부산대학교 도서관

Background: The rise in health spending in the United States and the prevalence of multimorbidity—having more than one chronic condition—are interlinked but not well understood. Multimorbidity is believed to have an impact on an individual's health spending, but how having one specific additional condition impacts spending is not well established. Moreover, most studies estimating spending for single diseases rarely adjust for multimorbidity. Having more accurate estimates of spending associated with each disease and different combinations could aid policymakers in designing prevention policies to more effectively reduce national health spending. This study explores the relationship between multimorbidity and spending from two distinct perspectives: (1) quantifying spending on different disease combinations; and (2) assessing how spending on a single diseases changes when we consider the contribution of multimorbidity (i.e., additional/reduced spending that could be attributed in the presence of other chronic conditions). Methods and findings: We used data on private claims from Truven Health MarketScan Research Database, with 16,288,894 unique enrollees ages 18 to 64 from the US, and their annual inpatient and outpatient diagnoses and spending from 2018. We selected conditions that have an average duration of greater than one year among all Global Burden of Disease causes. We used penalized linear regression with stochastic gradient descent approach to assess relationship between spending and multimorbidity, including all possible disease combinations with two or three different conditions (dyads and triads) and for each condition after multimorbidity adjustment. We decomposed the change in multimorbidity-adjusted spending by the type of combination (single, dyads, and triads) and multimorbidity disease category. We defined 63 chronic conditions and observed that 56.2% of the study population had at least two chronic conditions. Approximately 60.1% of disease combinations had super-additive spending (e.g., spending for the combination was significantly greater than the sum of the individual diseases), 15.7% had additive spending, and 23.6% had sub-additive spending (e.g., spending for the combination was significantly less than the sum of the individual diseases). Relatively frequent disease combinations (higher observed prevalence) with high estimated spending were combinations that included endocrine, metabolic, blood, and immune disorders (EMBI disorders), chronic kidney disease, anemias, and blood cancers. When looking at multimorbidity-adjusted spending for single diseases, the following had the highest spending per treated patient and were among those with high observed prevalence: chronic kidney disease ($14,376 [12,291,16,670]), cirrhosis ($6,465 [6,090,6,930]), ischemic heart disease (IHD)-related heart conditions ($6,029 [5,529,6,529]), and inflammatory bowel disease ($4,697 [4,594,4,813]). Relative to unadjusted single-disease spending estimates, 50 conditions had higher spending after adjusting for multimorbidity, 7 had less than 5% difference, and 6 had lower spending after adjustment. Conclusions: We consistently found chronic kidney disease and IHD to be associated with high spending per treated case, high observed prevalence, and contributing the most to spending when in combination with other chronic conditions. In the midst of a surging health spending globally, and especially in the US, pinpointing high-prevalence, high-spending conditions and disease combinations, as especially conditions that are associated with larger super-additive spending, could help policymakers, insurers, and providers prioritize and design interventions to improve treatment effectiveness and reduce spending. Using data from over 16 million people in the United States, Angela Y Chang and colleagues explore how multimorbidity, when considering 63 different conditions, influences health care expenditure. Author summary: Why was this study done?: Many would agree that much health spending is directed towards complex cases that include a combination of multiple chronic conditions, but existing literature estimating disease-specific spending generally fail to systematically account for multimorbidity. Few studies have explored whether different combinations of conditions lead to greater or less spending than the sum of having the diseases separately. What did the researchers do and find?: We used a large claims dataset of over 16 million commercially insurance US working population in 2018 to study the relationship between annual health spending and multimorbidity. We developed a novel approach to adjust spending for each disease for multimorbidity (i.e., estimating the additional/reduced spending that could be attributed in the presence of other conditions) and found that most diseases have higher estimated spending after adjustment. We further found that chronic kidney disease, ischemic heart disease-related heart conditions, cirrhosis, and inflammatory bowel disease are associated with high spending per treated case, high observed prevalence, and contribute the most to spending when in combination with other chronic conditions. What do these findings mean?: Multimorbidity adjustments should be performed for any health spending analysis, otherwise researchers will likely largely underestimate spending for most diseases while overestimating for the remaining diseases. In the midst of a surging health spending globally, and especially in the United States, pinpointing high-prevalence, high-spending conditions and super-additive disease combinations could help policymakers design interventions to improve treatment effectiveness and reduce spending. [ABSTRACT FROM AUTHOR]