Table of Content
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Interventions to strengthen early detection and timely treatment of the more costly diseases, for example, can reduce the need for more expensive treatment and excessive OOP spending downstream. The HRS collects information on a set of doctor-diagnosed chronic health problems, including heart disease, stroke, cancer, diabetes, chronic lung disease, hypertension, arthritis, and major psychiatric problems. Respondents are asked, “Has a doctor ever told you that you have had a ? We construct a dichotomous variable for each chronic disease coded as , where 1 indicates ever having the condition and 0 otherwise. We combine responses for heart disease and stroke to create a CVD dummy.
Data and sample
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For three of the four costliest conditions , prescription drug spending is singularly the most important driver of additional expenses. These findings are consistent with other recently published studies [38–39], and likely due to the extensive use of prescription drugs in disease management, e.g. oral agents or insulin therapy consumed by diabetes patients. A key conclusion is that service drivers of increased spending may be heterogeneous across disease types. Decomposition analysis can thus help health administrators and policymakers target interventions.
Out-of-pocket health spending among Medicare beneficiaries: Which chronic diseases are most costly?
Standard specification tests conducted support the use of the log link and the gamma distribution [29–30]. A series of goodness-of-fit tests further confirm that the fitted models do not have significant specification errors. For each outcome variable, we combine the results of the first and second parts to estimate average spending across different disease types. To ensure generalizability of the study findings to the U.S. population, we take into account the complex sample design of the HRS via individual-level sampling weights in all regressions. Statistical analyses are performed using STATA version 14.0 (STATA Corp., TX, USA).
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Statistical analysis
The prevalence of cancers among persons aged 70–79 is nearly double that of those aged 50–59 . Increasing life expectancy over the last decades also implies that there are more people living to old age, possibly with costly chronic health conditions that require ongoing care and management. Medicare is the biggest health insurance program covering the elderly in the U.S. and provides financial support for persons who have two or more serious chronic conditions that are expected to last at least a year. Among Medicare fee-for-service beneficiaries, those with NCDs account for 93% of total Medicare spending .
A final limitation is that our analyses are cross-sectional and do not take into account dynamic changes in OOP expenditure over time. To put these dollar estimates in context, we also evaluate the spending difference in percentage terms. That is, we divide increased spending by the predicted average spending for persons without that disease . Our results suggest that Medicare beneficiaries with CVD spend 30.5% more than those without CVD.
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The next section describes the HRS data, sample, and variables used in the empirical model. Section 4 presents the estimation results highlighting the effects of chronic conditions, as well as other factors, on OOP spending. Section 5 extends those results by performing a decomposition analysis to pinpoint sources of excess spending for the four costliest chronic conditions. Little is known about the impact of different types of chronic diseases on older adults’ out-of-pocket healthcare spending and whether certain diseases trigger higher spending needs than others.
First, the HRS measures of health status and OOP expenditure data are self-reported. As such, the cost data may be subject to recall bias or incomplete reporting, although in our context, the latter is minimized by the use of the HRS bracketing approach as was described earlier. Second, our evaluation of different disease types is limited to the set of available conditions in the HRS survey. Third, the OOP spending for pharmaceuticals we examined is limited to prescription drugs distributed at licensed pharmacies. Information on expenses relating to over-the-counter drugs is not available in the survey.
Wealth is considered in addition to income because elderly persons may draw on accumulated assets to pay for healthcare. In addition, many may have also exited the labour market at ages past 65. Chronic diseases are on the rise and older adults face the challenge of coping financially with these expensive long-lasting conditions. Recognizing that some chronic conditions may be relatively more costly for individuals in terms of additional OOP spending needs could contribute to more effective targeting of health interventions.
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For instance persons with and without CVD spend $1,689 and $1,224, respectively, on average, while persons with and without cancer spend $1,616 and $1,337, respectively. The difference in means, e.g. $279 [$1616–1337] for cancer, reflects the increased or excess spending among persons with that disease . Before adjusting for any confounding factors, increased spending is largest for CVD ($465), followed by diabetes ($357), chronic lung disease ($323), and finally, cancer ($279). The increased spending of other chronic conditions, e.g. arthritis is $193–270. Excess spending evaluated in percentage terms produce the same ranking. Thus, on an unadjusted basis, the four costliest chronic conditions are identical to WHO’s ‘big four’ NCDs.
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