Healthcare utilization and costs among high-need and frail Mexican American Medicare beneficiaries

Objectives To examine Medicare health care spending and health services utilization among high-need population segments in older Mexican Americans, and to examine the association of frailty on health care spending and utilization. Methods Retrospective cohort study of the innovative linkage of Medicare data with the Hispanic Established Populations for the Epidemiologic Study of the Elderly (H-EPESE) were used. There were 863 participants, which contributed 1,629 person years of information. Frailty, cognition, and social risk factors were identified from the H-EPESE, and chronic conditions were identified from the Medicare file. The Cost and Use file was used to calculate four categories of Medicare spending on: hospital services, physician services, post-acute care services, and other services. Generalized estimating equations (GEE) with a log link gamma distribution and first order autoregressive, correlation matrix was used to estimate cost ratios (CR) of population segments, and GEE with a logit link binomial distribution was applied to estimate odds ratios (OR) of healthcare use. Results Participants in the major complex chronic illness segment who were also pre-frail or frail had higher total costs and utilization compared to the healthy segment. The CR for total Medicare spending was 3.05 (95% CI, 2.48–3.75). Similarly, this group had higher odds of being classified in the high-cost category 5.86 (95% CI, 3.35–10.25), nursing home care utilization 11.32 (95% CI, 3.88–33.02), hospitalizations 4.12 (95% CI, 2.88–5.90) and emergency room admissions 4.24 (95% CI, 3.04–5.91). Discussion Our findings highlight that frailty assessment is an important consideration when identifying high-need and high-cost patients.

The Economics of a Bed Shortage: Certificate-of-Need Regulation and Hospital Bed Utilization during the COVID-19 Pandemic

Certificate-of-need (CON) laws are intended to restrain health care spending by limiting the acquisition of duplicative capital and the initiation of unnecessary services. Critics contend that need is difficult to objectively assess, especially considering the risks and uncertainty inherent in health care. We compare statewide bed utilization rates and hospital-level bed utilization rates in bed CON and non-bed CON states during the COVID-19 pandemic. Controlling for other possibly confounding factors, we find that states with bed CONs had 12 percent higher bed utilization rates and 58 percent more days in which more than 70 percent of their beds were used. Individual hospitals in bed CON states were 27 percent more likely to utilize all of their beds. States that relaxed CON requirements to make it easier for hospitals to meet the surge in demand did not experience any statistically significant decreases in bed utilization or number of days above 70 percent of capacity. Nor were hospitals in states that relaxed their CON requirements any less likely to use all their beds. Certificate-of-need laws seem to have exacerbated the risk of running out of beds during the COVID-19 pandemic. State efforts to relax these rules had little immediate effect on reducing this risk.

Change in Pediatric Health Care Spending and Drug Utilization during the COVID-19 Pandemic

Objective: To evaluate how the restrictive measures implemented during the SARS-CoV-2 pandemic have influenced the incidence of the most common children’s diseases and the consumption of medications in 2020 compared to 2019. Methods: We involved all family pediatricians of the local health authority of Latina, from which we requested data of monthly visits in 2019 and 2020 for six common diseases disseminated through droplets and contact, and the territorial and integrative pharmaceutical unit of the area, from which we requested data of the net expenditure regarding the most commonly used drugs at pediatric age. Results: There was significant reduction in the incidence of the evaluated diseases and in the consumption of investigated drugs between 2019 and 2020 in the months when the restrictive measures were in place, with an attenuation of this effect during the months of the gradual loosening of those measures. Conclusion: Nonpharmaceutical intervention measures have caused changes in the diffusion of common pediatric diseases. We believe that the implementation of a reasonable containment strategy, even outside of the pandemic, could positively influence the epidemiology of infectious and allergic diseases in children, and healthcare system spending.

