Health Insurance Plan Research Articles

HAS YOUR SMILE BEEN CHECKED?: CHARACTERISTICS OF DENTAL CARE UTILIZATION OF MEDICARE BENEFICIARIES

Abstract Background An area under researched is oral health disparities in the population of older adults residing in the United States. The current dental benefits offered through health insurance plans that older adults are likely to utilize, particularly Medicare and Medicaid, are inadequate. The deficiency harms this population’s physical and mental health and well-being. Additionally, insufficient oral health care has adverse outcomes, including tooth loss, poor self-esteem, malnutrition, and cardiovascular complications. While evidence suggests race and ethnicity can be predisposing factors impacting access to dental care, it is unclear the extent to which disparities in utilization exist. Methods This exploratory, cross-sectional study examined the relationship between dental care utilization and Medicare beneficiary characteristics. Data from the 2018 wave of the Health and Retirement Study were used. The sample consisted of 8,357 respondents aged 65 and older enrolled in Medicare. Results There was an association between dental care utilization and race, ethnicity, health status, education, and age. A much lower proportion of Black and Hispanic older adults reported seeing a dentist in two years than non-Hispanic White beneficiaries (p<.001). Dental care utilization was also less among respondents who were older (p<.01), in poorer health (p<.001), and had less education (p<.001). Conclusions and Implications Findings suggest disparities in dental care utilization exist and disproportionally impact vulnerable Medicare beneficiaries. This is concerning, particularly given racial and ethnic differences in health. The conclusions accentuate areas such as access to dental care services, coverage, and policy implementation concerning and impacting older adults.

INCONSISTENT ESTIMATES OF POOR HEALTH BETWEEN EUROPEAN SURVEYS—A MATTER OF SURVEY DESIGN?

Abstract Several studies have reported about inconsistencies in survey estimates on health, both within and between countries. One potential source of inconsistency in results from population surveys is survey quality. In particular, the prevalence of poor health among older people is sensitive for survey design factors and the inclusion of older people with poor health. We aimed to explore the association between survey design factors and within-country differences (between EU-SILC and SHARE) in estimated prevalence of poor health (long-term illness and global activity limitation (GALI)) among older people. The analyses include people 65 years and older from 25 European countries with data collections both in SHARE and EU-SILC 2017. Results show substantial differences in sociodemographic factors and estimates of poor health, both between and within countries. The within-country differences in the prevalence of long-term illness ranged from -18 percentage points for men in Denmark (lower in EU-SILC compared with SHARE) to 42 percentage points for women in Cyprus (higher in EU-SILC compared with SHARE). The prevalence of GALI showed similar patterns. We conclude that estimates of poor health, measured by long-term illness and GALI, have great variation between surveys within the same country. Survey design factors, such as response rate and the type and number of survey mode seems to explain part of these inconsistencies. Population surveys are widely used for monitoring health and the results from these surveys are utilized in health policy planning. Thus, it is crucial to understand the possible impact of survey design on health estimates.

Efficient and Robust Estimation of the Generalized LATE Model

This article studies the estimation of causal parameters in the generalized local average treatment effect (GLATE) model, which expands upon the traditional LATE model to include multivalued treatments. We derive the efficient influence function (EIF) and the semiparametric efficiency bound (SPEB) for two types of causal parameters: the local average structural function (LASF) and the local average structural function for the treated (LASFT). The moment conditions generated by the EIF satisfy two robustness properties: double robustness and Neyman orthogonality. Based on the robust moment conditions, we propose the double/debiased machine learning (DML) estimator for estimating the LASF. The DML estimator is well-suited for high dimensional settings. We also propose null-restricted inference methods that are robust against weak identification issues. As an empirical application of these methods, we examine the potential health outcome across different types of health insurance plans using data from the Oregon Health Insurance Experiment.

