for all years except 2014; and after 2014 increases were seen for both BC HDHP and non-HDHP participants with the latter showing the steepest increases. The trend for participant responsibility for PC HDHP participants was lower compared with non-HDHP between 2010-17; but this relationship inverted for 2018-19 with HDHP participants showing the highest participant responsibility levels. Conclusions: Our findings show discordance in claims trends for HDHP vs. non-HDHP participants with BC/PC; durable trends of reduced claims are observed for HDHP participants, but for non-HDHP participants claim trends show steady increases in later periods.

Administrative burden of establishing health care coverage for the uninsured and its impact on timely access to care.

11050 Background: Although safety net coverage programs like Medicaid exist to fund health care for low-income patients, the administrative burden of the lengthy application limits uptake by eligible individuals. Therefore, many patients do not attempt to enroll until they become ill, which is common among uninsured adults diagnosed with cancer. The literature reports worse cancer outcomes for those with Medicaid, but reasons for this are unclear. The delays in enrollment for patients with uncertain coverage at the time of diagnosis – those in limbo – are understudied and may explain some of this persisting disparity. We estimated the patient and health system burdens for those with uncertain coverage status at the time of cancer diagnosis at Parkland Health, the main provider of cancer treatment for the uninsured in Dallas, Texas. Methods: We assembled the electronic health record financial encounter data of patients with an incident cancer diagnosis in the Parkland tumor registry, diagnosed from 2010 – 2022. We defined patients as being “in limbo” if they had self-pay, pending, or missing insurance plans at the date of diagnosis. We also included patients whose coverage verification date occurred after diagnosis date, since coverage programs can apply retroactively for 90 days. We used plan verification dates to identify when limbo status resolved. We used coarsened exact matching to balance across age, race, Spanish-language preference, Charlson, and screenable cancer type, then multivariate regression to compare patients that were in limbo to those that had established coverage before cancer diagnosis. Results: We identified 17,468 adults newly diagnosed with cancer, 80% of whom were < age 65, 55% female, 73% Hispanic and/or Black, and 30% Spanish-language preferring. The most common cancer types were GI/colorectal (19%), breast (17%), and lung (9%); 23% were advanced stage at diagnosis and 13% died within 180 days of diagnosis. 52.1% were already enrolled in Medicaid/charity care, but one-third (35%) were in limbo at diagnosis, which lasted for a median 31 days (IQR 13 – 126). Over half (55%) of patients in limbo resolved to Medicaid or charity care. Patients who experienced limbo had a median of 596 financial account notes in the first year after diagnosis, 121 more than the comparison group (95% CI 86.7 - 156.1) and began treatment 10 days later (95% CI 7.7 - 12.7). They also had a median of 1 ED visit in the year after diagnosis, 0.2 more than patients never in limbo (95% CI 0.11 - 0.26). Conclusions: The administrative burden of enrolling into coverage programs for the uninsured may impose direct and indirect costs on patients and safety-net health systems and may contribute to worse cancer care outcomes. A more comprehensive accounting of the effects of administrative burdens might suggest ways to redesign coverage enrollment for the uninsured to improve care and reduce costs.

Temporal Trends in Spinal Imaging in Ontario (2002-2019) and Manitoba (2001-2011), Canada.

Background Several studies have reported the overuse of spinal imaging, which, in Canada, led to several provincial pathways aimed at optimizing the use of imaging. We assessed temporal trends in spine imaging in two Canadian provinces. Methods We explored the use of X-ray, computed tomography (CT), and magnetic resonance imaging (MRI) examinations of the cervical, thoracic, and lumbar spine regions among adults in Ontario (April 1, 2002, to March 31, 2019) and in Manitoba, Canada (April 1, 2001, to March 31, 2011) using linked Ontario Health Insurance Plan administrative databases and data from Manitoba Health. We calculated the age- and sex-adjusted rates of spinal X-ray, CT, and MRI examinations by dividing the number of imaging studies by the population of each province for each year and estimated the use of each imaging modality per 100,000 persons. Results The total cost of spine imaging in Ontario increased from $45.8 million in 2002/03 to $70.3 million in 2018/19 (a 54% increase), and in Manitoba from $2.2 million in 2001/02 to $5 million in 2010/11 (a 127% increase). In Ontario, rates of spine X-rays decreased by 12% and spine CT scans decreased by 28% over this time period, while in Manitoba, rates of spine X-rays and CT scans remained constant. Age- and sex-adjusted utilization of spinal MRI scans per 100,000 persons markedly increased over time in both Ontario (277%) and Manitoba (350%). Conclusion Despite efforts to reduce the use of inappropriate spinal imaging, both Ontario and Manitoba have greatly increased utilization of spine MRI in the past two decades.