A Comparison of Country’s Cultural Dimensions and Health Outcomes

Research comparing health care systems of countries, with a particular emphasis on health care spending and health care outcomes, has found unexplained differences which are often attributed to the countries’ cultures, yet these cultural dimensions are never completely identified or measured. This study examines if culture predicts a country’s population health, measured as life expectancy and health care spending. Using the Hofstede country-level measures (six dimensions) of culture as independent variables, two regression models to predict life expectancy and per capita health care using 2016 World Bank data were developed. The original data set included 112 countries which was reduced to a final total of 60 due to missing or incomplete data. The first regression model, predicting life expectancy, indicated an adjusted R square of 0.45. The second regression model, predicting per capita health care spending, indicated an adjusted R square of 0.63. The study suggests culture is a predictor of both life expectancy and health care spending. However, by creating a composite measure for all six culture measures, we have not found a significant association between culture and life expectancy and healthcare expenditure. The study is limited by small sample size, differences in geography, climate and political systems. Future research should examine more closely the relative influence of individualism on life expectancy and assumptions about models of socialized medicine.

Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data

Background Decomposing health care spending by disease, type of care, age, and sex can lead to a better understanding of the drivers of health care spending. But the lack of diagnostic coding in outpatient care often precludes a decomposition by disease. Yet, health insurance claims data hold a variety of diagnostic clues that may be used to identify diseases. Methods In this study, we decompose total outpatient care spending in Switzerland by age, sex, service type, and 42 exhaustive and mutually exclusive diseases according to the Global Burden of Disease classification. Using data of a large health insurance provider, we identify diseases based on diagnostic clues. These clues include type of medication, inpatient treatment, physician specialization, and disease specific outpatient treatments and examinations. We determine disease-specific spending by direct (clues-based) and indirect (regression-based) spending assignment. Results Our results suggest a high precision of disease identification for many diseases. Overall, 81% of outpatient spending can be assigned to diseases, mostly based on indirect assignment using regression. Outpatient spending is highest for musculoskeletal disorders (19.2%), followed by mental and substance use disorders (12.0%), sense organ diseases (8.7%) and cardiovascular diseases (8.6%). Neoplasms account for 7.3% of outpatient spending. Conclusions Our study shows the potential of health insurance claims data in identifying diseases when no diagnostic coding is available. These disease-specific spending estimates may inform Swiss health policies in cost containment and priority setting.

Multivariable Prediction Models for Health Care Spending Using Machine Learning: a Protocol of a Systematic Review

Background With rising cost pressures on health care systems, machine-learning (ML) based algorithms are increasingly used to predict health care costs. Despite their potential advantages, the successful implementation of these methods could be undermined by biases introduced in the design, conduct, or analysis of studies seeking to develop and/or validate ML models. The utility of such models may also be negatively affected by poor reporting of these studies. In this systematic review, we aim to evaluate the reporting quality, methodological characteristics, and risk of bias of ML-based prediction models for individual-level health care spending. Methods We will systematically search PubMed and Embase to identify studies developing, updating, or validating ML-based models to predict an individual’s health care spending for any medical condition, over any time period, and in any setting. We will exclude prediction models of aggregate-level health care spending, models used to infer causality, models using radiomics or speech parameters, models of non-clinically validated predictors (e.g. genomics), and cost-effectiveness analyses without predicting individual-level health care spending. We will extract data based on the CHARMS checklist, previously published research, and relevant recommendations. We will assess the adherence of ML-based studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) and examine the inclusion of transparency and reproducibility indicators (e.g. statements on data sharing). To assess the risk of bias, we will apply the Prediction model Risk Of Bias Assessment Tool (PROBAST). Findings will be stratified by study design, ML methods used, population characteristics, and medical field. Discussion Our systematic review will appraise the quality, reporting, and risk of bias of ML-based models for individualized health care cost prediction. This review will provide an overview of the available models and give insights into the strengths and limitations of using ML methods for the prediction of health spending. Trial registration: Not applicable.

Finances of the Nation: The Evolution of Health Expenditures in Canada, 1926-2019

In this article, Livio Di Matteo provides an overview of expenditures on health care in Canada over the long term. He examines changes in the size, relative importance, growth, and composition of health expenditures, with an additional focus on provincial-territorial government spending since the 1970s. Di Matteo links the evolution of health-care spending to factors affecting the demand for and supply of health services, including income, demographic changes, technological development, cost, policy, and public finances.