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About the article
Published Online: 2013-07-31
Published in Print: 2013-01-01
The exception to this is the 1996 MEPS-HC when health insurance benefits booklets were abstracted.
While the issue of financial exposure is pertinent to the Medicare-eligible population as well, it is challenging to contrast those with and without chronic illness, given the high prevalence of chronic conditions in this population. Moreover, comparisons between the Medicare and non-Medicare eligible populations are difficult given the different financing systems for the two groups.
Few families, if any, are fully insured from the risk of medical expenses. While a large amount of attention is placed on differences between the insured and uninsured, we want to focus on variability in financial exposure among those with coverage.
We acknowledge that the restrictions we impose on our sample may limit the extent to which these results may generalize to the under-65, privately insured population overall.
Families that have health insurance that provides less financial exposure may be more likely to seek medical care in which they will be diagnosed with a chronic condition, a possibility that might lead us to understate the differences in the degree of financial exposure from insurance between chronically ill families and non-chronically ill families.
We recoded multi-race families to reflect the less prevalent race in the population.
In the case of the family holding two plans, we sum each plan’s OOP premium to arrive at the family-level measure.
A small percentage of families reports having two policyholders. In this case, we defined the variable as equal to one if either of the policyholder’s had the particular attribute (e.g., worked for a small establishment, private organization, or belonged to a union).
Survey commands in STATA do not allow for explicit clustering to account for repeated observations on families, which is present given the overlapping panel design. When we re-estimated the model without explicit survey commands but utilized analytic weights and allowed for clustering, the standard errors are almost identical. Median regression is not supported by the survey commands. In this case, we used analytic weights with clustering. For robustness, we also considered alternative specifications including OLS regression and a square-root transformation. These results produced qualitatively similar patterns of results and are available from the corresponding author by request.
Approximately 18% of families in the sample have annual total spending in excess of $8000 per year.
A full set of model results is available from the corresponding author by request.
The reader should keep in mind that there is substantial heterogeneity in the plans held within the two groups. Our method aggregates across these plans and cannot speak to the frequency in which the individual plans held by the chronically ill are less generous, only to the average difference.
One might also want to consider longer-term or “lifetime” insurance curves that relate many years or lifetime OOP spending to total spending. The data we use (the MEPS) preclude our constructing these longer-term or lifetime measures. We suspect, however, that there is greater serial correlation in expenditures among the chronically ill than among those with acute conditions (almost by definition). Thus, a longer-term picture using our method would be likely to show an even larger difference in the degree of financial security from insurance between the chronically ill and the non-chronically ill.