Predicting carer health effects for use in economic evaluation

Abstract

Background: Illnesses and interventions can affect the health status of family carers in addition to patients. However economic evaluation studies rarely incorporate data on health status of carers.

Objectives: We investigated whether changes in carer health status could be 'predicted' from the health data of those they provide care to (patients), as a means of incorporating carer outcomes in economic evaluation.

Methods: We used a case study of the family impact of meningitis, with 497 carer-patient dyads surveyed at two points. We used regression models to analyse changes in carers' health status, to derive predictive algorithms based on variables relating to the patient. We evaluated the predictive accuracy of different models using standard model fit criteria.

Results: It was feasible to estimate models to predict changes in carers' health status. However, the predictions generated in an external testing sample were poorly correlated with the observed changes in individual carers' health status. When aggregated, predictions provided some indication of the observed health changes for groups of carers.

Conclusions: At present, a 'one-size-fits-all' predictive model of carer outcomes does not appear possible and further research aimed to identify predictors of carer's health status from (readily available) patient data is recommended. In the meanwhile, it may be better to encourage the targeted collection of carer data in primary research to enable carer outcomes to be better reflected in economic evaluation.

Fig 1. Conceptual framework for predicting health effects of treatments on carers.

A causal relationship between patient and carer outcomes is hypothesised, based on what we know about the experience of informal care. However, for the purposes of the modelling, we assume only that there is an association between the two outcomes. Where we anticipate an association (for example between the treatment and ΔH_P, or between the caring situation and ΔH_C) we have used dashed lines.

Fig 2. Distribution of change in patient’s EQ-5D-5L scores (n = 454).

Fig 3. Distribution of change in carer’s EQ-5D-5L score (n = 474).