US Spending on Personal Health Care and Public Health, 1996-2013.

Importance: US health care spending has continued to increase, and now accounts for more than 17% of the US economy. Despite the size and growth of this spending, little is known about how spending on each condition varies by age and across time. Objective: To systematically and comprehensively estimate US spending on personal health care and public health, according to condition, age and sex group, and type of care. Design and Setting: Government budgets, insurance claims, facility surveys, household surveys, and official US records from 1996 through 2013 were collected and combined. In total, 183 sources of data were used to estimate spending for 155 conditions (including cancer, which was disaggregated into 29 conditions). For each record, spending was extracted, along with the age and sex of the patient, and the type of care. Spending was adjusted to reflect the health condition treated, rather than the primary diagnosis. Exposures: Encounter with US health care system. Main Outcomes and Measures: National spending estimates stratified by condition, age and sex group, and type of care. Results: From 1996 through 2013, $30.1 trillion of personal health care spending was disaggregated by 155 conditions, age and sex group, and type of care. Among these 155 conditions, diabetes had the highest health care spending in 2013, with an estimated $101.4 billion (uncertainty interval [UI], $96.7 billion-$106.5 billion) in spending, including 57.6% (UI, 53.8%-62.1%) spent on pharmaceuticals and 23.5% (UI, 21.7%-25.7%) spent on ambulatory care. Ischemic heart disease accounted for the second-highest amount of health care spending in 2013, with estimated spending of $88.1 billion (UI, $82.7 billion-$92.9 billion), and low back and neck pain accounted for the third-highest amount, with estimated health care spending of $87.6 billion (UI, $67.5 billion-$94.1 billion). The conditions with the highest spending levels varied by age, sex, type of care, and year. Personal health care spending increased for 143 of the 155 conditions from 1996 through 2013. Spending on low back and neck pain and on diabetes increased the most over the 18 years, by an estimated $57.2 billion (UI, $47.4 billion-$64.4 billion) and $64.4 billion (UI, $57.8 billion-$70.7 billion), respectively. From 1996 through 2013, spending on emergency care and retail pharmaceuticals increased at the fastest rates (6.4% [UI, 6.4%-6.4%] and 5.6% [UI, 5.6%-5.6%] annual growth rate, respectively), which were higher than annual rates for spending on inpatient care (2.8% [UI, 2.8%-2.8%] and nursing facility care (2.5% [UI, 2.5%-2.5%]). Conclusions and Relevance: Modeled estimates of US spending on personal health care and public health showed substantial increases from 1996 through 2013; with spending on diabetes, ischemic heart disease, and low back and neck pain accounting for the highest amounts of spending by disease category. The rate of change in annual spending varied considerably among different conditions and types of care. This information may have implications for efforts to control US health care spending.

Health-care spending attributable to modifiable risk factors in the USA: an economic attribution analysis.

BACKGROUND: There is a robust understanding of how specific behavioural, metabolic, and environmental risk factors increase the risk of health burden. However, there is less understanding of how these risks individually and jointly affect health-care spending. The objective of this study was to quantify health-care spending attributable to modifiable risk factors in the USA for 2016. METHODS: We extracted estimates of US health-care spending by condition, age, and sex from the Institute for Health Metrics and Evaluation's Disease Expenditure Study 2016 and merged these estimates with population attributable fraction estimates for 84 modifiable risk factors from the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 to produce estimates of spending by condition attributable to these risk factors. Because not all spending can be linked to health burden, we adjusted attributable spending estimates downwards, proportional to the association between health burden and health-care spending across time and age for each aggregate health condition. We propagated underlying uncertainty from the original data sources by randomly pairing the draws from the two studies and completing our analysis 1000 times independently. FINDINGS: In 2016, US health-care spending attributable to modifiable risk factors was US$730·4 billion (95% uncertainty interval [UI] 694·6-768·5), corresponding to 27·0% (95% UI 25·7-28·4) of total health-care spending. Attributable spending was largely due to five risk factors: high body-mass index ($238·5 billion, 178·2-291·6), high systolic blood pressure ($179·9 billion, 164·5-196·0), high fasting plasma glucose ($171·9 billion, 154·8-191·9), dietary risks ($143·6 billion, 130·3-156·1), and tobacco smoke ($130·0 billion, 116·8-143·5). Spending attributable to risk factor varied by age and sex, with the fraction of attributable spending largest for those aged 65 years and older (45·5%, 44·2-46·8). INTERPRETATION: This study shows high spending on health care attributable to modifiable risk factors and highlights the need for preventing and controlling risk exposure. These attributable spending estimates can contribute to informed development and implementation of programmes to reduce risk exposure, their health burden, and health-care cost. FUNDING: Vitality Institute.

