Understanding Variation in Negotiated Rates Using Novel Health Plan Price Transparency Data.

This study uses health plan price transparency data to examine how negotiated rates for the same service vary within and across US payers and hospitals.

Social Determinants of Health and Insurance Claim Denials for Preventive Care.

Importance: The Patient Protection and Affordable Care Act (ACA) eliminated out-of-pocket cost-sharing for recommended preventive care for most privately insured patients. However, patients seeking preventive care continue to face cost-sharing and administrative hurdles, including claim denials, which may exacerbate inequitable access to care. Objective: To determine whether patient demographics and social determinants of health are associated with denials of insurance claims for preventive care. Design, Setting, and Participants: This cohort study of patients insured through their employers or the ACA Marketplaces used claims and remittance data from Symphony Health Solutions' Integrated DataVerse from 2017 to 2020; analysis was completed from January to July 2024. Exposure: Seeking preventive care. Main Outcomes and Measures: The primary outcome was the frequency of insurer denials for preventive services across 5 categories: specific benefit denials, billing errors, coverage lapses, inadequate coverage, and other. Subgroup analysis was performed across patient household income, education, and race and ethnicity. Secondary outcomes included charges for denied claims, approximating patients' remaining financial responsibility for care. Results: A total of 1 535 181 patients received 4 218 512 preventive services in 2 507 943 unique visits (mean [SD] age at visits, 54.02 [13.19] years; 1 804 637 visits for female patients [71.96%]); 585 299 patients (23.30%) had an annual household income $100 000 or higher, and 824 540 patients had some college education (32.88%). A total of 20 658 individuals (0.82%) were Asian, 139 950 (5.58%) were Hispanic, 219 646 (8.76%) were non-Hispanic Black, 1 372 223 (54.72%) were non-Hispanic White, and 25 412 (1.0%1) were other races and ethnicities not included in the other 4 groups. Of preventive claims, 1.34% (95% CI, 1.32%-1.36%) were denied, consisting mainly of specific benefit denials (0.67%; 95% CI, 0.66%-0.68%) and billing errors (0.51%; 95% CI, 0.50%-0.52%). The lowest-income patients had 43.0% higher odds of experiencing a denial than the highest-income patients (odds ratio, 1.43; 95% CI, 1.37-1.50; P < .001). The least educated enrollees had a denial rate of 1.79% (95% CI, 1.76%-1.82%) compared with 1.14% (95% CI, 1.12%-1.16%) for enrollees with college degrees. Denial rates for Asian (2.72%; 95% CI, 2.55%-2.90%), Hispanic (2.44%; 95% CI, 2.38%-2.50%), and non-Hispanic Black (2.04%; 95% CI, 1.99%-2.08%) patients were significantly higher than those for non-Hispanic White patients (1.13%; 95% CI, 1.12%-1.15%). Conclusions and Relevance: In this cohort study of 1 535 181 patients seeking preventive care, denials of insurance claims for preventive care were disproportionately more common among at-risk patient populations. This administrative burden potentially perpetuates inequitable access to high-value health care.

Systemic treatments for advanced prostate cancer: relationship between health insurance plan and treatment costs.

OBJECTIVES: The high costs of cancer care can cause significant harm to patients and society. Prostate cancer, the leading nonskin malignancy in men, is responsible for the second-highest out-of-pocket (OOP) payments among all malignancies. Multiple first-line treatment options exist for metastatic castration-resistant prostate cancer (mCRPC); although their costs vary substantially, comparative effectiveness data are limited. There is little evidence of how gross payments made by insurers and OOP payments made by patients differ by treatment and health plan type and how these payment differences relate to utilization. STUDY DESIGN: Retrospective cohort study. METHODS: We used IBM MarketScan databases from 2013-2019 to identify men with prostate cancer who initiated treatment with 1 of 6 drugs approved for first-line treatment of mCRPC. We calculated and compared gross and OOP payments and drug utilization across drug and insurance plan types. RESULTS: We identified 4298 patients who met our inclusion criteria. Insurer payments varied substantially by first-line therapy but were similar across different health plan types, except for docetaxel. OOP payments for a given first-line therapy, in contrast, varied by health plan type. Utilization of first-line therapies varied by plan type in unadjusted analyses, but not after adjusting for patient characteristics. CONCLUSIONS: The extent to which patient OOP payments for drugs reflect differences in gross payments made by insurers varies across health insurance plan types. However, even though OOP payments for the same treatment differ across plan types, treatment choice is not significantly different across type of health insurance after controlling for patient characteristics.

