Journal round-up: Health Economics 30(7)

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Papers in this edition of Health Economics seem to align into two themes; intergenerational health effects and COVID-19 related health research. As we are now more than a year since the pandemic began, data are more mature and are now allowing more careful epidemiological and health research. One paper not related to either of the themes that I wished to highlight presented an approach to estimating optimal willingness-to-pay thresholds for cost-effectiveness analysis.

Estimating optimal willingness to pay thresholds for cost-effectiveness analysis: A generalized method

This letter elucidated the use of cost-effectiveness analysis in decision making within a utility maximization framework. This may be of interest when thinking about how a cost-effectiveness threshold could be set based on demand for health.

The authors proposed a general method using hyperbolic absolute risk aversion utility functions to analyse the trade-off between health and consumption at a societal level. As with all studies trying to find the optimal cost-effectiveness threshold empirically, the devil is in the details of the measurement, in this case of rather abstract input parameters related to risk tolerance. The results report a central range of 1-3.3x per capita GDP as an appropriate threshold. If the method were to be adopted, careful validation of the risk tolerance estimation would certainly be needed given the limited source data.

The presence of care homes and excess deaths during the COVID-19 pandemic: Evidence from Italy

In the COVID-19 category, there was a study reporting higher levels of excess deaths during the first phase of the pandemic in 2020 in municipalities of Lombardy with care homes compared to municipalities without.

Excess deaths were defined as the difference between the recorded daily or total deaths in 2020 compared to the average of the previous five years. A spatial autoregressive regression model, applied to three separate age groups (70+, 50-69 and 0-49), was used to estimate the effect of care home presence on excess deaths. Graphical analysis similar to that often used in regression discontinuity studies presented evidence that differences are only found after the first reported COVID-19 cases occur.

Care home presence in a municipality appears to increase excess deaths during the pandemic’s first wave by a substantial degree. However, I would urge major caution in interpreting this result as a causal effect of care homes. The lack of the ability to adjust for differences in the level of co-morbidity among the elderly population, the use of age-stratification with coarse categories at older ages, and the only age-related covariate being % of the population aged over 70, means that the analysis is unlikely to be sufficient to capture between-municipality differences in age distribution or health. This is vital given that the exponential increase in the risk of death due to COVID-19 continues into the 8th and 9th decades of life. A further problem with lacking more fine-detail age and comorbidity data is that it is impossible to adjust for differences arising due to people moving municipality in order to move into care homes. This means that care homes may simply be a better indicator of the presence of a population particularly vulnerable to COVID-19, and not that people who would otherwise be residents of care homes would have been better off in another setting (if this is available).

Intergenerational health mobility: Magnitudes and Importance of Schools and Place

Intergenerational economic mobility is a well-known and well-researched phenomenon. Intergenerational health mobility (or its inverse, ‘persistence’) is less well understood. Existing literature suggested a moderately strong correlation (0.2-0.3) between health in one generation and the health of their descendants. However, the data available to explore the phenomenon have been limited.

The authors of this US-based study used the National Longitudinal Study of Adolescent to Adult Health (Add Health) to further investigate the subject. A number of potential modifiers of intergenerational health mobility were explored: race/ethnicity, health insurance, parental education, and geography by participating school (related to the design of the survey).

The outcome of interest was self‐rated health status as measured on a five‐point Likert scale. This was converted to a 0-100 index using the HALex health index and averaged over all waves of the survey. Persistence was estimated by OLS regression of parental self-rated health on child self-rated health. Heterogeneity of persistence/mobility by the various proposed modifiers was explored by stratification of the analysis by each modifying factor.

Persistence of self-rated health in the overall sample was estimated to be 0.17, somewhat lower than in previous studies. Heterogeneity in persistence was observed geographically (by school) which may be related to local race/ethnicity composition, the proportion of single parents, and mother’s education. However, this evidence is relatively uncertain given the limited sample size. Replication of findings in another sample would be useful to confirm these results. A more explicit consideration of causal pathways may also be useful, particularly to communicate this type of research with epidemiology and medical researchers.


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