Age and sex-related comparison of referral-based telemedicine service utilization during the COVID-19 pandemic in Ontario: a retrospective analysis

BackgroundThe COVID-19 pandemic has led to increased utilization of telemedicine services.MethodsA retrospective analysis of all referral-based ambulatory telemedicine services in Ontario from November 2019 to June 2021 was collected from the Ontario Health Insurance Plan (OHIP) billing database. Only fee-for-service billings were included in the present analysis. Coincident COVID-19 cases were obtained from Public Health Ontario. Comparisons were made based on age bracket, sex, telemedicine and in-person care.ResultsBillings for telemedicine services in Ontario increased from $1.7 million CAD in November 2019 to $64 million CAD in April 2020 and the proportions reached a mean peak of 72% in April 2020 and declined to 46% in June 2021. A positive correlation was found between the use of telemedicine and COVID-19 cases (p = 0.05). The age group with the highest proportion of telemedicine use was the 10–20-year-olds, followed by the 20–50-year-olds (61 ± 9.0%, 55 ± 7.3%, p = 0.01). Both age groups remained above 50% telemedicine services at the end of the study period. There seemed to be higher utilization by females (females 54.2 ± 8.0%, males 47.9 ± 7.7%, ANCOVA p = 0.05) for all specialties, however, after adjusting for male to female ratio m:f of 0.952:1.0 according to the 2016 census, this was no longer significant.ConclusionsThe use of telemedicine services remained at a high level across groups, particularly the 10–50-year-olds. There were clear age preferences for using telemedicine. Studying these differences may provide insights into how the delivery of non-hospital-based medicine has changed during the COVID-19 pandemic.

The Observation of Patient Satisfaction with Healthcare Services in Chinese Public Hospitals Through Patients Experiences in Jiangsu

Background: The objective of this study was to assess patients' overall satisfaction with and experiences with various aspects of healthcare in Chinese public hospitals. In addition, it aimed to find out the extent to which patients' experiences in public hospitals in urban and rural locations might be accountable for general satisfaction. Methods: The data were derived from questionnaires designed to serve the patient's satisfaction within hospitals regarding several aspects in ten primary and tertiary level public/private hospitals across Jiangsu (China). In 2019, a survey encompassing 1185 Patients, physicians, and paramedical staff members was conducted in ten public hospitals located in Jiangsu, China. Donabedian's model classified patient experience questions into six categories under the heading’s "structure" and "process," with overall satisfaction serving as the "outcome" category. To compare patient experiences and overall satisfaction in urban and rural settings, chi-square tests were employed. Results: For assessing the government healthcare system's staff, health insurance plans, and healthcare delivery units. The health status of the target populations was compared with that of the responders by looking at the demographic distribution, life expectancy, service delivery with and without favoritism, and health behaviors. Surveys from decision-makers, implementers, and the general public who experience policy revealed that a much higher proportion of respondents reported improvements and favorable changes in the majority of areas. The survey's findings indicate that patients are often more satisfied with urban hospitals as compared to rural hospitals. In Jiangsu, medical personnel are given nearly identical resources and facilities, albeit in transformed forms according to their jurisdiction. The process is how the policies are initiated, negotiated, formulated, communicated, implemented, and evaluated. Policy development is dominated by political factors impacting all aspects of policy, i.e., context, process, and content. Conclusions: Jiangsu has updated and implemented its health policy according to the demand of the day, which has noted the experiences of its workers, compassionate attitudes, and service delivery despite the workload. The province has put in place several insurance schemes, but there is an issue of dissatisfaction in rural health centers as compared to urban hospital patients that can be resolved by bringing awareness in the rural society; Jiangsu is also home to the best version of the Electronic Medical Record system. Furthermore, the satisfaction of urban hospital patients is comparatively high due to the excellent behavior of healthcare workers, the proper referral system, insurance availability, the duration of healthcare services, the out-of-pocket treatment cost, the resources availability at PHC centers, and the development of the healthcare system were also noted with noteworthy numbers.