Beyond average spending: distributional and seasonal commercial insurance trends, 2012-2021.

OBJECTIVES: The annual mean spending measures typically used to study longitudinal trends mask distributional and seasonal variation that is relevant to patients' perceptions of health care affordability and, in turn, provider collections. This study describes shifts in the distribution and seasonality of plan and patient out-of-pocket spending from 2012 through 2021. STUDY DESIGN: Analysis of multipayer commercial claims data. METHODS: Medical spending per enrollee was calculated by summing inpatient, outpatient, and professional services, which comprised plan payments and out-of-pocket payments (deductible, coinsurance, co-payment). To account for the long right tail of the spending distribution, enrollees were stratified by their decile of annual medical spending, and annual mean spending estimates were calculated overall and by decile. Mean spending estimates were also calculated by quarter-year. RESULTS: Inflation-adjusted medical spending grew most quickly among the highest decile of spenders, without proportional growth in their out-of-pocket expenses. Out-of-pocket spending increased for the majority of enrollees in our sample prior to the COVID-19 pandemic, in real dollars and as a share of total medical spending. Out-of-pocket spending was increasingly concentrated in the early months of the calendar year, driven by deductible spending, and was lower in 2020 and 2021, plausibly due to policies limiting cost sharing for COVID-19-related services. CONCLUSIONS: Insurance is working well to protect the highest spenders at the cost of reduced insurance generosity among spenders elsewhere in the distribution. The increasing cross-subsidization among enrollees through cost-sharing design-vs premiums-is a trend to watch among rising public concerns about underinsurance and medical debt.

Application of type II diabetes incidence and mortality rates for insurance.

Prolonging life is a global trend, and more medical expenditure is being spent on chronic diseases owing to population aging. Diseases commonly seen in middle-aged and elderly people, such as heart disease and diabetes, have slowed mortality improvement in recent years. Diabetes is a common chronic disease and comorbidity of many serious health conditions. The total estimated cost of diabetes in the United States was $327 billion in 2017. However, many people are unaware that diabetes is common, and at least 21.4% of adults do not know that they have diabetes. The number of diabetes-related deaths has been increasing, and diabetes was the 5th cause of death in Taiwan in 2019. In this study, we explore the trend and influence of diabetes in Taiwan and apply mortality models, such as the Lee-Carter and Age-Period-Cohort models, using data from Taiwan's National Insurance to model the incidence and mortality rates of diabetes. We found that the Lee-Carter model provides fairly satisfactory estimates and that people with diabetes regularly taking diabetes medication have lower mortality rates. Moreover, we demonstrate how these results can be used to design diabetes related insurance products and prepare the insured to face the impact of incurring diabetes. In addition, we consider different criteria for judging whether people have diabetes (as there is no consensus on these criteria) and investigate the issue of moral hazard in designing diabetes insurance products.

Household financial burden associated with out-of-pocket payments for healthcare in Iran: insights from a cross-sectional survey.

