Analysis of the Determinants of Healthcare Expenditure Based on Panel Data from 2000–2020

To determine the incremental increases in health care utilization and expenditures associated with sleep disorders. Adults with a diagnosis of a sleep disorder (International Classification of Diseases, 10th Revision, code G47.x) within the medical conditions file of the 2018 Medical Expenditure Panel Survey medical conditions file were identified. This dataset was then linked to the consolidated expenditures file and comparisons in health care utilization and expenditures were made between those with and without sleep disorders. Multivariate analyses, adjusted for demographics and comorbidities, were conducted for these comparisons. Overall, 5.6% ± 0.2% of the study population had been diagnosed with a sleep disorder, representing approximately 13.6 ± 0.6 million adults in the United States. Those with sleep disorders were more likely to be non-Hispanic, White, and female, with a higher proportion with public insurance and higher Charlson Comorbidity Scores. Adults with sleep disorders were found to have increased utilization of office visits (16.3 ± 0.8 vs 8.7 ± 0.3, P < .001), emergency room visits (0.52 ± 0.03 vs 0.37 ± 0.02, P < .001), and prescriptions (39.7 ± 1.2 vs 21.9 ± 0.4, P < .001) vs those without sleep disorders. The additional incremental health care expenses for those with sleep disorders were increased in all examined measures: total health care expense ($6,975 ± $800, P < .001), total office-based expenditures ($1,694 ± $277, P < .001), total prescription expenditures ($2,574 ± $364, P < .001), and total self-expenditures for prescriptions ($195 ± $32, P < .001). Sleep disorders are associated with significantly higher rates of health care utilization and expenditures. By using the conservative prevalence estimate found in this study, the overall incremental health care costs of sleep disorders in the United States represents approximately $94.9 billion. Huyett P, Bhattacharyya N. Incremental health care utilization and expenditures for sleep disorders in the United States. J Clin Sleep Med. 2021;17(10):1981-1986.

This study focuses on the relationship between the Human Development Index (HDI) and public social expenditures, analyzing socio-economic models using the examples of selected European countries and Ukraine. The study used the values of the HDI, GDP, and indicators of public expenditures for social purposes, namely, healthcare, education, leisure, culture and religion, and social protection for the period from 2010 to 2021. The analysis targeted 13 European countries using data sets from Eurostat, the Office for National Statistics of the United Kingdom, the State Statistics Service of Ukraine, and the Ministry of Finance of Ukraine. The input time series were checked for lagged values using the STATISTICA software.Empirical evidence suggests a relationship between HDI and public social spending. An increase in the share of public social expenditures in GDP leads to an increase in HDI and vice versa. European countries with a social-democratic model of development have the highest level of centralization of public expenditure in GDP (34.72%) and the highest HDI (0.930), while countries belonging to the Southern European model have the lowest share of socially oriented public expenditure (30.41%) and the lowest HDI (0.873). In addition, there is a time lag between the investment of public funds in healthcare, education, leisure, culture and religion, and social protection and their impact on HDI changes. Thus, ensuring a high level of HDI is achieved, among other things, through state financial support for the relevant components of the social sphere and social protection. AcknowledgmentThe study is funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under project No. 09I03-03-V01-00130.

BackgroundHealth care expenditure has been low over the years in developing regions of the world. A majority of countries in these regions, especially sub-Saharan Africa (SSA), rely on donor grants and loans to finance health care. Such expenditures are not only unsustainable but also inadequate considering the enormous health care burden in the region. The objectives of this study are to determine the effect of health care expenditure on population health status and to examine the effect by public and private expenditure sources.MethodsThe study used panel data from 1995 to 2010 covering 44 countries in SSA. Fixed and random effects panel data regression models were fitted to determine the effects of health care expenditure on health outcomes.ResultsThe results show that health care expenditure significantly influences health status through improving life expectancy at birth, reducing death and infant mortality rates. Both public and private health care spending showed strong positive association with health status even though public health care spending had relatively higher impact.ConclusionThe findings imply that health care expenditure remains a crucial component of health status improvement in sub-Saharan African countries. Increasing health care expenditure will be a significant step in achieving the Millennium Development Goals. Further, policy makers need to establish effective public-private partnership in allocating health care expenditures.