Investigating catastrophic health expenditure among people living with HIV and AIDS in South Western Nigeria

AbstractBackgroundThis study aimed to determine the catastrophic healthcare expenditure (CHE) among people living with HIV (PLHIV) in Lagos and to identify factors associated with CHE among them.MethodsThe study was a descriptive cross‐sectional survey conducted between January and March 2021 among 578 PLHIVs drawn from various healthcare facilities in Lagos where HIV care and treatment services should be provided free of charge. Data were collected through pretested questionnaires and analyzed using Stata SE 12.ResultsThe mean monthly expenditure on food was N29,282 ($53.2), while expenditure on healthcare averaged N8364 ($15.2). Nearly 60% of respondents experienced CHE, while around 30% had to borrow money to pay for some aspect of their medical treatment. Almost all (96%) had no health insurance plan. Respondents' group, personal income, perception of current health status, and the number of people in their households were significantly associated with catastrophic health expenditure p < 0.05. PLHIV in the racial/ethnic minority/migrants' group and those who earned less than ₦30,000 ($55) were statistically significantly associated with CHE at p < 0.001 with OR of 28.7 and 3.15, respectively.ConclusionsThe study, therefore, highlights the widespread financial hardship faced by PLHIV in accessing healthcare, and the need for policies to increase financial risk protection.

EE257 Real-World, Longitudinal, Retrospective Study of Healthcare Resource Utilization of Patients With Atopic Dermatitis Under the Public Health Insurance Plan of Quebec, Canada

EE257 Real-World, Longitudinal, Retrospective Study of Healthcare Resource Utilization of Patients With Atopic Dermatitis Under the Public Health Insurance Plan of Quebec, Canada

Budget impact of oral nirmatrelvir/ritonavir in adults at high risk for progression to severe COVID-19 in the United States.

Nirmatrelvir/ritonavir (NMV/r) is indicated for the treatment of mild-to-moderate COVID-19 in adults who are at high risk for progression to severe COVID-19. NMV/r has also been authorized for emergency use by the US Food and Drug Administration for the treatment of mild-to-moderate COVID-19 in pediatric patients (aged 226512 years and weighing at least 40 kg) who are at high risk for progression to severe COVID-19. Understanding the budget impact of introducing NMV/r for the treatment of adults with COVID-19 is of key interest to US payers. To estimate the annual budget impact of introducing NMV/r in a US commercial health plan setting in the current Omicron COVID-19 era. A budget impact model was developed to assess the impact of NMV/r on health care costs in a hypothetical 1-million-member commercial health insurance plan over a 1-year period in the US population; clinical and cost inputs were derived from published literature with a focus on studies in the recent COVID-19 era that included vaccinated population and predominance of the Omicron variant. In the base-case analysis, it was assumed the only effect of NMV/r was a reduction in incidence (not severity) of hospitalization or death; its potential effect on post-COVID conditions was assessed in a scenario analysis. Outcomes included the number of hospitalizations, total cost, per patient per year (PPPY) costs, and per member per month (PMPM) costs. Sensitivity and scenario analyses were conducted to assess uncertainty around key model inputs. An estimated 29,999 adults were eligible and sought treatment with oral antiviral for COVID-19 over 1 year. The availability of NMV/r was estimated to reduce the number of hospitalizations by 647 with a total budget impact of $2,733,745, $91 PPPY, and $0.23 PMPM. NMV/r was cost saving when including post-COVID conditions with a -$1,510,780 total budget impact, a PPPY cost of -$50, and a PMPM cost of -$0.13. Sensitivity analyses indicated results were most sensitive to the risk of hospitalization under supportive care, risk of hospitalization with NMV/r treatment and cost of NMV/r. Treatment with NMV/r in the current COVID-19 era is estimated to result in substantial cost offsets because of reductions in hospitalization and modest budget impact to potential overall cost savings.

Utilization of health insurance by patients admitted for dental surgical procedures at a tertiary care hospital in Coastal Karnataka: a retrospective study.

