A methodology to monitor the changing trends in health status of an elderly person by developing a markov model

Current recommendations on the estimation of transition probabilities in Markov cohort models for use in health care decision-making: a targeted literature review

OBJECTIVE

Although Markov cohort models represent one of the most common forms of decision-analytic models used in health care decision-making, correct implementation of such models requires reliable estimation of transition probabilities. This study sought to identify consensus statements or guidelines that detail how such transition probability matrices should be estimated.

METHODS

A literature review was performed to identify relevant publications in the following databases: Medline, Embase, the Cochrane Library, and PubMed. Electronic searches were supplemented by manual-searches of health technology assessment (HTA) websites in Australia, Belgium, Canada, France, Germany, Ireland, Norway, Portugal, Sweden, and the UK. One reviewer assessed studies for eligibility.

RESULTS

Of the 1,931 citations identified in the electronic searches, no studies met the inclusion criteria for full-text review, and no guidelines on transition probabilities in Markov models were identified. Manual-searching of the websites of HTA agencies identified ten guidelines on economic evaluations (Australia, Belgium, Canada, France, Germany, Ireland, Norway, Portugal, Sweden, and UK). All identified guidelines provided general guidance on how to develop economic models, but none provided guidance on the calculation of transition probabilities. One relevant publication was identified following review of the reference lists of HTA agency guidelines: the International Society for Pharmacoeconomics and Outcomes Research taskforce guidance. This provided limited guidance on the use of rates and probabilities.

CONCLUSIONS

There is limited formal guidance available on the estimation of transition probabilities for use in decision-analytic models. Given the increasing importance of cost-effectiveness analysis in the decision-making processes of HTA bodies and other medical decision-makers, there is a need for additional guidance to inform a more consistent approach to decision-analytic modeling. Further research should be done to develop more detailed guidelines on the estimation of transition probabilities.

Measuring change in health status of older adults at the population level: the transition probability model

Background

The current demographic transition will lead to increasing demands on health services. However, debate exists as to the role age plays relative to co-morbidity in terms of health services utilization. While age has been identified as a critical factor in health services utilization, health services utilization is not simply an outcome of ill health, nor is it an inevitable outcome of aging. Most data on health service utilization studies assess utilization at one point in time, and does not examine transitions in health service utilization. We sought to measure health services utilization and to investigate patterns in the transition of levels of utilization and outcomes associated with different levels of utilization.

Methods

We conducted a population-based retrospective cohort study of all Ontario residents aged 65+ eligible for public healthcare coverage from January 1998-December 2006. The main outcome measure was total number of utilization events. The total is computed by summing, on a per annum basis, the number of family physician visits, specialist visits, Emergency Department visits, drug claims, lab claims, X-rays, CT scans, MRI scans, and inpatient admissions. Three categories of utilization were created: low, moderate, and high.

Results

There is heterogeneity in health services utilization across the late lifespan. Utilization increased consistently in the 9-year study period. The probability of remaining at the high utilization category when the person was in the high category the previous year was more than 0.70 for both males and females and for all age groups. Overall healthcare utilization increases more rapidly among the high users compared to the low users. There was negligible probability for moving from high to low utilization category. Probability of death increased exponentially as age increased. Older adults in the low utilization category had the lowest probability of death. The number of male nonagenarians increased more rapidly than female nonagenarians.

Conclusion

There are measurable and identifiable differences in the patterns of health services utilization among older adults. This data will permit clinicians and policy makers to tailor interventions appropriate to the risk class of patients.