There exists a complex relationship between state-level healthcare spending and health outcomes. As seen at the national level, healthcare expenditures are not associated with a corresponding improvement in health outcomes. This may or may not be the case at the state-level. Furthermore, the health outcomes of residents in a state are influenced by the residents’ demographic profiles, socio-economic factors, environmental factors, and healthcare needs. The ever-increasing healthcare spending without a corresponding improvement in health outcomes of individuals in the U.S. requires a closer examination of state-level polices and characteristics. As state governments are a vital driver of healthcare implementation and as healthcare policy responses in containing healthcare expenses and outcomes vary among states based on the underlying state-level factors, it is critical to examine state-level variations in healthcare outcomes. The aim of this study is to assess the determinants of state-level mortality rates using a spatial Durbin fixed effect model and evaluate the association of mortality rate with healthcare/hospital expenditures using panel data from 2000 through 2009. The study revealed the presence of a significant positive spatial dependence of mortality rate among neighboring states. While population composition (percentage of African-Americans, percentage of male population and percentage of individuals over 65 years of age) and employment rate significantly increased the mortality rate of a state; percentage of Hispanic population, number of active physicians, percentage of married population, proportion of population aged below 5 years and percentage of population with a college degree (bachelors or higher) reduced the mortality rates of a state. Additionally, higher rates of Hispanic population, percentage of population below age 5, Gini coefficient (income inequality) and total number of hospitals per 1000 people of an individual state increased the mortality rates of the neighboring states and higher education level of the state decreased the mortality rate of the neighboring states. Finally the study reported that there is no impact of increasing health/hospital spending on the health outcomes (mortality rate). Results of the study identified the importance of the role of social-determinants as well as up-stream factors such as income, social interaction and education in improving health outcomes (mortality rate). Hence, focusing on the economic, social and population factors of the state is necessary to reduce the mortality rate without further burden of increasing health spending on the states.

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. …

ImportanceApproximately 30% of US families with employer-sponsored health insurance, disproportionately drawn from high-income groups, benefit from flexible spending accounts (FSAs) or health savings accounts (HSAs). The combined association through both out-of-pocket spending and premiums of these tax-favored accounts with health care expenditures and tax expenditures remain uncertain.ObjectiveTo compare the health care and health-related tax expenditures among families holding FSAs, HSAs, or neither type of account.Design, Setting, and ParticipantsThis cross-sectional study used family-level data from the Medical Expenditure Panel Survey from January 1, 2011, to December 31, 2019, and conducted regression models, controlling for demographic and socioeconomic characteristics, chronic conditions, prior health care expenditures, and marginal tax rates to analyze how holding tax-favored accounts is associated with families’ health care spending and tax expenditures. The sample was restricted to families included in the survey for 2 years, with no members 65 years or older, and with at least 1 policyholder covered (only) by full-year employer-sponsored insurance. Data were analyzed from December 1, 2023, to April 30, 2024.ExposuresHolding FSAs or HSAs.Main Outcomes and MeasuresOut-of-pocket and insurance-paid health expenditures overall and by service were measured. Health-related tax expenditures were based on tax-excluded insurance premiums and tax-sheltered out-of-pocket expenses.ResultsOf the 17 038 families included in the study sample, 2628 held FSAs (weighted 17%) and 1845 (weighted 13%) held HSAs. In regression-adjusted models, families with FSAs spent a mean of 20% or $2033 (95% CI, $789-$3276) more on health care annually than non–account holding families, largely due to increased insurer-paid expenses. Families with HSAs spent a mean of 44% or $697 (95% CI, $521-$873) more on out-of-pocket expenditures and had insignificantly higher insurance-paid expenditures than families without accounts, resulting in overall expenditures comparable to those of non–account holders. The additional tax expenditures associated with FSAs were a mean of $1306 (95% CI, $536-$2076) annually per family. Both types of funds were associated with significant increases in tax expenditures from additional office-based visits ($445 [95% CI, $244-$645] for FSAs and $174 [95% CI, $11-$336] for HSAs), outpatient visits ($330 [95% CI, $132-$528] for FSAs and $250 [95% CI, $15-$485] for HSAs), dental visits ($180 [95% CI, $126-$233] for FSAs and $165 [95% CI, $104-$226] for HSAs), and vision care ($36 [95% CI, $28-$45] for FSAs and $52 [95% CI, $40-$64] for HSAs).Conclusions and RelevanceParticipation in FSAs is associated with higher health care expenditures and tax expenditures, while HSAs are not associated with reduced expenditures. Tax policy could be better targeted to enhance insurance coverage and health care accessibility.