Background: There are various medical insurance options available in India. However, unlike many other countries, dental insurance plans are rare. The aim of this study was to assess the utilization of various government and private health insurance schemes by patients admitted for dental surgical procedures at a tertiary care hospital in coastal Karnataka, India. Methods: A study was conducted retrospectively to gather data on the socio-demographics, bill details, insurance, and benefits claimed by patients admitted to the Department of Oral and Maxillofacial Surgery at a tertiary care hospital from May 2016 to September 2022. Results: Out of 1750 patients, only 856 (48.9%) patients had availed of insurance, 395 patients (22.6%) utilized government health insurance policies, and 461 patients (26.3%) availed of private health insurance plans. Among Government schemes, primarily Ayushman Bharat-Arogya Karnataka was used by 262 (30.6%) patients, followed by Employees' State Insurance Scheme by 110 (12.9%) patients. Among private schemes, 212 (24.8%) patients used the policies purchased by them, 19 (2.2%) patients' medical expenses were paid by their employers, 105 (12.3%) patients utilized Manipal Arogya Suraksha and 124 (14.5%) patients used Medicare provided by the hospital. Bivariate linear regression confirmed that the total bill amount, out-of-pocket expenditure by the patient, and insurance amount reimbursed to the hospital were significantly associated with the type of insurance (government vs. private). The study noticed a gradual rise in insured patients every year. Conclusion: Greater utilization of health insurance should be encouraged because the cost of dental treatment has always hindered the use of oral health services worldwide. This study highlights that the benefit available to the patients were mainly through general health insurance schemes, not specifically dental health insurance. Insurance schemes covering dental must be promoted more aggressively in the media, highlighting their available benefits, merits, and demerits.

Assessment of Cataract Risk after Diagnostic Head CT Scan Radiation Exposure in Ontario, Canada.

Ionizing radiation is one of the known risk factors for cataract development, however, there is still debate regarding the level of risk after low dose exposures. One of the largest sources of radiation exposure to the lens of the eye is diagnostic CT scans. The aim of this study was to examine whether ionizing radiation associated with head CT scans increases cataract risk in residents of Ontario, Canada. Data were collected from January 1, 1994 to December 31, 2015 (22 years) from anonymized Ontario Health Insurance Plan (OHIP) medical records for over 16 million subjects. A lens dose was estimated for each CT scan using the National Cancer Institute dosimetry system for CT (NCICT) program combined with Canada-specific CTDIvol data. Multivariate Cox proportional hazards analysis was performed with cataract extraction surgery as the primary outcome and lens dose as the main variable of interest, with inclusion of various medical and demographic covariates. Lag periods of 3, 5 and 7 years were incorporated. When lens dose was treated as a continuous variable, hazard ratios (per 100 mGy) ranged from 0.82 (0.80-0.84) to 1.10 (1.09-1.11) depending on the lag period. As a secondary analysis, when individuals were binned based on their total cumulative dose, no significant dose response pattern was observed in the low dose region. Overall, within the bounds of this study, the data do not support an increased risk of vision impairing cataracts after diagnostic head CT scan radiation exposure.