Trends in immediate sequential bilateral cataract surgery in Ontario

ObjectiveBecause of increased evidence for safety and increased demands, there appears to be a recent adoption and endorsement of immediate sequential bilateral cataract surgery (ISBCS). This study aims to determine whether a paradigm shift has occurred in the delivery of cataract surgery in the province of Ontario and its the extent, if any, and to analyze the current role of ISBCS. DesignRetrospective health records analysis. MethodsWe analyzed aggregate Ontario Health Insurance Plan claim counts for cataract surgeries between 2016 and 2022, categorizing cases into unilateral, ISBCS, and delayed sequential bilateral cataract surgery (DSBCS) cases. We examined trends, compared wait times for second surgeries in the DSBCS cohort with wait times for first surgeries, and used previously published cost estimates for ISBCS to estimate annual savings in Ontario. ResultsThere were 1,122 ISBCS cases in 2016, which increased sixfold during the pandemic (2020–2021) and further increased to 11,876 cases in 2022. Unilateral and DSBCS cases decreased during the pandemic but rebounded in 2022 to 102% and 153% of baseline, respectively. ISBCS increased from 1% to 10%, unilateral cases decreased from 41% to 29% of total cases, and DSBCS increased from 58% to 61%. Median wait for patients’ first and second surgeries increased from 65 to 87 days and 28 to 33 days, respectively. Using estimates, ISBCS saved approximately $19 million in 2022. ConclusionOur study demonstrates a paradigm shift in Ontario in favour of ISBCS. ISBCS also may be a strategy to reduce increasing wait times while saving health care system dollars.

Health insurance plans offered at NCI-designated cancer centers.

9052 Background: The health insurance landscape in the US represents an expensive and complex challenge to patients and physicians alike. Little is known about health insurance plans (HIPs) offered to physicians, particularly at NCI-designated cancer centers, which represent centers seeking to meet the highest standards. Methods: We collected data on the HIPs offered to physicians at NCI-designated cancer centers by reviewing institution websites from 11/2023 - 01/2024. We abstracted data on the insurance premium, deductible, out-of-pocket (OOP) maximum, and coinsurance for hospitalizations, limited to three HIPs per institution. We derived estimates using the lowest charge. We calculated inpatient stay costs based on standard hospital stay costs ($2,883 per day over an average of 4.5 days). We compared HIPs between public and private cancer centers. We used US Census Bureau population density data and regression models to explore associations among population density and OOP costs. Results: Among 65 NCI-designated cancer centers reviewed, 61 (93.8%) had readily available information about HIPs provided. Most institutions provided HIPs for all hospital employees, while 2 institutions only covered physicians. On average, each cancer center offered 3.67 HIPs, involving 1.67 insurers (54.1% offered a single insurer). Among 153 HIPs overall, 51.6% were preferred provider organization (PPOs) and one-third (33.3%) were health maintenance organization (HMOs). Only 22.9% of HIPs were high-deductible health plans (HDHP). The majority of cancer centers offered PPO plans (75.4%), HMO plans (57.4%), HDHP plans (55.7%), and some (24.6%) offered other plans, such as point of service and exclusive provider organization. Average costs for different kinds of HIPs are presented in the Table. A higher percentage of public cancer centers provided HMOs (60.5% vs. 50.0%, p=0.76) and PPOs (84.3% vs. 60.5%, p=0.09) than private centers, yet differences did not reach significance. Centers located in regions with higher state population density were more likely to have higher HMO OOP costs (beta=2.72; p=.006) and lower PPO OOP costs (beta=-2.98; p=.012). Conclusions: We found that most NCI-designated cancer centers had readily available information about HIPs provided to physicians. For these centers, physicians had limited HIP options offered, particularly in private institutions, which offered fewer HMO and PPO plans. We also demonstrated associations among state population density and OOP max in HMO and PPO plans. Our study provides a current synopsis of HIPs and identifies disparities among them, contributing to a foundation for future research. [Table: see text]

Document Similarity Analysis and Template Matching in Health Insurance Using Python Flask

In recent days, a primary challenge faced by healthcare insurance organizations is the reliance on a large number of files to process insurance claims and make coverage decisions. With the gradual increase in medical insurance in India, the number of people buying medical insurance plans is rising. Document processing plays a pivotal role in the efficient management of health insurance policies and claims. This proposed study presents an innovative approach to document template matching specifically tailored for the health insurance domain, implemented using the Python Flask web framework. Integration with Python Flask offers scalability, flexibility, and ease of deployment, making it suitable for a wide range of insurance applications. This solution represents a significant advancement in document processing technology, empowering health insurance professionals with a comprehensive tool to effectively manage and analyze documents.