This paper studies the effect of health care expenditure on health outcomes in sub‐Saharan Africa (SSA) using data from 1995 to 2018 for 45 countries. It uses fixed effects and generalized methods of moments estimation approaches for the analysis. The paper measures health care expenditure using three proxies: namely, total health care expenditure per capita, public health care expenditure to gross domestic product (GDP), and private health care expenditure to total health expenditure. Health outcomes are measured by child health outcomes (under‐5 mortality rate) and life expectancy. The results show that increase in health care expenditure as measured by total health care expenditure per capita and public health care expenditure to GDP leads to decline in under‐5 mortality rates. The results also show that total health care expenditure per capita leads to an increase in life expectancy. The study therefore recommends that governments in SSA increase budget allocations towards the health care sector to achieve better health outcomes.

Life expectancy is one of the major key indicators of population health condition and economic development of a country. The main objective of this study was to find determine the impact of the life expectancy on changes of economic growth and health care expenditure, and also to find examine the sex difference trend of life expectancy according to the sex difference. Using We used multiple regression models are fitted to estimate the impact of the life expectancy on economic growth and health care expenditure and also to estimate elasticity of life expectancy on health care expenditure and economic growth. Results shows female life expectancy was more than male over the past 15 years. The higher Gross Domestic Product (GDP) per capita was seen in a longer life expectancy. i.e., one dollar increasing in GDP per capita will change in an average the life expectancy by 33 days, and also one unit increase in per person Health Expenditure Per Capita (HEPC) will increase the life expectancy in an average of 8 days in a year. one dollar increasing in GDP per capita by 33 days will also increase life expectancy, for Health Expenditure Per Capita (HEPC), by 8 days by one year on average. The higher proportion of total expenditure on health as a percentage of GDP and direct personal expenditure on health by household as a share of private expenditure on health results in also longer life span. The study has some policy implications for Bangladesh, we conclude that the increased life expectancy has direct impact on increase in per capita real income and higher expenditure on health., population planning and equity for health important for life expectancy. This study has policy implications for Bangladesh, in particular the needs for increased per capita real income and planning for future health and population policies/programs. Therefore, political stability, adequate and suitable social sector policies and government interventions are required to increase life expectancy and economic growth. There is also a need for involvement of health human force in macro and micro policy-makings and critically examine other determinant of health care expenditure.

This study examines the determinants of healthcare expenditure through a cross-sectional analysis of 153 countries using 2018 data. The research employs a classical linear regression model to identify key socioeconomic and demographic factors influencing healthcare spending per capita, expressed in PPP (Purchasing Power Parity). The results show that GDP per capita, public health expenditure as a percentage of GDP, physician availability, and out-of-pocket healthcare costs are statistically significant determinants of healthcare spending. Specifically, the analysis highlights the non-linear relationship between GDP and healthcare expenditure, where wealthier nations tend to spend disproportionately more on healthcare. Public investment in healthcare and the availability of medical professionals also play crucial roles in shaping national healthcare expenditures. Out-of-pocket expenses by households further increase overall healthcare costs, especially in countries with lower public funding. The proposed model explains 96.5% of the variation in healthcare expenditure, suggesting that the selected variables are strong predictors of healthcare spending. These findings provide valuable insights for policymakers, particularly in the context of balancing public healthcare financing and improving access to medical services while managing overall costs.

