Revisiting club convergence in healthcare expenditure and its drivers in the United States

Effect of the Arizona tobacco control program on cigarette consumption and healthcare expenditures.

본 연구는 주성분분석을 활용하여 1995-2009년 동안의 보건의료비 지출을 소득수준별로 일인당 지출, 민간부문 지출, 공공부문 지출 및 총지출로 구분하여 사회후생 증대 효과를 분석하였다. 분석결과에 의하면 소득수준별 보건의료비 지출의 사회후생 증대효과는 세계전체 소득과 우리나라의 경우 부문별 지출에 따라 편차가 크고, 중간소득 국가에서 가장 크게 나타났으며, 고소득 국가, 저소득 국가, EU 국가 및 OECD 국가는 낮은 수준의 유사한 형태를 보이고 있다. 한편, 보건의료비 지출을 부문별로 살펴보면 일인당 지출은 소득수준과 무관하게 사회후생 증대효과가 낮은 것으로 나타났고, 민간부문 지출은 부문별 지출 중에서 사회후생 증대효과가 가장 크게 나타났다. 공공부문 지출과 총지출의 경우 중간소득 국가가 크게 나타났으며 소득수준별로 편차가 큰 특징을 나타내고 있다. 결과적으로 보건의료비 지출의 효율성을 제고하여 사회후생 증대에 기여할 수 있는 방안을 모색하는 것이 가장 중요한 현안이라 하겠다. 이를 위해서는 무엇보다도 보건의료비 지출이 공공성의 특성을 벗어나 시장기능에 의한 자원배분을 할 수 있는 제도적 지원 방안이 모색되어야 할 것이다. Health care expenditure might be understood as a core source of social welfare. It is mainly due to the fact that the health care expenditure is closely related to the level of social welfare. And, therefore, the paper is basically designed to investigate the effect of social welfare improvement resulted from an increasing in per capita, private, public, and total health care expenditures during 1995-2009 periods. Empirical evidence reveals that the improvement of social welfare in middle income group reveals the highest improvement in social welfare, and the other income groups maintain a quite similar pattern in improvement of social welfare. As far as category of health care expenditure concerns, the effect of social welfare improvement by per capita health care expenditure is in general lower in all case. And, private sector achieves the highest improvement of social welfare in comparing with public and total health care expenditures. To this end, it could be tentatively concluded that the private health care expenditure has to be substituted for the public sector to induce market-oriented operational system. Accordingly, it would be better to build an institutional basis more elaborately in setting up the market system.

In response to rising health care costs, many have called for more effective regional health policy coordination. In this paper, we address the issue by examining the degree of convergence in per capita health care expenditure and its nine components across the US states from 1980 to 2004. The major finding is the moderate evidence of convergence in total health care expenditure and the diverse performance of the expenditure components regarding convergence. We also find hospital care to be responsible for the bulk of cross-state convergence in total expenditure. The expenditure on prescription drugs is the most important diverging factor. Policy implications of these empirical results are discussed.

Exploring racial and ethnic disparities in prescription drug spending and use among medicare beneficiaries

BackgroundPrevious research has shown that tobacco control funding in California has reduced per capita cigarette consumption and per capita healthcare expenditures. This paper refines our earlier model by estimating the effect of California tobacco control funding on current smoking prevalence and cigarette consumption per smoker and the effect of prevalence and consumption on per capita healthcare expenditures. The results are used to calculate new estimates of the effect of the California Tobacco Program.Methodology/Principal FindingsUsing state-specific aggregate data, current smoking prevalence and cigarette consumption per smoker are modeled as functions of cumulative California and control states' per capita tobacco control funding, cigarette price, and per capita income. Per capita healthcare expenditures are modeled as a function of prevalence of current smoking, cigarette consumption per smoker, and per capita income. One additional dollar of cumulative per capita tobacco control funding is associated with reduction in current smoking prevalence of 0.0497 (SE.00347) percentage points and current smoker cigarette consumption of 1.39 (SE.132) packs per smoker per year. Reductions of one percentage point in current smoking prevalence and one pack smoked per smoker are associated with $35.4 (SE $9.85) and $3.14 (SE.786) reductions in per capita healthcare expenditure, respectively (2010 dollars), using the National Income and Product Accounts (NIPA) measure of healthcare spending.Conclusions/SignificanceBetween FY 1989 and 2008 the California Tobacco Program cost $2.4 billion and led to cumulative NIPA healthcare expenditure savings of $134 (SE $30.5) billion.