BACKGROUND: One of the key functions and ultimate goals of health systems is to provide financial protection for individuals when using health services. This study sought to evaluate the level of financial protection and its inequality among individuals covered by the Social Security Organization (SSO) health insurance between September and December 2023 in Iran. METHODS: We collected data on 1691 households in five provinces using multistage sampling to examine the prevalence of catastrophic healthcare expenditure (CHE) at four different thresholds (10%, 20%, 30%, and 40%) of the household's capacity to pay (CTP). Additionally, we explored the prevalence of impoverishment due to health costs and assessed socioeconomic-related inequality in OOP payments for healthcare using the concentration index and concentration curve. To measure equity in out-of-pocket (OOP) payments for healthcare, we utilized the Kakwani progressivity index (KPI). Furthermore, we employed multiple logistic regression to identify the main factors contributing to households experiencing CHE. FINDINGS: The study revealed that households in our sample allocated approximately 11% of their budgets to healthcare services. The prevalence of CHE at the thresholds of 10%, 20%, 30%, and 40% was found to be 47.1%, 30.1%, 20.1%, and 15.7%, respectively. Additionally, we observed that about 7.9% of the households experienced impoverishment due to health costs. Multiple logistic regression analysis indicated that the age of the head of the household, place of residence, socioeconomic status, utilization of dental services, utilization of medicine, and province of residence were the main factors influencing CHE. Furthermore, the study demonstrated that while wealthy households spend more money on healthcare, poorer households spend a larger proportion of their total income to healthcare costs. The KPI showed that households with lower total expenditures had higher OOP payments relative to their CTP. CONCLUSION: The study findings underscore the need for targeted interventions to improve financial protection in healthcare and mitigate inequalities among individuals covered by SSO. It is recommended that these interventions prioritize the expansion of coverage for dental services and medication expenses, particularly for lower socioeconomic status household.

Equity considerations for the implementation of health insurance benefit package in Ethiopia: result of expert Delphi exercise.

BACKGROUND: Efficiency, equity and financial risk protection are key health systems objectives. Equitable distribution of health care is among the priority strategic initiative of the government of Ethiopia. However, data on the distribution of interventions benefits or on disease burden disaggregated by subpopulations to guide health care priority setting is not available in Ethiopia. METHODS: Aligned with policy documents, we identified the following groups to be the worse off in the Ethiopian context: under-five children, women of reproductive age, the poor, and rural residents. We used the Delphi technique by a panel of 28 experts to assign a score for 253 diseases/conditions over a period of two days, in phases. The expert panel represented different institutes and professional mix. Experts assigned a score 1 to 4; where 4 indicates disease/condition predominantly affecting the poor and rural residents and 1 indicates a condition more prevalent among the wealthy and urban residents. Subsequently, the average equity score was computed for each disease/condition. RESULTS: The average scores ranged from 1.11 (for vitiligo) to 3.79 (for obstetric fistula). We standardized the scores to be bounded between 1 and 2; 1 the lowest equity score and 2 the highest equity score. The scores for each disease/condition were then assigned to their corresponding interventions. We used these equity scores to adjust the CEA values for each of the interventions. To adjust the CEA values for equity, we multiplied the health benefits (the denominator of the cost-effectiveness value) of each intervention by the corresponding equity scores, resulting in equity adjusted CEA values. The equity adjusted CEA was then used to rank the interventions using a league table. CONCLUSIONS: The Delphi method can be useful in generating equity scores for prioritizing health interventions where disaggregated data on the distribution of diseases or access to interventions by subpopulation groups are not available.

The Annual Cost of Cancer Screening in the United States.

BACKGROUND: Cancer has substantial health, quality-of-life, and economic impacts. Screening may decrease cancer mortality and treatment costs, but the cost of screening in the United States is unknown. OBJECTIVE: To estimate the annual cost of initial cancer screening (that is, screening without follow-up costs) in the United States in 2021. DESIGN: Model using national health care survey and cost resources data. SETTING: U.S. health care systems and institutions. PARTICIPANTS: People eligible for breast, cervical, colorectal, lung, and prostate cancer screening with available data. MEASUREMENTS: The number of people screened and associated health care system costs by insurance status in 2021 dollars. RESULTS: Total health care system costs for initial cancer screenings in the United States in 2021 were estimated at $43 billion. Approximately 88.3% of costs were attributable to private insurance; 8.5% to Medicare; and 3.2% to Medicaid, other government programs, and uninsured persons. Screening for colorectal cancer represented approximately 64% of the total cost; screening colonoscopy represented about 55% of the total. Facility costs (amounts paid to facilities where testing occurred) were major drivers of the total estimated costs of screening. LIMITATIONS: All data on receipt of cancer screening are based on self-report from national health care surveys. Estimates do not include costs of follow-up for positive or abnormal screening results. Variations in costs based on geography and provider or health care organization are not fully captured. CONCLUSION: The $43 billion estimated annual cost for initial cancer screening in the United States in 2021 is less than the reported annual cost of cancer treatment in the United States in the first 12 months after diagnosis. Identification of cancer screening costs and their drivers is critical to help inform policy and develop programmatic priorities, particularly for enhancing access to recommended cancer screening services. PRIMARY FUNDING SOURCE: None.

