The next #HEJC discussion will take place Thursday 17th October, at 8am London time. Join the Facebook event here. For more information about the Health Economics Twitter Journal Club and how to take part, click here.
The paper for discussion this month is a working paper published by IZA. The authors are Maja Adena and Michal Myck. The title of the paper is:
“Poverty and transitions in health”
Following the meeting, a transcript of the Twitter discussion can be downloaded here.
Links to the article
Summary of the paper
My interest in discussing ‘Poverty and transitions in health’ by Adena and Myck was driven by my own curiosity in the area of socio-economic determinants of health and well-being more generally. The income based approach to assessing a country’s progress or assessing welfare within nations has faced sustained criticism from a number of quarters, most notably the Commission on the Measurement of Economic Performance and Social Progress (Stiglitz et al. 2009). Even when it comes to assessing poverty, the method of drawing a relative income poverty threshold has faced further scrutiny, most notably from advocates of a multidimensional poverty approach (Alkire & Foster, 2011).
The paper by Adena and Myck is another critique on the relative income approach on assessing wellness, but in a new light – by investigating the ability of relative income to predict future health outcomes in an older population group. The authors argue that old age poverty is one of the key challenges to developed countries, with the demographics of the over 65 expected to make up almost three in every ten EU citizens by 2060 adding concerns about the sustainability of national pension plans. The authors argue that epidemiological research has so far failed to account for the relationship between material conditions and health in the later stages of life to date.
The data applied to investigate this research question were drawn from 12 European countries from a large (n=29,110) European Panel Survey, the Survey for Health, Aging and Retirement in Europe (SHARE). The percentage of the population aged 50-64 at baseline was 53.23% with the remainder 65 and older. Males accounted for 54.69% of the sample. At baseline Wave 2 of the SHARE dataset (year 2006) was used to predict binary outcomes of good or bad health in Wave 4 of the survey (year 2012), which depended on whether or not an individual was in good or bad health at baseline (Wave 2). Three measures of “material circumstances” were applied to predict three measures of ‘health’ in this study (as well as mortality). The three material circumstances measures were:
- Income poverty – 60% of the median equivalised household income
- Subjective poverty – having difficulty to “make ends meet” per month
- Wealth poverty – bottom tertile of country wealth distributions.
The three health measures were:
- Self-assessed health status (SAH) – “fair” or “poor” health status on a five-part scale
- Symptoms of poor health (SMT) – poor if they have 3 or more of 12 symptoms measured
- Limitations in performing activities of daily living (ADL) – poor if they have 3 or more of 23 ADLs.
The authors’ key findings suggest that the “broader measures” of subjective and wealth poverty are more accurately able to predict negative health outcomes than income poverty (in some cases, no relationship was found between income poverty and health outcomes). People who were in bad health in Wave 2 are also less likely to recover if they are classified as subjective or wealth poor in Wave 4. The most striking finding by Adena and Myck is the probability of death when reported as subjectively poor in Wave 2. The probability of dying is 40.3% higher for men and 58.3% higher for all aged between 50-64 years old. The authors conclude by stating that “improvements in material conditions may not only translate into better quality of life but also living longer”
- The method of defining health poverty as 3 problems for the health measures SMT and ADL is reported as arbitrary but common. How common is this practice? No reference to other examples in the paper.
- Should having 3 problems be equivalent irrespective of ‘problem’ under consideration? Might be worth considering literature on ‘core’ poverty methods (Clark & Qizilbash, 2008).
- Sensitivity analysis considered people with two or more problems: I was expecting an analysis which went higher (i.e. four or more problems), especially for ADL.
- Sensitivity analysis would have also been useful for the material measures. This paper shows problems with the 60% median relative income threshold, rather than relative income itself.
- Is the method for defining wealth within a country appropriate for defining “wealth poverty”?
- While the authors touched on qualitative information contained in Wave 3 in the sensitivity analysis, this could warrant future research as to the drivers of changes in health outcomes over time.
- Personally, I did not feel the link to Grossman’s (1972) model on health stock was necessary. Felt the results, if properly presented, could stand up on their own merits.
- Imputation of missing values for income and wealth needed further explanation.
- Related to Footnote 11: why were the result in Hahn et al. (1995) different than what were found in this study? This requires more detailed consideration.
Can’t join in with the Twitter discussion? Add your thoughts on the paper in the comments below.