Abstract. The United States ranks third in 2013 among the nations of the world in per capita health care expenditures. However, there is wide variation in health care across states. paper explores factors that influence the per capita outlays in health care across the United States between 2000 and 2009. A Spatial Durbin Panel Model is used to account for the possibility that the health care expenditures of any particular state may influence health care expenditure patterns in neighboring states in the same way. Results indicate that, apart from the presence of positive spatial dependence in health care across the states, variables such as a state's gross domestic product (GDP), Medicaid expenditures, proportion of the population that is elderly, number of active physicians per 100,000 people, and poverty rate positively influence per capita state-level health expenditures. GDP (by state), proportion of population above age 65, and poverty rate negatively affect the neighboring states' per capita health expenditures. Furthermore, the number of hospital beds per 1,000 people and number of hospitals per 1,000 people positively influence bordering states' per capita health expenses.(ProQuest: ... denotes formulae omitted.)1. IntroductionIn 2010, the United States spent 18% of its GDP ($2.6 trillion) on health care (Bipartisan Policy Center, 2012). was a significantly higher proportion than other major industrialized nations spent in 2010, including the United Kingdom (9.6% of its GDP), Germany (11.6%), and Japan (9.5%) (Bipartisan Policy Center, 2012). In 1960, the United States spent 5% of its GDP on health care, which grew to 16% in 2004 and then to 17% in 2009 (Health at a Glance, OECD Indicators, 2011). Thus, it can be seen that within the last 50 years, the total United States health care expenditures as a share of GDP has more than tripled. Also, health care expenditures have grown 2% faster than the U.S. GDP over the past 22 years (U.S. Healthcare Cost, Report on Healthcare Spending, 2013). Furthermore, the United States spends twice as much per capita on health care expenditures as any other advanced nation in the world (Rugy, 2013). Although this growth has declined in recent years (Roehrig et al., 2012), it has been predicted that health will reach 19.6% of the GDP by 2016 (Poisal et al., 2007) and 20% of the GDP by 2021 (Bipartisan Policy Center, 2012). Hall and Jones (2007) predicted that on health care is likely to increase to over 30% of GDP by the year 2050.Despite ranking at the top of the list in spending, the United States health care system ranks thirty-seventh in the world (World Health Report, 2000). Among the OECD countries studied in the National Vital Statistics Report by MacDorman et al. (2014), the United States has the highest prevalence of infant mortality. It lacks in many measures of health care outcomes and quality (Bipartisan Policy Center, 2012). Therefore, it is evident that improvement in the quality of health care has not paralleled the growth exhibited in health care expenses. As stated in the report by the Bipartisan Policy Center (2012, p. 4), This rapid growth in health expenditures is creating an unsustainable burden on America's economy, with far-reaching consequences. Due to the presence of such problems and mismatch with spending, it is necessary to carefully examine the structural aspects of the health care system across the states that contribute to inefficiency and wasteful spending (Bipartisan Policy Center, 2012, p. 4).To understand the factors that result in health expenditure variations across the United States, it is important to frame health policies in ways that not only limit cost growth but also prevent decline in the quality of health care (Martin et al., 2002). It will help to control the factors that led to such growth in the cost structure of the health sector and reduce the waste of the economy's output by reallocating it to other sectors. …

PurposeThe main purpose of this paper is to examine the role of population aging in determining the health care expenditure (HCE) in India over the period 1981 to 2018.Design/methodology/approachWhile establishing the linkage between population aging and HCE, the study has used economic growth, urbanization and CO2 emissions as control variables and used the autoregressive distributed lag (ARDL) approach to cointegration and VECM based Granger causality approach to estimate both the long-run and short-run relationships among the variables.FindingsThe results of the ARDL bounds test showed that there is a stable and long-run relationship among the variables. The long-run and short-run coefficients reveal that population aging and income per capita exert a statistically significant and positive effect on per capita HCE in India. The VECM causality evidence shows that there is a presence of short-run causality from economic growth and population aging to per capita HCE, urbanization to environmental degradation and further from aging to urbanization. However, the long-run causality evidence confirms unidirectional causality from population aging to the per capita HCE.Research limitations/implicationsThe research findings could be improved by considering the changes in mortality rate over time because of other environmental factors such as air pollution, among others as control variables. Various other variables affecting the health of an aged person could be considered for better research outcome which is not included in the present study because of the paucity of data. However, the present research findings would certainly serve effective policy instrument aiming at maximizing health gains that are highly associated with the elderly population and economic growth towards achieving sustainable development in India.Originality/valueThe uniqueness of the present study lies in its estimation where the relationship between population aging and HCE is looked at while considering the impact of other environmental factors separately. The causal relationship is shown among the variables using updated econometrics time-series techniques. The study tried to resolve the ambiguity associated with the relationship between aging and HCE at a macro level.