The Second Comprehensive National Plan (2024-2028) for Sustainable National Health Insurance: The New Role and Challenges

The Second Comprehensive National Plan (2024-2028) for Sustainable National Health Insurance: The New Role and Challenges

Evaluation of a pharmacist-led personal continuous glucose monitor workflow to improve glycemic management in an internal medicine clinic

BackgroundThe use of personal continuous glucose monitors (CGMs) in patients with diabetes has increased substantially and is expected to continue to increase as CGMs become more affordable and insurance plans improve coverage. The utilization of CGMs has improved diabetes management and reduced hypoglycemic events. ObjectivesTo create pharmacist-led personal CGM workflow and evaluate its impact on glycemic management in patients with diabetes. Practice DescriptionThe study took place at an Internal Medicine Clinic. The practice providers include 2 medical doctors, 5 physician assistants, 2 nurse practitioners, and 1 clinical pharmacist. Practice InnovationTo create and implement a sustainable pharmacy led CGM workflow for enhanced CGM use within an internal medicine clinic. Evaluation MethodsThis was a prospective, investigator-initiated pilot study conducted at an Atrium Health Internal Medicine clinic over 28 weeks. In this pilot, 42 patients were qualifying candidates with diabetes and personal CGM use. In addition, 30 patients were followed until study completion and included into final analysis. ResultsThe average baseline A1c was reduced from 8.3% to 7.1% over a 3- to 6-month period. The pharmacist-led CGM workflow revealed a statistically significant reduction in A1c from baseline by an average of 1.2% (95% CI −0.6 to −1.8, P = 0.0006). On average, patients were enrolled for 19.9 weeks and had an average of 5 visits during this time. During the study duration, 100 medications changes were implemented under the existing clinical pharmacist practitioner agreement between the pharmacists and the provider. Overall, 58 Current Procedural Terminology 95251 codes were billed yielding $7052.00 in billed CGM services for the clinic. This project generated 40.6 provider relative value units. ConclusionThe utilization of a pharmacist-led personal CGM workflow can improve diabetes outcomes.

Study on Wellness Programs Offered by the Health Insurance Companies

Introduction: Wellness is the act of ensuring a healthy lifestyle regularly. It is a multidimensional phenomenon. There are wellness providers such as hospitals, insurance companies, diagnostics, and wellness firms. It focuses on the wellness programs offered by Indian and International health insurance companies. Method: Descriptive and cross-sectional study on the wellness programs delivered by health insurance companies. The survey was conducted on the outlook of customers for the wellness programs offered by insurance companies. Result: List of wellness programs offered by various health insurance companies. The determination of the interest of consumers in wellness programs and rewards related to it. Discussion: Wellness programs include fitness challenges, modifications in diet and lifestyle, goal-based exercises, and healthy living. This paper discusses wellness initiatives, benefits, and regulations of wellness programs for insurance companies. Application: A comprehensive wellness plan for health insurance companies to increase the well-being of their customers. Conclusion: Both the insured and the insurer get benefit from the wellness initiatives. Wellness programs keep individuals healthy and therefore reduce the possibility of future claims. This increases the revenue of insurance companies.

Oral Prednisone Use Following Primary Total Knee Arthroplasty Does Not Increase Infections, but can be Associated With Higher Risk for Medical and Surgical Complications