2308. Exploring the Seasonality of COVID-19 with Machine Learning: A Distinct Bimodal Pattern?

Abstract Background Respiratory viruses, such as influenza and RSV, occur annually during the winter season in temperate regions. It is essential to determine if SARS-CoV-2 exhibits seasonal patterns for public health planning and vaccination policies. Although winter spikes in COVID-19 cases have been observed, the higher transmissibility of SARS-CoV-2 suggests its seasonality may have a different pattern. Methods We plotted the time-series data on COVID-19, Influenza, and RSV from January 2020-February 2023 at Stanford Healthcare, to compare seasonal patterns. We then modeled predictions for COVID-19 cases in the United States for the next 913 days, using data from January 2020-February 2023. This data was split into a training set and a test set, then Facebook Prophet machine learning library was used to fit a model to the training set and generate forecasts for the test set and future time periods. Cross-validation of the model was performed by calculating the root mean squared error (RMSE) and mean absolute percentage error (MAPE). Results The time-series data of cases for Influenza, RSV, and COVID-19 were plotted from January 1, 2020, to February 28, 2023, revealing bimodal peaks for COVID-19 during the winter (December - February) and summer (July - September) months (Figure 1). This differed from the unimodal patterns observed for Influenza and RSV cases (Figures 2 and 3, respectively). The Prophet model for COVID-19 cases over the next 913 days in the United States demonstrated an additional peak during the summer months (Figure 4). However, other external factors that may influence seasonality, such as the stochastic development of new SARS-CoV-2 variants, weaning of immunity, and testing intensity were not modeled.Figure 1.COVID-19 time-series at Stanford Healthcare during January 2020–February 2023Time-series of COVID-19 cases demonstrates bimodal peaks during the winter (December - February) and summer (July - September) months.Figure 2.Influenza time-series at Stanford Healthcare during January 2020–February 2023Time-series of Influenza cases demonstrates one peak during the winter (December - February) months.Figure 3.RSV time-series at Stanford Healthcare during January 2020–February 2023Time-series of RSV cases demonstrates one peak during the winter (December - February) months. Conclusion COVID-19 may exhibit distinct bimodal seasonal patterns that are not observed for other respiratory viruses. Future public health interventions should consider the distinct seasonal patterns of COVID-19 when developing prevention and control strategies.Figure 4.Prophet Model forecasting COVID-19 peaks in the United States for the next 3 years Cross-validation results for the Prophet model's performance in predicting COVID-19 cases in the United States over a 5-day horizon. The model's RMSE was 110,342.81, indicating an average deviation from the actual values. The MAPE was 0.486, reflecting the model's average percentage error. The coverage was 0.35, indicating that the actual value fell within the 80% prediction interval of the model only 35% of the time, which may suggest limitations for longer-term forecasting. Disclosures All Authors: No reported disclosures

An Investigation of the Impact of Expanding High-Deductible Health Plans on Patient Decision for Cochlear Implant Surgery and Postoperative Outcomes

HypothesisAfter the expansion of high deductibles, patients will delay cochlear implant (CI) surgery to the end of the year, and the risk of postoperative known risks will increase.BackgroundThe Affordable Care Act was associated with increased enrollment in high-deductible health plans (HDHPs), which resulted in rising health insurance deductibles. Health insurance plans can cover a patient's cost of healthcare once the deductible is met. Patients have been shown to be economic rational decision makers and make decisions based on cost rather than health. They wait for their deductible to be met, typically at the end of the year, then proceed to have costly care. The goal of this study was to evaluate the impact of rising health insurance deductibles on the rate and postoperative outcomes of cochlear implantation and to assess changes by the Tax Cuts and Jobs Act.MethodsTriNetX was used to accumulate summary data on patients who obtained a CI between 2005 and 2022 at the beginning (quarter 1) and the end of the year (quarter 4) from the electronic medical records of 75 healthcare organizations. The trends in average rate of cochlear implantation and resultant postoperative known risks or complications were statistically evaluated.ResultsAfter expansion of HDHPs, the rate of cochlear implantation between quarter 4 (19 cases per year) and quarter 1 (17 cases/year) was similar (p= 0.18). For all patient groups, the case volume significantly increased. Between quarter 4 and quarter 1, postoperative tinnitus was more common in the beginning of the year (risk ratio, 0.68; 95% confidence interval, 0.46–0.99).ConclusionsThe number of patients receiving CIs significantly increased despite the expansion of HDHPs. Tinnitus was a rare postoperative known risk in the beginning of the year. Patients are less likely concerned about cost of CI surgery because of the impact of hearing loss on quality of life.

Entrepreneurship Lock and the Demand for Health Insurance: Evidence from the US Affordable Care Act

Most US workers have health insurance plans sponsored and subsidized by their employers. The US Affordable Care Act (ACA) improved and expanded the availability of non-employer-based health insurance, with protections for pre-existing conditions, guaranteed issue, and community rating in non-group markets. Using National Health Interview Survey data for 2009 to 2018 and a difference-in-differences modeling approach, this study finds that the ACA increased self-employment in 2015 and 2016 among US adults with higher demand for health insurance. The probability of self-employment increased by 1.4 to 1.8 percentage points among adults ages 30 to 64 with at least one pre-ACA declinable condition and no alternative source of health insurance through a spouse’s employer or public programs. However, these effects were short-lived. As uncertainty about the long-term viability of the ACA’s health insurance exchanges increased in 2017 and 2018, the probability of self-employment among individuals with high demand for insurance fell to pre-ACA levels.