The evidence found in most studies suggests a strong positive relationship between health care expenditure and gross domestic product. However, this evidence weakens with respect to the actual value of the income elasticity. There are two possible sources of these discrepancies, the use of arbitrary deflators and specification errors. We find that health PPP cannot be taken as a 'universal' price index. The problem is that its components do not move together. Nevertheless, we derive a 'universal' health price index from a dynamic system in which its components share both short and long run co-movements. The omission of relevant explanatory variables seems to be the main cause of the discrepancies. We confirm that there exists a strong positive relationship between per capita health care expenditure and per capita GDP. However we estimate a long run income elasticity at or around unity, although it is greater than unity for the countries with lower per capita income (Spain and Ireland). The results for income elasticity are the same regardless of whether health care expenditure is converted using the GDP PPP or the 'universal' health price index. The importance of non-income variables is also confirmed, in particular the relative price of health care. We find that relative price has a strong rationing effect on the quantity of health demanded and has no effect on the expenditures.

Morbid obesity (body mass index (BMI) > or =40 kg/m2) is associated with substantially increased morbidity and mortality from chronic health conditions and with poorer health-related quality of life; however, less is known about the impact of morbid obesity on healthcare expenditures. To examine the impact of morbid obesity on healthcare expenditures using a nationally representative sample of US adults. We performed a cross-sectional analysis of 16 262 adults from the 2000 Medical Expenditure Panel Survey, a nationally representative survey of the noninstitutionalized civilian population of the United States. Per capita healthcare expenditures were calculated for National Institutes of Health BMI categories, based on self-reported height and weight, using a two-part, multivariable model adjusted for age, gender, race, income, education level, type of health insurance, marital status, and smoking status. Odds of incurring any healthcare expenditure and per capita healthcare expenditures associated with morbid obesity in 2000. When compared with normal-weight adults, the odds of incurring any healthcare expenditure in 2000 were two-fold greater among adults with morbid obesity. Per capita healthcare expenditures for morbidly obese adults were 81% (95% confidence interval (CI): 48-121%) greater than normal-weight adults, 65% (95% CI: 37-110%) greater than overweight adults, and 47% (95% CI: 11-96%) greater than adults with class I obesity. Excess costs among morbidly obese adults resulted from greater expenditures for office-based visits, outpatient hospital care, in-patient care, and prescription drugs. Aggregate US healthcare expenditures associated with excess body weight among morbidly obese US adults exceeded $11 billion in 2000. The economic burden of morbid obesity among US adults is substantial. Further research is needed to identify interventions to reduce the incidence and prevalence of morbid obesity and improve the health and economic outcomes of morbidly obese adults.

BackgroundReductions in smoking in Arizona and California have been shown to be associated with reduced per capita healthcare expenditures in these states compared to control populations in the rest of the US. This paper extends that analysis to all states and estimates changes in healthcare expenditure attributable to changes in aggregate measures of smoking behavior in all states.Methods and FindingsState per capita healthcare expenditure is modeled as a function of current smoking prevalence, mean cigarette consumption per smoker, other demographic and economic factors, and cross-sectional time trends using a fixed effects panel data regression on annual time series data for each the 50 states and the District of Columbia for the years 1992 through 2009. We found that 1% relative reductions in current smoking prevalence and mean packs smoked per current smoker are associated with 0.118% (standard error [SE] 0.0259%, p < 0.001) and 0.108% (SE 0.0253%, p < 0.001) reductions in per capita healthcare expenditure (elasticities). The results of this study are subject to the limitations of analysis of aggregate observational data, particularly that a study of this nature that uses aggregate data and a relatively small sample size cannot, by itself, establish a causal connection between smoking behavior and healthcare costs. Historical regional variations in smoking behavior (including those due to the effects of state tobacco control programs, smoking restrictions, and differences in taxation) are associated with substantial differences in per capita healthcare expenditures across the United States. Those regions (and the states in them) that have lower smoking have substantially lower medical costs. Likewise, those that have higher smoking have higher medical costs. Sensitivity analysis confirmed that these results are robust.ConclusionsChanges in healthcare expenditure appear quickly after changes in smoking behavior. A 10% relative drop in smoking in every state is predicted to be followed by an expected $63 billion reduction (in 2012 US dollars) in healthcare expenditure the next year. State and national policies that reduce smoking should be part of short term healthcare cost containment.