Health insurance, agricultural production and investments.

We study the effects of health insurance coverage on agricultural production decisions, examining the causal relationships by exploiting a health care reform and providing a theoretical framework to elucidate underlying mechanisms. We find that the reform led to long-run increases in total cultivation investments and output, accompanied by a shift in households' cultivation portfolio towards riskier crops. We explain these findings using a model of agricultural investment, highlighting the important roles of health insurance in mitigating background medical expenditure risks and enhancing health. We also find that the reform improved households' financial well-being through reduced debts and defaults on loans.

Relationship between social determinants of health and hospitalizations and costs in patients with major depressive disorder.

BACKGROUND: The relationship of patient characteristics and social determinants of health (SDOH) with hospitalizations and costs in patients with major depressive disorder (MDD) has not been assessed using real-world data. OBJECTIVE: To identify factors associated with higher hospitalizations and costs in patients with MDD. METHODS: A retrospective observational study identified patients aged 18 years and older newly diagnosed with MDD between July 1, 2016, and December 31, 2018. SDOH were linked to patients at the "near-neighborhood" level. Multivariable models assessed association of patient characteristics with hospitalizations (incidence rate ratios [95% CI]) and costs (cost ratios [95% CI]). RESULTS: Of 1,958,532 patients with MDD, 49.6% had Commercial and 50.4% Medicaid insurance; mean ages were similar (43.9; 43.4) with more female patients (67.6%; 70.5%). MDD patients with Commercial insurance had a mean household income of $75,044; 53.2% were married; 76.5% owned their home; 64.4% completed high school or less; and 2.8% had limited English-language proficiency (LEP). Patients covered by Medicaid had a household income of $46,708; 68.1% lived alone with 41.6% married; 54.6% owned their home; more than 4-in-5 patients (80.8%) completed high school or less, and 6.3% had LEP. Nearly one-third of Medicaid insured patients with MDD had at least 1 hospitalization (29.6%) with a mean length of stay 6.8 days; total health care costs were $21,467 annually. Commercially insured patients with MDD had 14.7% hospitalization rates with a length of stay of 5.9 days; total costs were $14,531. Multivariable models show female patients are less likely (Commercial 0.87; Medicaid 0.80; P < 0.05), and patients with more comorbidities are more likely to be hospitalized (Commercial 1.33; Medicaid 1.27; P < 0.05). All treatment classes relative to antidepressants only increased likelihood of hospitalizations-particularly antipsychotic+antianxiety use (Commercial 2.99; Medicaid 2.29)-and costs (Commercial 2.32; Medicaid 2.00) (all P < 0.05). Household income was inversely associated with hospitalizations for both insured populations. LEP reduced the likelihood of hospitalizations by more than 70% among Medicaid patients (0.27, P < 0.05) and was associated with higher costs for Commercial (2.01) but lower costs for Medicaid (0.37) (P < 0.05). Living in areas with no shortage of mental health practitioners was associated with higher hospitalizations and costs. CONCLUSIONS: We identified patient characteristics associated with higher rates of hospitalizations and costs in patients with MDD in 2 insured populations. Female sex, higher comorbidities, and living in areas with no shortage of mental health practitioners were associated with higher hospitalizations and costs, whereas income was inversely associated with hospitalizations. The findings suggest disparities in access to care related to income, LEP, and availability of mental health practitioners that should be addressed to assure equitable care for patients with MDD.

Health Outcome Changes in Individuals With Type 1 Diabetes After a State-Level Insulin Copayment Cap.