Ukrainian migrants’ and war refugees’ admissions to hospital: evidence from the Polish Nationwide General Hospital Morbidity Study, 2014–2022

BackgroundConsidering the rapid influx of Ukrainian migrants and war refugees into Poland, the knowledge of their health condition is becoming increasingly important for health system policy and planning.The aim of the study was to assess war-related changes in the frequency and structure of hospitalizations among Ukrainian migrants and refugees in Poland.MethodsThe study is based on the analysis of hospital admission records of Ukrainian patients, which were collected in the Nationwide General Hospital Morbidity Study from 01.01.2014 to 31.12.2022.ResultsIn the study period, 13,024 Ukrainians were hospitalized in Poland, 51.7% of whom had been admitted to hospital after February 24, 2022. After the war broke out, the average daily hospital admissions augmented from 2.1 to 21.6 person/day. A noticeable increase in the share of women (from 50% to 62%) and children (from 14% to 51%) was also observed. The average age of patients fell from 33.6 ± 0.2 years to 24.6 ± 0.3 years. The most frequently reported hospital events among the migrants until 23.02.2022 were injuries (S00-T98) – 26.1%, pregnancy, childbirth and the puerperium (O00-O99) – 18.4%, and factors influencing health status and contact with health services (Z00-Z99) – 8.4%. After the war started, the incidence of health problems among migrants and war refugees changed, with pregnancy, childbirth and the puerperium (O00-O99) being the most common – 14.9%, followed by abnormal clinical and lab findings (R00-R99) – 11.9%, and infectious and parasitic diseases (A00-B99) – 11.0%.ConclusionsOur findings may support health policy planning and delivering adequate healthcare in refugee-hosting countries.

Telemental Health Utilization in Commercial Health Insurance Plans in the United States From 2010 Through 2019.

Telemental Health Utilization in Commercial Health Insurance Plans in the United States From 2010 Through 2019.

Optimal reimbursement limitation for a health plan

AbstractThe goal of this paper is the assessment of an optimal reimbursement strategy for employer-based health insurance plans (HP), that cover several categories of medical services. Indeed, a health plan may offer several cost-sharing provisions for these categories, and the “Percent Expense Paid” by the plan also known as “Actuarial Value” represents a summary measure of the protection provided. Starting from a wide industrial applied model in insurance for rate-making, the generalized linear models (GLM), we estimate the expected value and variance of the health expenditure for each category. Moreover, different reimbursement rules (e.g. deductibles, co-payments, policy limits, etc.) involve a change of the “Actuarial Value” calculated as the ratio between the benefits paid by the plan (i.e. reimbursement amounts) and the expenses paid by policyholders (i.e. expenditures). The latter is the percentage of expenditure reimbursed by the plan and is sometimes defined in actuarial literature as Indicated Deductible Relativity (IDR); an IDR can be calculated for each category covered by the Health Plan or per policyholder and is a commonly used method for scoring the benefits of health insurance. Hence, we calculate the optimal IDR for each category, using the optimization problem proposed by de by Finetti (Il problema dei pieni, Giornale dell’istituto italiano degli attuari, 1940) in the context of proportional reinsurance. The goal is the minimization of the variance of the total reimbursement of the Health Plan by fixing the total gain. Furthermore, we propose a numerical application to a real dataset, containing observed expenditures of an Italian HP.

