Investigating determinants of catastrophic health spending among poorly insured elderly households in urban Nigeria

By our study’s age-cut off definition of 50 years and above, we expect the elderly population estimate to be higher.

We take the position that ageing is likely to begin from 50 years old for urban Nigerians to prevent losing valuable insights on elderly groups in Nigeria. The convention is to adopt the criterion in many western literature of using 60 or 65 years to define who the elderly are. Apart from a few recent epidemiological studies and the WHO reports on Ageing in Africa – most studies follow convention. However, there is contrary evidence to support a lower ageing cut-off point for countries with low life expectancies. Key stakeholders in the WHO’s minimum data set project (1995–2003) presented strong arguments for the development of a separate criterion for Africa and suggested 50 or 55 years as the cut-off point for the elderly [74]. This communiqué continues to influence the WHO’s research studies on ageing in Africa (for instance, The World Health Organization (WHO)'s Study on Global Ageing and Adult Health (SAGE) – a longitudinal study of ageing and older adults). Moreover, in comparison with other LMICs, Nigeria’s life expectancy is still relatively low at 48 years old. This provides a strong basis for applying an age -adjustment for any study of elderly people in Nigeria. Our preliminary data analysis revealed similar physical functioning characteristics of those aged 50–59 and 60 and above in the NGHPS 2010 data adding more support for the definition of contextual definition of ageing in Africa in line with the WHO’s findings.

Results on the alternative specifications of CHE thresholds of 40 % of non-food expenditure can be furnished upon request.

Past research on the relationship between household size and CHE report mixed results. On one hand, some studies argue that larger households will incur larger health out-of-pocket payments because there is higher health demand amongst this group. This demand-side effect may push health spending higher to resulting to CHE. Therefore, in this case these larger households are more likely to report high levels of health spending leading to CHE. There is also another possible explanation: larger households may have fewer resources to spend on health in the first place because they are too poor to afford health care, and in this case are more likely to divert resource to subsistence rather than seek medical care therefore incurring low levels of health spending. We tested both scenarios. We do not present full results here but larger households (those with household size of greater than the average of 8) incurred low levels of health spending making them less likely to incur CHE in comparison to smaller households. This difference between the mean OOP for bigger and smaller households is significant (p = 0.0086). However, poorer elderly households tend to have higher household sizes in comparison to richer elderly households, and we found this difference to be statistically significant (p =0.0001).