Disentangling Moral Hazard and Adverse Selection in Private Health Insurance

Patient Factors Associated With Use of Adult Primary Care and Virtual Visits During the COVID-19 Pandemic

BACKGROUND

The delivery of adult primary care (APC) shifted from predominately in-person to modes of virtual care during the COVID-19 pandemic. It is unclear how these shifts impacted the likelihood of APC use during the pandemic, or how patient characteristics may be associated with the use of virtual care.

METHODS

A retrospective cohort study using person-month level datasets from 3 geographically disparate integrated health care systems was conducted for the observation period of January 1, 2020, through June 30, 2021. We estimated a 2-stage model, first adjusting for patient-level sociodemographic, clinical, and cost-sharing factors, using generalized estimating equations with a logit distribution, along with a second-stage multinomial generalized estimating equations model that included an inverse propensity score treatment weight to adjust for the likelihood of APC use. Factors associated with APC use and virtual care use were separately assessed for the 3 sites.

RESULTS

Included in the first-stage models were datasets with total person-months of 7,055,549, 11,014,430, and 4,176,934, respectively. Older age, female sex, greater comorbidity, and Black race and Hispanic ethnicity were associated with higher likelihood of any APC use in any month; measures of greater patient cost-sharing were associated with a lower likelihood. Conditional on APC use, older age, and adults identifying as Black, Asian, or Hispanic were less likely to use virtual care.

CONCLUSIONS

As the transition in health care continues to evolve, our findings suggest that to ensure vulnerable patient groups receive high quality health care, outreach interventions to reduce barriers to virtual care use may be warranted.

A Linear Decomposition Approach to Explain Excess Direct Healthcare Expenditures Associated with Pain Among Adults with Osteoarthritis

Objective:

Many patients with osteoarthritis experience pain which can lead to higher healthcare expenditures. It is important to understand the factors that drive the excess expenditures associated with pain in osteoarthritis.

Design:

Cross-sectional.

Study sample:

Our study sample consisted of adults (age ⩾ 18 years) from the Medical Expenditure Panel Survey (MEPS, 2018).

Methods:

Adults who were alive during the calendar year and had pain status were included in this study (N = 2804 weighted N = 32.03 million). Osteoarthritis was identified from the medical conditions file and household file. We used multivariable ordinary least squares regression to identify the statistically significant association of pain with direct healthcare expenditures. The Blinder-Oaxaca post-linear decomposition on log-transformed total direct healthcare expenditures was used to estimate the extent to which differences in characteristics contribute to the excess expenditures associated with pain.

Results:

Adults with osteoarthritis and pain had higher average expenditures ($21 814 vs $10 827, P < .001; 9.318 vs 8.538 in logtransformed expenditures) compared to those without pain. Pooled regression weights explained 62.9% of excess expenditures differences in characteristics between the 2 groups. The 2 main drivers of excess healthcare expenditures among adults with osteoarthritis and pain were (i) comorbidities (diabetes, asthma, chronic obstructive pulmonary disease, depression, heart diseases, cancer, and non-cancer pain conditions and (ii) prescription medications (NSAIDs, opioids, and polypharmacy).

Conclusion:

Need factors such as comorbid conditions, and prescription treatment explained the excess healthcare expenditures among adults with osteoarthritis and pain. The study findings suggest that reducing polypharmacy and appropriate management of comorbid conditions may be a pathway to reduce excess expenditures among adults with osteoarthritis and pain.

Does Health Insurance Reduce the Alcohol Consumption? Evidence from China Health and Nutrition Survey

Whether health insurance reduces alcohol consumption has been debated. To identify it, the authors used three-wave balance panel data from China Health and Nutrition Survey and applied a two-way fixed-effect model. The authors found that (1) health insurance reduces alcohol consumption, (2) the effect would be deducted when they have been diagnosed with diabetes, (3) the heterogeneity existed between old and young individuals as well as rural and urban areas, those old individuals would behave more cautious, and urban individuals would consume more alcohol. This study identified the effects of health insurance and the moderating effect of diabetes, which were helpful for policymakers to optimize health insurance policy to ensure the sustainability of health insurance and suggested that primary medical staff should assist patients to establish healthy living habits and reduce their risky behaviors.

Creating ethics guidelines for artificial intelligence and big data analytics customers: The case of the consumer European insurance market

The European Union (EU) has a strong reputation and track record for the development of guidelines for the ethical use of artificial intelligence (AI) generally. In this paper, we discuss the development of an AI and ethical framework by the European Insurance and Occupational Pensions Authority (EIOPA), for the European insurance market. EIOPA's earlier report on big data analytics (EIOPA, 2019) provided a foundation to analyze the complex range of issues associated with AI being deployed in insurance, such as behavioral insurance, parametric products, novel pricing and risk assessment algorithms, e-service, and claims management. The paper presents an overview of AI in insurance applications throughout the insurance value chain. A general discussion of ethics, AI, and insurance is provided, and a new hierarchical model is presented that describes insurance as a complex system that can be analyzed by taking a layered, multi-level approach that maps ethical issues directly to specific level(s).

How Public Trust in Health Care Can Shape Patient Overconsumption in Health Systems? The Missing Links

Overconsumption of health care is an ever-present and complex problem in health systems. It is especially significant in countries in transition that assign relatively small budgets to health care. In these circumstances, trust in the health system and its institutions is of utmost importance. Many researchers have studied interpersonal trust. Relatively less attention, however, has been paid to public trust in health systems and its impact on overconsumption. Therefore, this paper seeks to identify and examine the link between public trust and the moral hazard experienced by the patient with regard to health care consumption. Moreover, it explores the mediating role of patient satisfaction and patient non-adherence. For these purposes, quantitative research was conducted based on a representative sample of patients in Poland. Interesting findings were made on the issues examined. Patients were shown not to overconsume health care if they trusted the system and were satisfied with their doctor-patient relationship. On the other hand, nonadherence to medical recommendations was shown to increase overuse of medical services. The present study contributes to the existing knowledge by identifying phenomena on the macro (public trust in health care) and micro (patient satisfaction and non-adherence) scales that modify patient behavior with regard to health care consumption. Our results also provide valuable knowledge for health system policymakers. They can be of benefit in developing communication plans at different levels of local government.