Analysing Health Insurance Customer Dataset to Determine Cross-Selling Potential

Health insurance cross-selling refers to the practice of offering additional or complementary insurance products to existing policyholders. Insurance providers leverage cross-selling, offering customers additional policies like dental or life insurance when they already have a basic health insurance plan. This study is conducted to focus on the application of machine learning techniques to predict health insurance cross-selling opportunities among South African customers. The aim of this study is to develop a cross-selling predictive machine learning model that can assist health insurance companies to identify potential customers for cross-selling probabilities. To achieve this goal, a quantitative research methodology is adopted, focusing on extracting a comprehensive dataset of health insurance consumer information and employing various machine learning algorithms using the Python programming language, including Random Forest, K-Nearest Neighbours, XGBoost classifier, and Logistic Regression algorithms to build the cross-selling predictive machine learning model. The experimental results obtained demonstrate the accuracy scores of four different machine learning algorithms trained using 1,000,000 customer dataset with 17 features, logistic regression is considered as the top-performing model. It achieved an accuracy score of 0.83 and an F1 score of 0.91. The analysis indicates that customers aged 30-60, with prior insurance, and longer service history are more likely to buy additional health insurance products. The findings of this research can help health insurers boost revenue by improving their customer targeting and retention strategies.

Mortality after cardiovascular surgery and percutaneous coronary intervention prior to versus during the COVID-19 pandemic

Abstract Background The COVID-19 pandemic disrupted the usual delivery of health care services globally. However, little is known about its specific impact on cardiovascular procedure rates and outcomes. Purpose To compare rates of isolated coronary artery bypass graft (CABG) surgery, isolated open valve surgery (iValve) and percutaneous coronary intervention (PCI), and 30-day all-cause mortality among patients undergoing these procedures before versus during the COVID-19 pandemic. Methods We undertook a retrospective cohort study of patients aged 18 years and older in Ontario, Canada, comparing procedure and outcome event rates between April 1, 2018 and March 31, 2020 (pre-pandemic) with those between April 1, 2020 and March 31, 2022 (pandemic). Procedures were identified from hospital records, available for all patients hospitalized in the province, and were calculated per 100,000 population. Deaths were identified from the Ontario Health Insurance Plan Registered Persons Database. Multivariable logistic regression models were used to compute mortality rates adjusted for relevant sociodemographic and clinical characteristics. Additionally, we estimated the number of procedures needed to result in one excess death as the difference between the two periods divided by the pre-pandemic rate. Results During the study period, 24,823, 7,768 and 86,549 patients underwent CABG, iValve and PCI respectively. Between the pre-pandemic and pandemic periods, CABG, iValve and PCI rates declined 18.8%, 19.3% and 14.7%, respectively (Figure). Overall, the 30-day mortality rates were 1.9%, 2.4% and 2.6% following CABG, iValve and PCI, respectively. Adjusted 30-day mortality after CABG increased from 1.92 per 100 procedures (95% CI 1.69-2.15) pre-pandemic to 2.25 (2.01-2.49) during the pandemic (p=0.043). Adjusted 30-day mortality also increased among patients receiving iValve [from 2.25 (1.78, 2.71) to 3.29 (2.76, 3.82); p=0.016] and PCI [from 2.41 (2.28, 2.54) to 2.74 (2.60, 2.88); p<0.001]. These differences translate to an estimated excess of one death per 303 CABG surgeries, 96 iValve surgeries and 303 PCIs during the pandemic period. Conclusions In Ontario, Canada, CABG, iValve and PCI rates decreased during the COVID-19 pandemic and had not fully returned to pre-pandemic levels by two years following its initiation. While 30-day mortality rates following cardiovascular procedures significantly increased during the pandemic, the incremental number of additional deaths was small.

Estimated Effectiveness of Coadministration of the BNT162b2 BA.4/5 COVID-19 Vaccine With Influenza Vaccine.