BackgroundPostoperative use of oral prednisone to augment the effect of multimodal pain regimens after total knee arthroplasty (TKA) has increased in popularity. However, data on the risks of its utilization, especially as it relates to infection, have been lacking. We tested the null hypothesis that perioperative prednisone use is not associated with the incidence of surgical and medical complications after TKA. MethodsUsing a national administrative claims database, we identified 949,555 patients undergoing primary TKA. We excluded patients who filled oral prednisone prescriptions within 90 days prior to surgery or between 90 and 364 days after surgery. Patients who had acute prednisone use were defined as those who filled prednisone prescriptions only within 30 days after surgery. Outcomes consisted of surgical and medical complications after TKA. Multivariable logistic regression models were used to evaluate the association between acute prednisone use and complications, adjusting for age, sex, region, insurance plan, and Elixhauser comorbidities. ResultsPatients in the acute prednisone cohort had greater adjusted odds of subsequent manipulation under anesthesia (adjusted odds ratio [OR] = 1.23 [95% confidence interval {CI}: 1.09 to 1.38]; P < .001) and lysis of adhesions (adjusted OR = 1.58 [95% CI: 1.02 to 2.33]; P = .03) compared to patients who did not have acute prednisone use. Patients who had acute prednisone use also had greater adjusted odds of acute kidney injury (adjusted OR = 1.47 [95% CI: 1.25 to 1.71]; P < .001) and pneumonia (adjusted OR = 4.04 [95% CI: 3.53 to 4.59]; P < .001). There was no increased incidence of infection. ConclusionsPrednisone use shortly following TKA may be associated with a higher incidence of certain surgical and medical complications, but without increased risk for infection. However, given these risks, the optimal patient profile for postoperative prednisone use remains to be defined.

MEDICAL INSURANCE PREMIUM PREDICTION WITH MACHINE LEARNING

A machine learning method for predicting health insurance rates is presented in this article. With healthcare expenditures becoming more complex, it is critical for insurance companies and policyholders to accurately estimate insurance prices. Utilizing a dataset that included medical history, demographic data, and other pertinent variables, a variety of machine learning techniques, such as ensemble methods and regression, were used to create prediction models. R-Squared and mean absolute error were two measures used to assess these models' performance. According to the developed models' results, insurance premiums can be predicted with accuracy, offering useful information for insurance counteragents. This approach has the potential to optimize pricing strategies, enhance risk assessment, and improve decision-making in the healthcare insurance sector. Machine Learning-Based Prediction of Medical Insurance Premiums Make predictions about health insurance companies based on personal traits. A dataset of policyholder attributes (such as age, gender, BMI, number of children, smoking behaviors, and geography) was gathered and preprocessed .Divide the data into sets for testing and training. Create and train a model for an artificial neural network with TensorFlow and Karas. R-squared metrics and mean R-squared error were used to assess the performance of the model. created a high R-Squared predictive model that was accurate. determined the main determinants of insurance rates. Machine learning has shown promise in estimating healthcare costs. This experiment demonstrates how well machine learning predicts medical insurance rates. Insurance companies may offer more individualized insurance plans, expedite the underwriting process, and help customers make well-informed decisions about their healthcare coverage by creating these predictive models. The created model can help policyholders make educated judgments and insurance companies establish proper prices. In the long run, our research helps the insurance industry enhance data-driven techniques, which benefits insurers as well as insured individuals in general.

The Association Between Type of Insurance Plan, Out-of-Pocket Cost, and Adherence to Antihypertensive Medications in Medicare Supplement Insurance Enrollees.

Medicare supplement insurance, or Medigap, covers 21% of Medicare beneficiaries. Despite offsetting some out-of-pocket (OOP) expenses, remaining OOP costs may pose a barrier to medication adherence. This study aims to evaluate how OOP costs and insurance plan types influence medication adherence among beneficiaries covered by Medicare supplement plans. We conducted a retrospective analysis of the Merative MarketScan Medicare Supplement Database (2017-2019) in Medigap enrollees (≥65 years) with hypertension. The proportion of days covered (PDC) was a continuous measure of medication adherence and was also dichotomized (PDC ≥0.8) to quantify adequate adherence. Beta-binomial and logistic regression models were used to estimate associations between these outcomes and insurance plan type and log-transformed OOP costs, adjusting for patient characteristics. Among 27,407 patients with hypertension, the average PDC was 0.68 ± 0.31; 47.5% achieved adequate adherence. A mean $1 higher in 30-day OOP costs were associated with a 0.06 (95% confidence intervals [CIs]: -0.09 to -0.03) lower probability of adequate adherence, or a 5% (95% CI: 4%-7%) decrease in PDC. Compared with comprehensive plan enrollees, the odds of adequate adherence were lower among those with point-of-service plans (odds ratio [OR]: 0.69, 95% CI: 0.62-0.77), but higher among those with preferred provider organization (PPO) plans (OR: 1.08, 95% CI: 1.01-1.15). Moreover, the association between OOP costs and PDC was significantly greater for PPO enrollees. While Medicare supplement insurance alleviates some OOP costs, different insurance plans and remaining OOP costs influence medication adherence. Reducing patient cost-sharing may improve medication adherence.

The impact of terminating cost-sharing reductions payments on health insurance plan choices.