The purpose of this paper is to model the determinants of health care expenditures (HCE) and investigate the short-run, long-run equilibrium dynamic causal relationship between health care and income per capita within the time series framework from 1981 to 2014 in Malaysia. For appropriate model specification and forecasting accuracy, different econometric diagnostic tests were applied. Ordinary least square (OLS) method was used to estimate the long run parameters. Long run co-integration was investigated by Auto-Regressive Distributed Lag Model (ARDL) Bound approach, whereas, for causality analysis the Engle-Granger method was used. Income, population structure, and population growth was identified as the significant contributing factors to explain variations in HCE. The estimated income elasticity for HCE was found 0.99 < 1 showing health care was a necessity. The results confirmed a feed-back hypothesis between health expenditure and income per capita. Money spending and health care expenditure relationship has long been established (Getzen, 2014). Better health has been identified as an important factor to raise economic growth and increased productivity. A healthy population of any country is of important importance and has positive connections to economic growth (Sachs, 2002; Khan et al., 2015). However, rapidly growing HCE is a matter of grave concern for policy and decision makers across countries in the world. The fast growth rate of health care spending exerts pressure on various sectors of the economy, which might slow down the economic growth sustainability (Jakovljevic and Milovanovic, 2015; Jakovljevic, 2016) create poverty trap, as more out-of-pocket health expenditure hugely affects household income (Khan et al., 2015). Health care expenditure and the Malaysia case Malaysia with a total land area of 329,758 square kilometers is one of the leading and fast growing high middle-income economies in the Southeast Asian countries. The total population of the country is approximately 29,717 million which is distributed within 14 states, with a per capita gross national income of US $22 (international PPP); and life expectancy rate ranging from 72 to 76 years at birth of male and female respectively. It spends US$ 938 billion total on health with a growth rate of more than 4.49% on HCE (WHO, 2013). Malaysia, a rapidly fast growing developing economy in the Southeast Asian countries, spent 2.94, and 4.49% of GDP on its total health expenditure, in 1997 and 2012, respectively. The overall per capita spending over the same period was US $223 and US$463, respectively. In 2012, the sector-wise share of health care financing expenditure was: Ministry of health 44%; out-of-pocket 37%; private insurance 7%; other federal agencies 4% (MOH, 2014). The health expenditure growth rate of 4.49%, when compared to the annual GDP growth rate of 6%, shows the persistent rise in growth of health expenditure which might cause slowing down growth process of economy to a snail's pace. This might exert burden on country's GDP in the form of deficit budget, provision of health care services, and patients out of pocket finances. Thus, it is needed to model and forecast determinants of health care expenditure and future trends in the health care spending, in order to devise appropriate policies to control the rapidly growing HCE growth, equitable health care services provision, and affordable treatments to the people of Malaysia. This paper aims at, modeling the determinants of health care expenditure (HCE) and the effects of contributing factors of increased health care spending on economic growth by using annual data ranging from 1981 to 2014 in Malaysia.

U.S. health care spending nearly doubled in the decade from 2000-2010. Although the pace of increase has moderated recently, the rate of growth of health care costs is expected to be higher than the growth in the economy for the near future. Previous studies have estimated that 5% of patients account for half of all health care costs, while the top 1% of spenders account for over 27% of costs. The distribution of health care expenditures by type of service and the prevalence of particular health conditions for these patients is not clear, and is likely to differ from the overall population. To examine health care spending patterns and what contributes to costs for the top 5% of managed health care users based on total expenditures. This retrospective observational study employed a large administrative claims database analysis of health care claims of managed care enrollees across the full age and care spectrum. Direct health care expenditures were compared during calendar year 2011 by place of service (outpatient, inpatient, and pharmacy), payer type (commercially insured, Medicare Advantage, and Medicaid managed care), and therapy area between the full population and high resource patients (HRP). The mean total expenditure per HRP during calendar year 2011 was $43,104 versus $3,955 per patient for the full population. Treatment of back disorders and osteoarthritis contributed the largest share of expenditures in both HRP and the full study population, while chronic renal failure, heart disease, and some oncology treatments accounted for disproportionately higher expenditures in HRP. The share of overall expenditures attributed to inpatient services was significantly higher for HRP (40.0%) compared with the full population (24.6%), while the share of expenditures attributed to pharmacy (HRP = 18.1%, full = 21.4%) and outpatient services (HRP = 41.9%, full = 54.1%) was reduced. This pattern was observed across payer type. While the use of physician-administered pharmaceuticals was slightly higher in HRP, their use did not alter this spending pattern. Overall, expenditures in the HRP population are more than 10-fold higher compared with the full population. Managed care pharmacy can benefit from understanding what contributes to these higher costs, and managed care directors should consider an appropriately balanced assessment of the share of total spend by service and therapeutic category in HRP when devising drug usage and related cost-management strategies.