The United States is facing a severe crisis over health care costs. Although nearly 15% of the population—some 45 million people—lack health insurance,1 per capita health care expenditures in the United States are approximately twice as high as in other industrialized countries,2 most of which provide near-universal health insurance coverage. Despite these high per capita health care expenditures, health outcomes in the United States are no better, and arguably significantly worse, than in these other countries.3 Moreover, because health care expenditures in the United States have been rising at a rate almost 3% faster than the economy as a whole over the past 30 yr, health care expenses have been absorbing an ever-increasing fraction of the gross domestic product (GDP).4 Even before the current severe recession, it had been estimated that if current trends continue, health care expenses would represent 30% of the GDP within 30 yr, and that the Medicare hospital insurance trust fund would be exhausted within a decade.5 Some have argued that the projected increase in health care expenditures to consume a higher and higher fraction of GDP is not necessarily a problem.6,7 After all, the health care industry itself is an important driving force of the economy and its growth creates new jobs. Moreover, as long as the economy as a whole enjoys sufficient growth, devoting an increasing share of GDP to health care need not reduce absolute spending in areas other than health care, and therefore need not reduce the standard of living. But now that it represents over one-sixth of the national economy, the inefficiency of the American health care system represents a grave threat to America's economic competitiveness. Again, it should be emphasized that per capita health care expenditures in the United States are twice …

This article investigates the variation in the effects of various determinants on the per capita health care expenditure. A total of 28 OECD countries are studied over the period 1990-2012, employing an instrumental variable quantile regression method for a dynamic panel model with fixed effects. The results show that the determinants of per capita health care expenditure do vary with the distribution of the health care expenditure growth, while the change patterns are dissimilar. Specifically, the lagged health spending has a significantly positive effect, with an effect that decreases towards the higher quantiles of of per capita health care expenditure. Per capita GDP has a significantly positive effect, both the short and long run income elasticities are smaller than one, and health care is a necessity. The density of physicians only has a significant negative effect at the lower tail of the distribution. The elderly population has the reverse effect at the lower and upper tails, and this shows an upward trend with the increase in health expenditure growth. Life expectancy has an effect similar to the proportion of the old. Variable representing Baumol's model of unbalanced growth theory has a significantly positive effect, and the change pattern of its influence shows a marked upward trend. However, one component of Baumol variable, labor productivity, only shows significant effect in the low half of the distribution. More attention needs to be paid to the influence of determinants in health expenditure study.

[Author Affiliation]Donald Vandegrift, Professor of Economics, The College of New Jersey, 2000 Pennington Rd., Ewing, NJ 08628, USA; vandedon@tcnj.eduAnusua Datta, Associate Professor of Economics, School of Business Administration, Philadelphia University, School House Lane and Henry Avenue, Philadelphia, PA 19144, USA; dattaa@philau.edu; corresponding author[Acknowledgment]We wish to thank Subarna Samanta, Judith Shinogle, and Karen Conway for helpful discussions and comments. Julie L. Hotchkiss provided us with valuable suggestions for an earlier version of this article, and we benefited from advice given by two anonymous referees at this journal. Ellie Fogarty, Ravi Kaneriya, Michael Ferlise, and Leigh Ann Culbertson provided valuable research assistance.1. IntroductionDuring the period 1990-1998, real per capita expenditures on prescription drugs in the U.S. increased by 84% (1996 dollars, GDP deflator). Beginning in 1993, the average annual percentage increase in prescription drug spending exceeded the overall percentage increase in national health expenditures. By 1998, the annual percentage growth in prescription drug spending was more than 16%, while overall spending on health care rose less than 6% (compared to the previous year). In total, U.S. consumers spent more than $90 billion on prescription drugs in 1998, or $334 on a per capita basis (Centers for Medicare and Medicaid Services 2002). Not surprisingly, prescription drug coverage and its associated costs have become issues in national as well as state political campaigns.1But the rising expenditures were not simply caused by higher prescription drug prices. While the CPI (Consumer Price Index) for prescription drugs rose by 42%, nominal per capita expenditures on prescription drugs rose 120% during the period (U.S. Bureau of the Census 2002). Consequently, higher rates of prescription drug consumption explain at least part of the story. Further, while overall prescription drug spending has increased rapidly, per capita prescription drug use in the United States varies widely by state. In 1998, some states had per capita prescription drug spending of more than $400, while other states' residents spent half that much.While we do not wish to absolve the pharmaceutical manufacturers, this data suggests that arguments placing the blame for high prescription drug costs on the prices and profits of pharmaceutical manufacturers may be missing a portion of the story. The literature on prescription drugs suggests a number of causes (e.g., new drug introductions, aging of the population, insurance coverage). However, Berndt (2002) notes that a shortcoming of the existing literature is the lack of quantitative estimates of the causes for increased consumption of prescription drugs since the mid-1990s. In a similar vein, Kane (1997) concludes that the effect of the managed care revolution has been difficult to separate out from the other forces affecting pharmaceuticals.To better inform public policy, we provide some quantitative estimates of the factors that have contributed to the increase in prescription drug expenditures. We investigate the role of public health factors (obesity, smoking, and alcohol consumption rates); aging (population 65 and over); access to medical care (managed care enrollments and the relative size of the uninsured population); new pharmaceutical products; income; and unemployment on real prescription drug expenditures, using panel data from all 50 U.S. states for the period 1990-1998. The estimates should allow better projections of the anticipated increase in prescription drug expenditures.Gauging the costs of smoking, obesity, and alcohol consumption is also important. High costs associated with any of these public health problems make it easier to justify costly government programs to reduce their prevalence. Thus, public policy responses to rising prescription drug expenditures will vary based on the source of the increase. …