No data comparing the estimated effectiveness of coadministering COVID-19 vaccines with seasonal influenza vaccine (SIV) in the community setting exist. To examine the comparative effectiveness associated with coadministering the BNT162b2 BA.4/5 bivalent mRNA COVID-19 vaccine (BNT162b2-biv [Pfizer BioNTech]) and SIV vs giving each vaccine alone. A retrospective comparative effectiveness study evaluated US adults aged 18 years or older enrolled in commercial health insurance or Medicare Advantage plans and vaccinated with BNT162b2-biv only, SIV only, or both on the same day between August 31, 2022, and January 30, 2023. Individuals with monovalent or another brand of mRNA bivalent COVID-19 vaccine were excluded. Same-day coadministration of BNT162b2-biv and SIV; receipt of BNT162b2-biv only (for COVID-19-related outcomes) or SIV only (for influenza-related outcomes) were the comparator groups. For adults aged 65 years or older, only enhanced SIVs were included. COVID-19-related and influenza-related hospitalization, emergency department (ED) or urgent care (UC) encounters, and outpatient visits. Overall, 3 442 996 individuals (57.0% female; mean [SD] age, 65 [16.7] years) were included. A total of 627 735 individuals had BNT162b2-biv and SIV vaccine coadministered, 369 423 had BNT162b2-biv alone, and 2 445 838 had SIV alone. Among those aged 65 years or older (n = 2 210 493; mean [SD] age, 75 [6.7] years; 57.9% female), the coadministration group had a similar incidence of COVID-19-related hospitalization (adjusted hazard ratio [AHR], 1.04; 95% CI, 0.87-1.24) and slightly higher incidence of emergency department or urgent care encounters (AHR, 1.12; 95% CI, 1.02-1.23) and outpatient visits (AHR, 1.06; 95% CI, 1.01-1.11) compared with the BNT162b2-biv-only group. Among individuals aged 18 to 64 years (n = 1 232 503; mean [SD] age, 47 [13.1] years; 55.4% female), the incidence of COVID-19-related outcomes was slightly higher among those who received both vaccines vs BNT162b2-biv alone (AHR point estimate range, 1.14-1.57); however, fewer events overall in this age group resulted in wider CIs. Overall, compared with those who received SIV alone, the coadministration group had a slightly lower incidence of most influenza-related end points (AHR point estimates 0.83-0.93 for those aged ≥65 years vs 0.76-1.08 for those aged 18-64 years). Negative control outcomes suggested residual bias and calibration of COVID-19-related and influenza-related outcomes with negative controls moved all estimates closer to the null, with most CIs crossing 1.00. In this study, coadministration of BNT162b2-biv and SIV was associated with generally similar effectiveness in the community setting against COVID-19-related and SIV-related outcomes compared with giving each vaccine alone and may help improve uptake of both vaccines.

Optimal allocation of physicians improves accessibility and workload disparities in stroke care

BackgroundInequalities in access to stroke care and the workload of physicians have been a challenge in recent times. This may be resolved by allocating physicians suitable for the expected demand. Therefore, this study analyzes whether reallocation using an optimization model reduces disparities in spatial access to healthcare and excessive workload.MethodsThis study targeted neuroendovascular specialists and primary stroke centers in Japan and employed an optimization model for reallocating neuroendovascular specialists to reduce the disparity in spatial accessibility to stroke treatment and workload for neuroendovascular specialists in Japan. A two-step floating catchment area method and an inverted two-step floating catchment area method were used to estimate the spatial accessibility and workload of neuroendovascular specialists as a potential crowdedness index. Quadratic programming has been proposed for the reallocation of neuroendovascular specialists.ResultsThe reallocation of neuroendovascular specialists reduced the disparity in spatial accessibility and the potential crowdedness index. The standard deviation (SD) of the demand-weighted spatial accessibility index improved from 125.625 to 97.625. Simultaneously, the weighted median spatial accessibility index increased from 2.811 to 3.929. Additionally, the SD of the potential crowdedness index for estimating workload disparity decreased from 10,040.36 to 5934.275 after optimization. The sensitivity analysis also showed a similar trend of reducing disparities.ConclusionsThe reallocation of neuroendovascular specialists reduced regional disparities in spatial accessibility to healthcare, potential crowdedness index, and disparities between facilities. Our findings contribute to planning health policies to realize equity throughout the healthcare system.