The well-being valuation model: a method for monetizing the nonmarket good of individual well-being

Prediction of Future Medical Costs by Modifiable Measures of Health

Introduction

Strategies to mitigate rising health-care costs are a priority for patients, employers, and health insurers. Yet gaps currently exist in whether health risk assessment can forecast medical claims costs. This study examined the ability of a health quotient (HQ) based on modifiable risk factors, age, sex, and chronic conditions to predict future medical claims spending.

Methods

The study included 18,695 employees and adult dependents who participated in health assessments and were enrolled in an employer-sponsored health plan. Linear mixed effect models stratified by chronic conditions and adjusted for age and sex were utilized to evaluate the relationship between the health quotient (score of 0-100) and future medical claims spending.

Results

Lower baseline health quotient was associated with higher medical claims cost over 2 years of follow up. For participants with chronic condition(s), costs were $3628 higher for those with a low health quotient (<73; N = 2673) compared to those with high health quotient (>85; N = 1045), after adjustment for age and sex (P value = 0.004). Each one-unit increase in health quotient was associated with a decrease of $154 (95% CI: 87.4, 220.3) in average yearly medical claims costs during follow up.

Discussion

This study used a large employee population with 2 years of follow-up data, which provides insights that are applicable to other large employers. Results of this analysis contribute to our ability to predict health-care costs using modifiable aspects of health, objective laboratory testing and chronic condition status.

Testing the added value of self-reported health and well-being in understanding healthcare utilization and costs

PURPOSE

We investigated the relationship between measures of self-reported health and well-being and concurrent and prospective healthcare utilization and costs to assess the added value of these self-reported measures in understanding utilization and cost.

METHODS

Kaiser Permanente members (N = 6752) completed a 9-item survey measuring life evaluation, financial situation, social support, meaning and purpose, physical health, and mental health. Responses were linked to medical record information during the period 12 months before and after the survey.

RESULTS

Correlations between health and well-being measures and healthcare utilization and cost variables were generally weak, with stronger correlations for future life evaluation and selected health measures (ρ = .20-.33, ps < .001). Better overall life evaluation had a significant but weak association with lower total cost and hospital days in the following year after controlling for age, sex, and race/ethnicity (p < .001). Full multivariate models, adjusting for age, sex, race/ethnicity, prior utilization, and relative risk models, showed weak associations between health and well-being measures and following year total healthcare cost and utilization, though the associations were relatively stronger for the health variables than the well-being variables.

CONCLUSION

Overall, the health and well-being variables added little to no predictive utility for future utilization and cost beyond prior utilization and cost and the inclusion of predictive models based on clinical information. Perceptions of well-being may be associated with factors beyond healthcare utilization. When information about prior use is unavailable, self-reported health items have some predictive utility.

Synergies and Trade-offs among Sustainable Development Goals: The Case of Spain

The United Nations plans have marked global sustainable development for more than two decades. Most of the developed and developing countries have adopted these plans to achieve the Agenda 2030, currently formed by the 17 Sustainable Development Goals (SDGs). The analysis of the interactions between the SDGs is a growing area in research and of interest for governments. However, studies on how positive correlations can improve deteriorated goals are scarce for countries to date. This study aims at filling this gap by finding and quantifying the synergies and trade-offs among the SDGs of Spain. During the years 2000–2019, almost 80% of the SDG targets had significant interactions, either positive (synergy) or negative (trade-off). SDG 4 (quality education), SDG 5 (gender Equality) and SDG 7 (affordable and clean energy) contained the largest number of positive interactions, more than 60% in all of them. SDG 3 (good health and wellbeing) was strongly linked with indicators from SDG 4 (quality education) and also SDG 6 (clean water and sanitation). Furthermore, indicators from SDG 7 (affordable and clean energy) shared a high correlation with the ones from SGD 12 (responsible consumption and production) and SDG 15 (life on land). SDG 1 (no poverty), 2 (zero hunger), 6 (clean water and sanitation), 8 (decent work and economic growth) and 11 (sustainable cities and communities) had the slowest evolution during the years 2000–2019, showing contractions in some instances. We developed a regression model to assess the influence that selected targets have had on a less evolved target (target 8.6—proportion of youth not in education, employment or training). We managed to clarify high influence from target 1.3 (unemployment compensations), target 8.4 (domestic material consumption) and target 10.5 (non-performing loans) on the dependent variable. Identifying numerical dependencies between the SDGs may help nations to develop a roadmap where targets work as cogwheel towards achieving the Agenda 2030.