The Trump administration terminated cost-sharing reductions (CSRs) payments to health insurers in 2017, while still required insurers to provide CSRs to eligible enrollees in the Marketplace. Marketplace administration data reveals that, in response to this termination, insurers raised premiums to compensate for their loss. Consequently, premium increases led to more advanced premium tax credits for enrollees in the Marketplace. To investigate the impact of CSRs payment termination on low-income consumer insurance plan choices, I leverage variations in state price regulations and employed a difference-in-differences design. In a robustness analysis, I utilized differences in county income distributions to examine the effects of the termination on insurance choices. The results indicate that after the termination, more low-income enrollees opted for cheaper bronze plans, and fewer chose silver plans. These results suggest that alterations in subsidy channels may inadvertently encourage low-income individuals to purchase less expensive health insurance plans, highlighting an unintended consequence of the termination of cost-sharing subsidies.

Nonstationary frequency analysis of extreme precipitation: Embracing trends in observations

Knowledge of the recurrence intervals of precipitation extremes is vital for infrastructure design, risk assessment, and insurance planning. However, trends and shifts in rainfall patterns globally pose challenges to the application of extreme value analysis (EVA) which relies critically on the assumption of stationarity. In this paper, we explore: (1) the suitability of nonstationary (NS) models in the presence of statistically significant trends, and (2) their potential in modeling out-of-sample data to improve frequency analysis of extreme precipitation. We analyze the benefits of using a nonstationary Generalized Extreme Value (GEV) model for annual extreme precipitation records from Southern Brazil. The location of the GEV distribution is allowed to change with time. The unknown GEV model parameters are estimated using Bayesian techniques coupled with Markov chain Monte Carlo simulation. Next, we use GAME sampling to compute the evidence (and their ratios, the so-called Bayes factors) for stationary and nonstationary models of annual maximum precipitation. Our results show that the presence of a statistically significant trend in annual maximum precipitation alone does not justify the use of a NS model. The location parameter of the GEV distribution must also be well defined, otherwise, stationary models of annual maximum precipitation receive more support by the data. These findings reiterate the importance of accounting for GEV model parameters and predictive uncertainty in frequency analysis and hypothesis testing of annual maximum precipitation data records. Furthermore, a meaningful EVA demands detailed knowledge about the origin and persistence of observed changes.

Protocol for Evaluating Remote Patient Blood Pressure Monitoring Adapted to Black Women and Birthing Persons.

Cardiovascular disease is the leading cause of maternal death among Black women in the United States. A large, urban hospital adopted remote patient blood pressure monitoring (RBPM) to increase blood pressure monitoring and improve the management of hypertensive disorders of pregnancy (HDP) by reducing the time to diagnosis of HDP. The digital platform integrates with the electronic health record (EHR), automatically inputting RBPM readings to the patients' chart; communicating elevated blood pressure values to the healthcare team; and offers a partial offset of the cost through insurance plans. It also allows for customization of the blood pressure values that prompt follow-up to the patient's risk category. This paper describes a protocol for evaluating its impact. Objective 1 is to measure the effect of the digitally supported RBPM on the time to diagnosis of HDP. Objective 2 is to test the effect of cultural tailoring to Black participants. The ability to tailor digital content provides the opportunity to test the added value of promoting social identification with the intervention, which may help achieve equity in severe maternal morbidity events related to HDP. Evaluation of this intervention will contribute to the growing literature on digital health interventions to improve maternity care in the United States.

The effect of Certificate‐of‐Need laws on substance use disorder care for vulnerable populations

AbstractSubstance use disorders are a prevalent and growing problem across the United States, especially for households that rely on publicly funded healthcare insurance plans. State Certificate‐of‐Need (CON) laws for substance use disorder (SUD) treatment facilities can worsen outcomes for these patients by restricting the supply of facilities and beds, leading to spillovers into the general hospital system. We present a choice theory for treatment facility patient admission and model the outcome as a function of the patient's insurance type. We then combine two datasets on state CON laws for SUD treatment facilities with Medicaid patient data from 2017 to 2020 to test the model using a three‐stage least squares design and provide some of the first evidence on Medicaid patient outcomes under CON laws for SUD treatment facilities. We find significant evidence that state CON laws for SUD treatment facilities are associated with higher rates of hospital bed utilization, increases in the number of infants born with Neonatal Abstinence Syndrome, and higher rates of emergency department visits. Our findings are robust to several specification tests, including a model of conditional mixed method endogeneity and incorporating timing of the Affordable Care Act.