Estimating lifetime medical costs from censored claims data

Abstract

Claims databases consisting of routinely collected longitudinal records of medical expenditures are increasingly utilized for estimating expected medical costs of patients with a specific condition. Survival data of the patients of interest are usually highly censored, and observed expenditures are incomplete. In this study, we propose a survival-adjusted estimator for estimating mean lifetime costs, which integrates the product of the survival function and the mean cost function over the lifetime horizon. The survival function is estimated by a new algorithm of rolling extrapolation, aided by external information of age- and sex-matched referents simulated from national vital statistics. The mean cost function is estimated by a weighted average of mean expenditures of patients in a number of months prior to their death, of which the number could be determined by observed costs in their final months, and the weights depend on extrapolated hazards. We evaluate the performance of the proposed approach in comparison with that of a popular method using simulated data under various scenarios and 2 cohorts of intracerebral hemorrhage and ischemic stroke patients with a maximum follow-up of 13 years and conclude that our new method estimates the mean lifetime costs more accurately.

Lifetime risks, loss of life expectancy, and health care expenditures for 19 types of cancer in Taiwan

“…Further extrapolation of survival function was estimated using a rolling extrapolation algorithm, aided by the age- and sex-matched referents simulated from the National Vital Statistics life tables. 11 The EYLL was the difference between LEs of the cancer cohort and those of the age- and sex-matched referents (Supplementary materials). The iSQoL 2 software was used to compute these estimations.…”

“…The estimated lifetime cost was calculated by adding the product of monthly survival rates and monthly mean costs. 11 The monthly mean costs beyond the maximum follow-up were estimated using the mean expenditures of the patients in a specific number of months prior to their death with a weighted average. The number could be decided according to the observed costs in the patients’ last months, where the weights were dependent on the extrapolated hazard of death.…”

“… 9 , 10 Moreover, inter-linkages of these data files can provide opportunities for estimation of lifetime health care expenditures and the demand for long-term care. 11 – 13 The development of these novel methods and big data linkages would make possible a more comprehensive estimation of the societal impact of a specific illness, 7 including cancer.…”

Lifetime risks, expected years of life lost, and cost-per-life year of esophageal cancer in Taiwan

“…where IR i is the incidence rate for the i-th age group and Δt i = 5 year age range. The extrapolation of the survival function was estimated using a rolling extrapolation algorithm, aided by the age-and sex-matched referents simulated from the National Vital Statistics life tables, as detailed in previous literature 19,34 . The method could be briefly summarized as follows: First, for every patient in our cohort, we generated the age-, sex-, and calendar year of diagnosis-matched referents based on the life tables of Taiwan during www.nature.com/scientificreports www.nature.com/scientificreports/ the study period and estimated the Kaplan-Meier's lifetime survival function.…”

“…www.nature.com/scientificreports www.nature.com/scientificreports/ We summed up all the reimbursement costs for every esophageal cancer case and calculated the monthly average cost after diagnosis. The estimated lifetime cost was quantified by adding the product of monthly survival rates and monthly mean costs 34 . Briefly, since we assumed that the medical expenditures spent near mortality increased when the patient with esophageal cancer approached the end of life, we stratified the healthcare costs of those who died during the study period and those who were censored.…”