BackgroundEurope’s demographic transition has led to an ageing population, posing challenges for healthcare and pension systems. This study aims to forecast healthcare and social security expenditure in Italy over the next four decades, starting from the different population projection scenarios, and to forecast its incidence on the Gross Domestic Product (GDP) in the context of significant demographic changes.MethodsThis study utilized annual data from 1988 to 2022. The dataset included the following key variables: GDP per capita, Healthcare Expenditures per capita, Social Security Expenses per capita, Ratio of Social Security Expenses and Healthcare Expenditures to GDP, and Italian Population Demographics. A Bayesian Vector Autoregressive model was employed to analyze the development of these variables in future years; forecasts extended to 2060 under six demographic scenarios provided by Eurostat: Baseline, Low Mortality Rate, Low Fertility Rate, High Migration, Low Migration, and No Migration.ResultsAll scenarios showed increases in per capita healthcare and social security expenditures over time. The No Migration scenario exhibited the fastest growth in both expenditures, especially after 2050. The High Migration scenario showed the slowest growth. Other scenarios showed intermediate growth. Per capita healthcare expenditure grew by drawing a convex line, while the per capita social expenditure grew faster, following a concave trajectory.ConclusionsDue to demographic changes, social and healthcare expenditure is set to increase. Results suggest that strong and timely measures should be taken to limit the demographic imbalances affecting healthcare and social expenditures.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13690-025-01719-9.

Achieving equitable access to medicines requires understanding of how pharmaceutical use and spending vary by race and ethnicity across the US. To quantify variation in prescription drug utilization and spending per capita and per prevalent case by race, ethnicity, health condition, payer, and US state. In this cross-sectional study, the US Disease Expenditure project was extended to incorporate disaggregation by race and ethnicity for state-level retail prescription drug utilization and spending-in addition to 143 health conditions, 38 age and sex groups, and 4 payers (Medicare, Medicaid, private insurance, and out of pocket)-across the 2019 population in all 50 states and Washington, DC. Data were analyzed from October 2023 to April 2025. Four mutually exclusive racial and ethnic groups (Asian or Pacific Islander, Black, Hispanic, and White). Outcomes include prescriptions dispensed and spending for retail pharmaceuticals. Estimates were standardized by population size, population age, and-where data permitted-by disease burden (52 conditions). Das Gupta decomposition was used to estimate the relative contribution of 3 factors (disease prevalence, prescriptions per prevalent case, and spending per prescription) on observed disparities in age-standardized per capita pharmaceutical spending. In 2019, age-standardized pharmaceutical utilization and spending per person with a given disease was substantially lower than the all-population mean for Black populations, close to the mean for Hispanic populations, and often higher than the mean for Asian or Pacific Islander and White populations. These trends-particularly those for the Black population-were generally consistent across 52 health conditions but varied widely across payers and US states. The decomposition analysis for these 52 conditions showed that differences in per capita pharmaceutical spending across race and ethnicity groups were primarily explained by disease prevalence for Black populations (associated with increased per capita spending) and by utilization rates per prevalent case for Hispanic populations (also associated with increased spending). In contrast, differences in drug price or product type (spending per prescription) contributed less to observed spending disparities. In this cross-sectional study, racial and ethnic disparities in medication use persisted, most notably the underutilization of medicines relative to disease burden among Black populations. These patterns varied by state, highlighting the need for local- and condition-specific approaches to advancing pharmacoequity in the US.