Importance: Many insulin users ration doses due to high out-of-pocket costs. Starting January 2020 with Colorado, 25 states and the District of Columbia enacted laws that cap insulin copayments. Objective: To estimate the association of Colorado's $100 copayment cap with out-of-pocket spending, medication adherence, and health care services utilization for diabetes-related complications. Design, Setting, and Participants: In this cohort study using Colorado's All-Payer Claims Database, nonelderly insulin users with type 1 diabetes were analyzed from January 2019 to December 2020. Outcome changes were compared in the prepolicy and postpolicy period among individuals continuously enrolled in state-regulated and non-state-regulated plans using difference-in-differences regressions. Subgroup analyses were conducted based on individuals' prepolicy spending (low: never ≥$100 out-of-pocket vs high: ≥$100 out-of-pocket cost at least once). Data were analyzed from June 2023 to May 2024. Exposure: Enrollment in state-regulated health insurance plans subject to the copayment cap legislation. Main Outcomes and Measures: Adherence to basal and bolus insulin treatment was evaluated using the proportion of days covered measure, out-of-pocket spending reflected prescription cost for a 30-day supply, and health care utilization for diabetes-related complications was identified using primary diagnosis codes from medical claims data. Results: The panel included 1629 individuals with type 1 diabetes (39 096 person-months), of which 924 were male (56.7%), 540 (33.1%) had 1 or more comorbidities, and the mean (SD) age was 40.6 (15.9) years. Overall, the copayment cap was associated with out-of-pocket spending declines of $17.3 (95% CI, -$27.3 to -$7.3) for basal and $11.5 (95% CI, -$24.7 to $1.7) for bolus insulins and increases in adherence of 3.2 (95% CI, 0.0 to 6.5) percentage points for basal and 3.3 (95% CI, 0.3 to 6.4) percentage points for bolus insulins. Changes in adherence were associated with increases within the prepolicy high-spending group (basal, 9.9; 95% CI, 2.4 to 17.4 percentage points; bolus, 13.0; 95% CI, 5.1 to 20.9 percentage points). The policy was also associated with a mean reduction of -0.09 (95% CI, -0.16 to -0.02) medical claims for diabetes-related complications per person per month among high spenders, a 30% decrease. Conclusions and Relevance: In this cohort study of Colorado's insulin copayment cap among individuals with type 1 diabetes, the policy was associated with an overall decline in out-of-pocket spending, an increase in medication adherence, and a decline in claims for diabetes-related complications only among insulin users who spent more than $100 in the prepolicy period at least once.

GLP-1 Medication Use for Type 2 Diabetes Has Soared.

This JAMA Data Brief discusses new data from the Medical Expenditure Panel Survey, or MEPS, Household Component, a household survey sponsored by the Agency for Healthcare Research and Quality.

Cost-sharing and associated factors in the Peruvian private health care system.

BACKGROUND: The costs associated with healthcare are of critical importance to both decision-makers and users, given the limited resources allocated to the health sector. However, the available scientific evidence on healthcare costs in low- and middle-income countries, such as Peru, is scarce. In the Peruvian context, the health system is fragmented, and the private health insurance and its financing models have received less research attention. We aimed to analyse user cost-sharing and associated factors within the private healthcare system. METHODS: Our study was cross-sectional, using open data from the Electronic Transaction Model of Standardized Billing Data-TEDEF-SUSALUD, between 2021-2022. Our unit of analysis is the user's medical bills. We considered the total amount of cost-sharing, proportion of total payments as cost-sharing, and cost-sharing as a proportion of minimum salaries. We use a multiple regression model to perform the analyses. RESULTS: Our study included 5,286,556 health services provided to users of the private health insurance in Peru. We found a significant difference was observed in the cost-sharing for hospitalization-related services, with an average of 419.64 soles per day (95% CI: 413.44 to 425.85). Also, we identified that for hospitalization-related services per day is, on average, 0.41 (95% CI: 0.41 to 0.41) minimum salaries more expensive than outpatient care, although cost-sharing per day of hospitalization represent on average only 14% of the total amount submitted. CONCLUSIONS: Our study provides a detailed overview of cost-sharing in the private healthcare system in Peru and the factors associated with them. Policymakers can use the study's finding that higher cost-sharing for inpatient hospitalization compared to outpatient care in private insurance can create inequities in access to healthcare to design policies aimed at reducing these costs and promoting a more equitable and accessible healthcare system in Peru.

