Dataset Information

Incorporating self-reported health measures in risk equalization through constrained regression.

ABSTRACT: Most health insurance markets with premium-rate restrictions include a risk equalization system to compensate insurers for predictable variation in spending. Recent research has shown, however, that even the most sophisticated risk equalization systems tend to undercompensate (overcompensate) groups of people with poor (good) self-reported health, confronting insurers with incentives for risk selection. Self-reported health measures are generally considered infeasible for use as an explicit 'risk adjuster' in risk equalization models. This study examines an alternative way to exploit this information, namely through 'constrained regression' (CR). To do so, we use administrative data (N = 17 m) and health survey information (N = 380 k) from the Netherlands. We estimate five CR models and compare these models with the actual Dutch risk equalization model of 2016 which was estimated by ordinary least squares (OLS). In the CR models, the estimated coefficients are restricted, such that the under-/overcompensation for groups based on self-reported general health is reduced by 20, 40, 60, 80, or 100%. Our results show that CR can improve outcomes for groups that are not explicitly flagged by risk adjuster variables, but worsens outcomes for groups that are explicitly flagged by risk adjuster variables. Using a new standardized metric that summarizes under-/overcompensation for both types of groups, we find that the lighter constraints can lead to better outcomes than OLS.

SUBMITTER: Withagen-Koster AA

PROVIDER: S-EPMC7214515 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Incorporating self-reported health measures in risk equalization through constrained regression.

Withagen-Koster A A AA van Kleef R C RC Eijkenaar F F

The European journal of health economics : HEPAC : health economics in prevention and care 20200108 4

Most health insurance markets with premium-rate restrictions include a risk equalization system to compensate insurers for predictable variation in spending. Recent research has shown, however, that even the most sophisticated risk equalization systems tend to undercompensate (overcompensate) groups of people with poor (good) self-reported health, confronting insurers with incentives for risk selection. Self-reported health measures are generally considered infeasible for use as an explicit 'ris ...[more]

PMID: 31916028

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Project description:BackgroundHealth literacy (HL) and numeracy are measured by one of two methods: performance on objective tests or self-report of one's skills. Whether results from these methods differ in their relationship to health outcomes or use of health services is unknown.MethodsWe performed a systematic review to identify and evaluate articles that measured both performance-based and self-reported HL or numeracy and examined their relationship to health outcomes or health service use. To identify studies, we started with an AHRQ-funded systematic review of HL and health outcomes. We then looked for newer studies by searching MEDLINE from 1 February 2010 to 9 December 2014. We included English language studies meeting pre-specified criteria. Two reviewers independently assessed abstracts and studies for inclusion and graded study quality. One reviewer abstracted information from included studies while a second checked content for accuracy.ResultsWe identified four "fair" quality studies that met inclusion criteria for our review. Two studies measuring HL found no differences between performance-based and self-reported HL for association with self-reported outcomes (including diabetes, stroke, hypertension) or a physician-completed rheumatoid arthritis disease activity score. However, HL measures were differentially related to a patient-completed health assessment questionnaire and to a patient's ability to interpret their prescription medication name and dose from a medication bottle. Only one study measured numeracy and found no difference between performance-based and self-reported measures of numeracy and colorectal cancer (CRC) screening utilization. However, in a moderator analysis from the same study, performance-based and self-reported numeracy were differentially related to CRC screening utilization when stratified by certain patient-provider communication behaviors (e.g., the chance to always ask questions and get the support that is needed).DiscussionMost studies found no difference in the relationship between results of performance-based and self-reported measures and outcomes. However, we identified few studies using multiple instruments and/or objective outcomes.

Project description:ImportanceRecent evidence suggests that social determinants of health (SDOH) affect vision loss, but it is unclear whether estimated associations differ between clinically evaluated and self-reported vision loss.ObjectiveTo identify associations between SDOH and evaluated vision impairment and to assess whether these associations hold when examining self-reported vision loss.Design, setting, and participantsThis population-based cross-sectional comparison included participants 12 years and older in the 2005 to 2008 National Health and Nutrition Examination Survey (NHANES), participants of all ages (infants and older) in the 2019 American Community Survey (ACS), and adults 18 years and older in the 2019 Behavioral Risk Factor Surveillance System (BRFSS).ExposuresFive domains of SDOH that are based on Healthy People 2030: economic stability, education access and quality, health care access and quality, neighborhood and built environment, and social and community context.Main outcomes and measuresPresenting vision impairment of 20/40 or worse in the better-seeing eye (NHANES) and self-reported blindness or serious difficulty seeing, even with glasses (ACS and BRFSS).ResultsOf 3 649 085 included participants, 1 873 893 were female (51.1%) and 2 504 206 were White (64.4%). SDOH across domains of economic stability, educational attainment, health care access and quality, neighborhood and built environment, and social context were significant predictors of poor vision. For example, higher income (poverty to income ratio [NHANES]: OR, 0.91; 95% CI, 0.85-0.98; [ACS]: OR, 0.93; 95% CI, 0.93-0.94; categorical income [BRFSS:<$15 000 reference]: $15 000-$24 999; OR, 0.91; 95% CI, 0.91-0.91; $25 000-$34 999: OR, 0.80; 95% CI, 0.80-0.80; $35 000-$49 999: OR, 0.71; 95% CI, 0.71-0.72; ≥$50 000: OR, 0.49; 95% CI, 0.49-0.49), employment (BRFSS: OR, 0.66; 95% CI, 0.66-0.66; ACS: OR, 0.55; 95% CI, 0.54-0.55), and owning a home (NHANES: OR, 0.85; 95% CI, 0.73-1.00; BRFSS: OR, 0.82; 95% CI, 0.82-0.82; ACS: OR, 0.79; 95% CI, 0.79-0.79) were associated with lower odds of vision loss. The study team identified no differences in the general direction of the associations when using either clinically evaluated or self-reported vision measures.Conclusions and relevanceThe study team found evidence that associations between SDOH and vision impairment track together when using either clinically evaluated or self-reported vision loss. These findings support the use of self-reported vision data in a surveillance system to track trends in SDOH and vision health outcomes within subnational geographies.

Dataset Information

Incorporating self-reported health measures in risk equalization through constrained regression.

Publications

Incorporating self-reported health measures in risk equalization through constrained regression.

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