Adverse selection in a start-up long-term care insurance market

Abstract

Common to all previous studies assessing the cost of adverse selection associated with genetics has been the assumption of an established market, i.e., the adverse selectors have been buying insurance at that rate for such a period that premiums have already absorbed it. Their analyses involve calculating the percentage difference between premiums in a market with adverse selection and one without adverse selection. They can shed no light on how the premiums would get to this stage over time and what losses might be incurred in the process. We take the modelling further by outlining a multiple state Markov model for a start-up market of long-term care insurance. With this model, we explicitly show the progression of adverse selection costs using the development of information that an insurer would gain from analysing the claims history of its existing business, to reprice premiums for new business. To overcome the complication of insurance benefit amounts, which depend on the value of previous benefit payments, we develop a simulation approach of estimating the expected present values of insurance benefits and premium payments. In applying our modelling to a UK setting, we find genetic testing of the apolipoprotein E gene (whose variants can cause a high risk of developing dementia) to be of a relatively small impact compared with our hypothetical state of intermediate dementia progression. Furthermore, we find that the government’s cap on care costs has little effect on adverse selection costs as it benefits only a small proportion of people.

Hospital at Home admission avoidance with comprehensive geriatric assessment to maintain living at home for people aged 65 years and over: a RCT

“…We planned one subgroup analysis of the effect of setting (home vs. hospital) on the incidence of delirium 18 in people who were cognitively impaired (defined as having a MoCA score of < 26). 19 Owing to the small number of participants with delirium, assessed by the CAM, 18 six individual log-Poisson generalised linear mixed models with robust standard errors were fitted to the data, one at each of the three time points for one subgroup with a MoCA score of < 26 and another subgroup with a MoCA score of ≥ 26. The models included site as a random effect.…”

“…Unit costs were obtained from secondary sources for each health and social care service. 18,[22][23][24][25] Appendix 2 provides a list of the unit costs and their sources. Unit costs were inflated to 2017/18 prices, where necessary, using the hospital care and health services inflation index.…”

“…Peter Langhorne reports that he was on the Health Technology Assessment CET Committee and Health Technology Assessment End of Life Care Add on Studies Committee until 2019. David 10 New long-term residential care at 6-and 12-month follow-up (includes those who died in long-term residential care) and the adjusted RRs TABLE 11 Any residential care at the 6-and 12-month follow-ups (includes those who died in long-term residential care) and the adjusted RRs TABLE 12 The presence of delirium (CAM) at 3 days, 5 days and 1 month, and the adjusted relative risk between the randomised arms 15 Activities of daily living (Barthel Index) at 6 months and the adjusted mean difference TABLE 16 Charlson Comorbidity Index score and the Charlson probability at baseline and 6 months TABLE 17 Readmission or transfer to hospital at 1 month and 6 months and the adjusted RR between the randomised groups TABLE 18 The presence of delirium measured by the CAM in the subgroup with a MoCA score of < 26, indicating cognitive impairment TABLE 19 'Living at home' at 6 months and the adjusted RR, including baseline factors that predicted missingness of the primary outcome in the model TABLE 20 'Living at home' at 6 months and the adjusted RR after imputing data missing from the primary outcome as 'living at home and alive' TABLE 21 'Living at home' at 6 months and the adjusted RR after imputing missing primary outcome as 'not living at home and alive/dead' TABLE 22 'Living at home' at 6 months and the adjusted RR with imputed missing primary outcome data using multiple imputation TABLE 23 'Living at home' at 6 and 12 months and the adjusted RR between the randomised groups TABLE 24 Resource use by treatment group from baseline to 1-month follow-up and baseline to 6-month follow-up: complete cases TABLE 25 Costs by treatment group from baseline to 1-month follow-up and baseline to 6-month follow-up: complete cases (costs in GBP, 2017-18 prices)…”

Genetic discrimination in foreign legislation and law-enforcement practice

“…It was in 2017 that the Genetic Discrimination Act was passed by the Parliament of Canada. This document, along with amendments to the Labor Code of Canada and the Canadian Human Rights Act, prohibits companies and employers from demanding the results of genetic tests or participation in genetic testing from employees and applicants [16].…”

Should Australia Ban the Use of Genetic Test Results in Life Insurance?

“…A report prepared for the Actuaries Institute 2017 Summit ( 20 ) asserts that a ban on genetic test results will result in adverse selection, but its claims are arguably based on a set of worst-case assumptions that are unlikely to be met ( 18 ). Independent modeling undertaken elsewhere, including in Canada prior to legislation being passed, indicates that a ban on the use of genetic test results would not have a significant effect on the operation of a reasonably sized life insurance market ( 21 – 23 ).…”