Healthcare systems around the world are facing great challenges. This has included rising health care prices and its impact on healthcare expenditures and the concomitant effects on access to healthcare, particularly in emerging and developing countries. This study focuses on health care price developments and health expenditures in South Africa. The study identifies four major results. Firstly, South Africa’s healthcare expenditures compare quite favourably with countries at similar levels of development. However, the efficiency of these expenditures lags those in comparable countries. Secondly, it was found that South Africa’s healthcare price rises have exceeded those in advanced countries even though healthcare demand and expenditures in these countries are much higher than is the case in South Africa. Thirdly, healthcare rises exceeds those in other sectors of the South African economy. Finally, healthcare price changes adversely impact healthcare expenditures in South Africa. These results indicate that price considerations are critical to improving healthcare access in South Africa. The paper also highlights some non-price determinants of healthcare access that warrant attention by policymakers in South Africa.

We estimated the effect of the ACA expansion of dependents' coverage on health care expenditures and utilization for young adults by race/ethnicity. We used difference-in-difference models to estimate the impact of the ACA expansion on health care expenditures, out-of-pocket payments (OOP) as a share of total health care expenditure, and utilization among young adults aged 19 to 26 years by race/ethnicity (White, African American, Latino, and other racial/ethnic groups), with adults aged 27 to 30 years as the control group. In 2011 and 2012, White and African American young adults aged 19 to 26 years had significantly lower total health care spending compared with the 27 to 30 years cohort. OOP, as a share of health care expenditure, remained the same after the ACA expansion for all race/ethnicity groups. Changes in utilization following the ACA expansion among all racial/ethnic groups for those aged 19 to 26 years were not significant. Our study showed that the impact of the ACA expansion on health care expenditures differed by race/ethnicity.

The aim was to examine the impact of receipt of mental health services on health care expenditures for U.S. adults with major chronic physical conditions. Medical Expenditure Panel Survey data for 2004-2014 were analyzed for adults ages ≥18 with at least one of six chronic physical conditions (cardiovascular diseases, cancer, diabetes, emphysema, asthma, and arthritis) who were followed up for 2 years (N=33,419). Outcomes included overall health care spending and expenditure by service type (inpatient services, outpatient services, emergency department visits, office-based physician visits, and prescribed medication). A difference-in-differences model compared a change in health care costs in the subsequent year for those who did and did not receive mental health services in the preceding year. On average, the increase in overall health care expenditure in the subsequent year among adults receiving mental health services in the preceding year was smaller by 12.6 percentage points (p<0.05) than for those who did not receive such services. The difference was equivalent to $1,146 in 2014 constant U.S. dollars (p=0.05). Medication treatment alone did not have a meaningful effect on overall costs. The combination of psychotherapy and medication was associated with a per-capita reduction in overall health care expenditure of 21.7 percentage points, or $2,690 (p<0.01). The combination was also associated with reduced costs for office-based visits (p<0.05) and medication (p<0.05). Receipt of mental health services was associated with a reduction in overall health care costs, particularly for office-based visits and prescribed medication, among adults with chronic physical conditions.

Scholars in health economics have been studying the relationship between healthcare expenditure and health outcomes for the last half-century. Researchers emphasized the increase of public health expenditure towards providing primary healthcare based on the logic that health expenditure has a direct effect on the health outcomes of the people. However, such studies have a lot of inconsistencies. Given the background, the present study has three research objectives. First, to investigate the effect of healthcare spending on multiple health outcomes in SAARC nations after controlling for country-specific health infrastructures and economic conditions. Second, to undertake a differential analysis of the effect of public and private healthcare spending (both aggregate and out of pocket) on specific health outcomes. Third, to explore the presence (if any) of the differential effect of health expenditure and health infrastructure variables on specific health outcome variables, including mortality and morbidity indicators. Based on a 20-year (1993–2012) panel data from seven SAARC countries, health expenditure was found to influence improved health outcomes in SAARC nations. In addition, the differential effect of public, private and out-of-pocket (OOP) health expenditure was observed on different health outcomes. Thus, OOP expenditures was found to be the major influencer of life expectancy, death rate and TB instances, while public expenditure was found to be influential for improving infant mortality rate (IMR). The present study supports the notion that disaggregated effects of health expenditure (by including the effect of public, private and OOP expenditures) are needed to get a complete understanding of the health expenditure–health outcome linkage. In addition, the findings emphasize on the role of proximal predictors of health outcomes (alongside expenditure variables in the same model) as important inclusion in the health expenditure-health outcome investigation.