Cross-validation of insurer and hospital price transparency data.

Given recent congressional interest in codifying price transparency regulations, it is important to understand the extent to which newly available price transparency data capture true underlying procedure-level prices. To that end, we compared the prices for maternity services negotiated between a large payer and 26 hospitals in Mississippi across 2 separate price transparency data sources: payer and hospital. The degree of file overlap is low, with only 16.3% of hospital-billing code observations appearing in both data sources. However, for the observations that overlap, pricing concordance is high: Corresponding prices have a correlation coefficient of 0.975, 77.4% match to the penny, and 84.4% are within 10%. Exact price matching rates are greater than 90% for 3 of the 4 service lines included in this study. Taken together, these results suggest that although administrative misalignment exists between payers and hospitals, there is a measure of signal amid the price transparency noise.

Multilevel medical insurance mitigate health cost inequality due to air pollution: Evidence from China.

BACKGROUND: Air pollution affects residents' health to varying extents according to differences in socioeconomic status. However, there has been a lack of research on whether air pollution contributes to unfair health costs. METHODS: In this research, data from the China Labour Force Dynamics Survey are matched with data on PM2.5 average concentration and precipitation, and the influence of air pollution on the health expenditures of residents is analysed with econometric methods involving a two-part model, instrument variables and moderating effects. RESULTS: The findings reveal that air pollution significantly impacts Chinese residents' health costs and leads to low-income people face health inequality. Specifcally, the empirical evidence shows that air pollution has no significant influence on the probability of residents' health costs (ß = 0.021, p = 0.770) but that it increases the amount of residents' total outpatient costs (ß = 0.379, p < 0.006), reimbursed outpatient cost (ß = 0.453, p < 0.044) and out-of-pocket outpatient cost (ß = 0.362, p < 0.048). The heterogeneity analysis of income indicates that low-income people face inequality due to health cost inflation caused by air pollution, their total and out-of-pocket outpatient cost significantly increase with PM2.5 (ß = 0.417, p = 0.013; ß = 0.491, p = 0.020). Further analysis reveals that social basic medical insurance does not have a remarkable positive moderating effect on the influence of air pollution on individual health inflation (ß = 0.021, p = 0.292), but supplementary medical insurance for employees could reduce the effect of air pollution on low-income residents' reimbursed and out-of-pocket outpatient cost (ß=-1.331, p = 0.096; ß=-2.211, p = 0.014). CONCLUSION: The study concludes that air pollution increases the amount of Chinese residents' outpatient cost and has no significant effect on the incidence of outpatient cost. However, air pollution has more significant impact on the low-income residents than the high-income residents, which indicates that air pollution leads to the inequity of medical cost. Additionally, the supplementary medical insurance reduces the inequity of medical cost caused by air pollution for the low-income employees.

State Out-Of-Pocket Caps On Insulin Costs: No Significant Increase In Claims Or Utilization.

Nearly all patients with type 1 diabetes and 20-30 percent of patients with type 2 diabetes use insulin to manage glycemic control. Approximately one-quarter of patients who use insulin report underuse because of cost. In response, more than twenty states have implemented monthly caps on insulin out-of-pocket spending, ranging from $25 to $100. Using a difference-in-differences approach, this study evaluated whether state-level caps on insulin out-of-pocket spending change insulin usage among commercially insured enrollees. The study included 33,134 people ages 18-64 who had type 1 diabetes or who used insulin to manage type 2 diabetes with commercial insurance coverage that was subject to state-level oversight and was included in the 25 percent sample of the IQVIA PharMetrics database during 2018-21. Insulin out-of-pocket caps did not significantly increase quarterly insulin claims for enrollees who had type 1 diabetes or who used insulin to manage type 2 diabetes. State-level caps on insulin out-of-pocket spending for commercial enrollees did not significantly increase insulin use; that may be in part because of out-of-pocket expenses being lower than cap amounts.

The Role of Individual Coverage Health Reimbursement Arrangements-The Liger of Health Insurance?

This JAMA Forum discusses aspects of individual coverage health reimbursement arrangements and their expanded use over the last few years.