Adjusting health spending for the presence of comorbidities: an application to United States national inpatient data.

BACKGROUND: One of the major challenges in estimating health care spending spent on each cause of illness is allocating spending for a health care event to a single cause of illness in the presence of comorbidities. Comorbidities, the secondary diagnoses, are common across many causes of illness and often correlate with worse health outcomes and more expensive health care. In this study, we propose a method for measuring the average spending for each cause of illness with and without comorbidities. METHODS: Our strategy for measuring cause of illness-specific spending and adjusting for the presence of comorbidities uses a regression-based framework to estimate excess spending due to comorbidities. We consider multiple causes simultaneously, allowing causes of illness to appear as either a primary diagnosis or a comorbidity. Our adjustment method distributes excess spending away from primary diagnoses (outflows), exaggerated due to the presence of comorbidities, and allocates that spending towards causes of illness that appear as comorbidities (inflows). We apply this framework for spending adjustment to the National Inpatient Survey data in the United States for years 1996-2012 to generate comorbidity-adjusted health care spending estimates for 154 causes of illness by age and sex. RESULTS: The primary diagnoses with the greatest number of comorbidities in the NIS dataset were acute renal failure, septicemia, and endocarditis. Hypertension, diabetes, and ischemic heart disease were the most common comorbidities across all age groups. After adjusting for comorbidities, chronic kidney diseases, atrial fibrillation and flutter, and chronic obstructive pulmonary disease increased by 74.1%, 40.9%, and 21.0%, respectively, while pancreatitis, lower respiratory infections, and septicemia decreased by 21.3%, 17.2%, and 16.0%. For many diseases, comorbidity adjustments had varying effects on spending for different age groups. CONCLUSIONS: Our methodology takes a unified approach to account for excess spending caused by the presence of comorbidities. Adjusting for comorbidities provides a substantially altered, more accurate estimate of the spending attributed to specific cause of illness. Making these adjustments supports improved resource tracking, accountability, and planning for future resource allocation.

Assessing the Complex and Evolving Relationship between Charges and Payments in US Hospitals: 1996 - 2012.

BACKGROUND: In 2013 the United States spent $2.9 trillion on health care, more than in any previous year. Much of the debate around slowing health care spending growth focuses on the complicated pricing system for services. Our investigation contributes to knowledge of health care spending by assessing the relationship between charges and payments in the inpatient hospital setting. In the US, charges and payments differ because of a complex set of incentives that connect health care providers and funders. Our methodology can also be applied to adjust charge data to reflect actual spending. METHODS: We extracted cause of health care encounter (cause), primary payer (payer), charge, and payment information for 50,172 inpatient hospital stays from 1996 through 2012. We used linear regression to assess the relationship between charges and payments, stratified by payer, year, and cause. We applied our estimates to a large, nationally representative hospital charge sample to estimate payments. RESULTS: The average amount paid per $1 charged varies significantly across three dimensions: payer, year, and cause. Among the 10 largest causes of health care spending, average payments range from 23 to 55 cents per dollar charged. Over time, the amount paid per dollar charged is decreasing for those with private or public insurance, signifying that inpatient charges are increasing faster than the amount insurers pay. Conversely, the amount paid by out-of-pocket payers per dollar charged is increasing over time for several causes. Applying our estimates to a nationally representative hospital charge sample generates payment estimates which align with the official US estimates of inpatient spending. CONCLUSIONS: The amount paid per $1 charged fluctuates significantly depending on the cause of a health care encounter and the primary payer. In addition, the amount paid per charge is changing over time. Transparent accounting of hospital spending requires a detailed assessment of the substantial and growing gap between charges and payments. Understanding what is driving this divergence and generating accurate spending estimates can inform efforts to contain health care spending.