Celuvra

Documentation

Understand how Celuvra models long-term care risk and compares LTC strategies, or integrate our actuarial engine into your application via the REST API.

Quickstart

Enter your profile and get a complete LTC strategy analysis in under 5 minutes.

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Longevity Risk Engine

SSA period life tables with health-status mortality adjustments. Survival probabilities and conditional longevity.

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Markov Health Model

Four-state Markov chain: Independent, ADL Need, Facility Care, Death. Transition rates from HHS/ASPE.

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Monte Carlo Simulation

10,000 wealth trajectory simulations varying LTC onset age, duration, care intensity, returns, and inflation.

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Strategy Comparison

Buy LTCI vs. self-insure vs. hybrid with expected cost, worst-case, ruin probability, and breakeven.

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Tax & Partnership

IRS LTCI deduction limits, HSA contributions, tax-free benefits, and state LTC Partnership Program modeling.

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API Endpoints

POST/v1/calculate
GET/v1/care-costs/:state
GET/v1/life-table/:age/:sex
POST/v1/signup
POST/v1/login
GET/v1/account
POST/v1/checkout

Authentication

All API requests require an API key passed via the X-API-Key header.

curl https://celuvra-api.smarttechinvest.com/v1/care-costs/california \
  -H "X-API-Key: cel_your_api_key_here"

Longevity Risk Engine

We use SSA Period Life Tables (2024 edition) to compute survival probabilities by age and sex. For each user, we calculate the conditional probability of reaching ages 80, 85, and 90+ -- the ages where LTC need is highest. Health status adjustments use relative mortality multipliers from SOA LTC Experience Studies (smoker/non-smoker, BMI ranges, family history).

Remaining life expectancy at current age drives the premium payment window for LTCI strategies and the investment horizon for self-insurance strategies.

Markov Health State Transition Model

Our four-state Markov chain models transitions between: Independent (no LTC need), Needs Help with ADLs (2+ activities of daily living), Facility Care (nursing home or assisted living), and Death. Transition probabilities are calibrated by age and sex using HHS/ASPE LTSS risk projections and SOA disability incidence rates.

The model computes expected duration in each state and the overall probability of ever entering facility care -- a critical input for the self-insure vs. buy decision.

Monte Carlo Wealth Simulation

For each strategy, we run 10,000 simulations varying six key parameters: age at LTC onset (Markov-derived), duration of need, care intensity level, investment returns (normally distributed around user's expected return), care cost inflation (3-5% range), and LTCI rate increases (for buy strategy).

  • Expected (mean) cost -- the average lifetime LTC cost across all scenarios
  • Worst-case (P95) -- the 95th percentile cost for planning purposes
  • Ruin probability -- the percentage of scenarios where assets reach zero
  • Breakeven age -- the age at which buying LTCI becomes cheaper than self-insuring

Three-Strategy Comparison

We model three distinct strategies and compare them on expected total cost, worst-case cost, probability of financial ruin, and breakeven analysis:

Buy Traditional LTCI

Annual premiums, benefit period, daily benefit, inflation rider, elimination period. Includes rate increase risk modeling.

Self-Insure

Wealth accumulation path with investment returns, minus expected LTC costs. Includes Medicaid spend-down threshold modeling.

Hybrid Life/LTC

Single premium or 10-pay life policy with LTC acceleration rider and extension of benefits. Death benefit if never used.

Tax Efficiency & Partnership Programs

LTCI premiums are tax-deductible up to age-based IRS limits ($5,880 for age 60-70 in 2026). We model the after-tax cost of each strategy based on your income and filing status. For states with LTC Partnership Programs (43 states participate), we model the dollar-for-dollar Medicaid asset protection and compute the effective return on partnership-qualified policies.