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A Comparison Between Subjective and Objective Methods of Predicting Health Care Expenses to Support Consumers' Health Insurance Plan Choice.

ABSTRACT: Objective. Numerous electronic tools help consumers select health insurance plans based on their estimated health care utilization. However, the best way to personalize these tools is unknown. The purpose of this study was to compare two common methods of personalizing health insurance plan displays: 1) quantitative healthcare utilization predictions using nationally representative Medical Expenditure Panel Survey (MEPS) data and 2) subjective-health status predictions. We also explored their relations to self-reported health care utilization. Methods. Secondary data analysis was conducted with responses from 327 adults under age 65 considering health insurance enrollment in the Affordable Care Act (ACA) marketplace. Participants were asked to report their subjective health, health conditions, and demographic information. MEPS data were used to estimate predicted annual expenditures based on age, gender, and reported health conditions. Self-reported health care utilization was obtained for 120 participants at a 1-year follow-up. Results. MEPS-based predictions and subjective-health status were related (P < 0.0001). However, MEPS-predicted ranges within subjective-health categories were large. Subjective health was a less reliable predictor of expenses among older adults (age × subjective health, P = 0.04). Neither significantly related to subsequent self-reported health care utilization (P = 0.18, P = 0.92, respectively). Conclusions. Because MEPS data are nationally representative, they may approximate utilization better than subjective health, particularly among older adults. However, approximating health care utilization is difficult, especially among newly insured. Findings have implications for health insurance decision support tools that personalize plan displays based on cost estimates.

SUBMITTER: Politi MC

PROVIDER: S-EPMC6124924 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:INTRODUCTION:In South Korea, Health Insurance Review and Assessment claims data contain comprehensive information on healthcare services for almost the entire population. The present study used claims data on parasitic diseases from 2011 to 2018, and associated medical expenses to investigate infection trends associated with endemic parasitic diseases in South Korea, including those not monitored by Korea Centers for Disease Control and Prevention. METHODS:Basic data regarding each parasitic disease were curated from the Healthcare Bigdata Hub (http://opendata.hira.or.kr). Ten endemic parasitic diseases, three pandemic protozoan diseases, and three ectoparasitic diseases were evaluated between 2011 and 2018. Data on each parasitic disease included the number of patients of each sex, age range within 5 years, province, and total medical expenses. Heatmap and principal component analysis were performed to visualize the incidence pattern of parasitic diseases by provinces. RESULTS:Clonorchiasis and pinworm infections decreased remarkably from 6,097 and 4,018 infections in 2011 to 3,008 and 1,988 infections in 2018, respectively. Other endemic parasitic diseases mostly declined or remained steady over the 8-year period, except for anisakiasis, which doubled from 409 in 2011 to 818 in 2018. Provinces close to North Korea had a higher frequency of claims for Plasmodium vivax infection. The highest rate of clonorchiasis was in Gyeongsangnam-do, while that of anisakiasis was in southern Korea. Jeju province had the highest number of claims for cysticercosis, anisakiasis, pinworm infection, and soil-transmitted helminth infections. The total medical expense for anisakiasis was 65 million Korean won (57,000 US$) in 2011, rising to 237 million Korean won (206,000 US$) in 2018. The medical expense for trichomoniasis was 6,063 million won and for scabies was 1,669 million won in 2018. Since the claims data include only data reported by healthcare providers, some discrepancies might have occurred. CONCLUSION:Our findings provide the basis for a health policy to reduce further infections and medical expense.

Project description:BackgroundObjective health measures, such as registered illnesses or frailty, predict mortality and institutionalization in older adults. Also, self-reported assessment of health by simple self-rated health (SRH) has been shown to predict mortality and institutionalization. The aim of this study was to assess the association of objective and subjective health with mortality and institutionalization in Finnish community-dwelling older adults.MethodsIn this prospective study with 10- and 18-year follow-ups, objective health was measured by registered illnesses and subjective health was evaluated by simple SRH, self-reported walking ability (400 m) and self-reported satisfaction in life. The participants were categorized into four groups according to their objective and subjective health: 1. subjectively and objectively healthy, 2. subjectively healthy and objectively unhealthy, 3. subjectively unhealthy and objectively healthy and 4. subjectively and objectively unhealthy. Cox regression model was used in the analyses. Death was used as a competing factor in the institutionalization analyses.ResultsThe mean age of the participants (n = 1259) was 73.5 years (range 64.0-100.0). During the 10- and 18-year follow-ups, 466 (37%) and 877 (70%) died, respectively. In the institutionalization analyses (n = 1106), 162 (15%) and 328 (30%) participants were institutionalized during the 10- and 18-year follow-ups, respectively. In both follow-ups, being subjectively and objectively unhealthy, compared to being subjectively and objectively healthy, was significantly associated with a higher risk of institutionalization in unadjusted models and with death both in unadjusted and adjusted models.ConclusionsThe categorization of objective and subjective health into four health groups was good at predicting the risk of death during 10- and 18-year follow-ups, and seemed to also predict the risk of institutionalization in the unadjusted models during both follow-ups. Poor subjective health had an additive effect on poor objective health in predicting mortality and could therefore be used as part of an older individual's health evaluation when screening for future adverse outcomes.

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A Comparison Between Subjective and Objective Methods of Predicting Health Care Expenses to Support Consumers' Health Insurance Plan Choice.

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