State Health Expenditure Research Articles

Economic Costs Attributed to Diagnosed Diabetes in Each U.S. State and the District of Columbia: 2021.

To update state-specific estimates of diabetes-attributable costs in the U.S. and assess changes in spending from 2013 to 2021. We used an attributable fraction approach to estimate direct medical costs of diagnosed diabetes using the 2021 State Health Expenditure Accounts, the 2021 Behavioral Risk Factor Surveillance System, and the Centers for Medicare and Medicaid Services 2018-2019 Minimum Data Set. We estimated diabetes-attributable productivity losses from morbidity and mortality using the 2016-2021 National Health Interview Survey and the 2021 mortality data from the Centers for Disease Control and Prevention. Costs were adjusted to 2021 U.S. dollars. Total diabetes-attributable cost in 2021 was $640 billion ($335 billion in direct medical costs and $305 billion in indirect costs). The median state-level total diabetes-attributable cost was $8.2 billion (range $842 million to $81 billion). The median state-level per-person cost was $21,082, ranging from $17,452 to $37,090. Total diabetes-attributable cost increased by a median of 33% between 2013 and 2021, ranging from 16 to 68% across states. Medical costs increased by 50% overall (range 33-79%) and by 27% (range 15-41%) for per person with diabetes. Costs paid by Medicaid experienced the highest increase between 2013 and 2021 (median 153%; range 41-483%). State economic costs of diagnosed diabetes are substantial and increased over the last decade. These costs and their growth vary considerably across states. These findings may help state policy makers in developing evidenced-based public health interventions in their respective states to prevent and control the prevalence of diabetes.

Gender differences in premature mortality for cardiovascular disease in India, 2017–18

BackgroundThe present study tries to provide a comprehensive estimate of gender differences in the years of life lost due to CVD across the major states of India during 2017–18.MethodsThe information on the CVD related data were collected from medical certification of causes of death (MCCD reports, 2018). Apart from this, information from census of India (2001, 2011), SRS (2018) were also used to estimate YLL. To understand the variation in YLL due to CVD at the state level, nine sets of covariates were chosen: share of elderly population, percentage of urban population, literacy rate, health expenditure, social sector expenditure, labour force participation, HDI Score and co-existence of other NCDs such as diabetes, & obesity. The absolute number of YLL and YLL rates were calculated. Further, Pearson’s correlation had been calculated and to understand the effect of explanatory variables on YLL due to CVD, multiple linear regression analysis had been applied.ResultsMen have a higher burden of premature mortality in terms of Years of life lost (YLL) due to CVD than women in India, with pronounced differences at adult ages of 50–54 years and over. The age pattern of YLL rate suggests that the age group 85 + makes the highest contribution to the overall YLL rate due to CVD. YLL rate showed a J-shaped relationship with age, starting high at ages below 1 years, dropping to their lowest among children aged 1–4 years, and rising again to highest levels at 85 + years among both men and women. In all the states except Bihar men had higher estimated YLL due to CVD for all ages than women. Among men the YLL due to CVD was higher in Tamil Nadu followed by Madhya Pradesh and Chhattisgarh. On the other hand, the YLL due to CVD among men was lowest in Jharkhand followed by Assam. Similarly, among women the YLL due to CVD was highest in Tamil Nadu followed by Madhya Pradesh and Chhattisgarh. While, the YLL due to CVD among women was lowest in Jharkhand. Irrespective of gender, all factors except state health expenditure were positively linked with YLL due to CVD, i.e., as state health expenditure increases, the years of life lost (YLL) due to CVDs falls. Among all the covariates, the proportion of a state's elderly population emerges as the most significant predictor variable for YLL for CVDs (r = 0.42 for men and r = 0.50 for women).ConclusionYLL due to cardiovascular disease varies among men and women across the states of India. The state-specific findings of gender differences in years of life lost due to CVD may be used to improve policies and programmes in India.

Varied Health Spending Growth Across US States Was Associated With Incomes, Price Levels, And Medicaid Expansion, 2000-19.

Little is known about health care spending variation across the US for recent years. To estimate health spending by state and payer, we combined data from the government's State Health Expenditure Accounts, which have estimates through 2014, with other primary data on spending. In 2019 state-specific per person spending ranged from $7,250 to $14,500. After adjustment for inflation, annualized per person spending growth for each state ranged from 1.0percent in Washington, D.C., to 4.2percent in South Dakota between 2013 and 2019. The factors that explained the most variation across states were incomes (25.3percent) and consumer prices (21.7percent). Medicaid expansion was associated with increases in total spending per person, although the median of spending in expansion states showed slower growth in out-of-pocket spending than the median in nonexpansion states. Contemporary estimates of state health spending are valuable for tracking divergent expenditure trajectories in the US and assessing the associated factors.

The Economic Burden of Vision Loss and Blindness in the United States

To estimate the economic burden of vision loss (VL) in the United States and by state. Analysis of secondary data sources (American Community Survey [ACS], American Time Use Survey, Bureau of Labor Statistics, Medical Expenditure Panel Survey [MEPS], National and State Health Expenditure Accounts, and National Health Interview Survey [NHIS]) using attributable fraction, regression, and other methods to estimate the incremental direct and indirect 2017 costs of VL. People with a yes response to a question asking if they are blind or have serious difficulty seeing even when wearing glasses in the ACS, MEPS, or NHIS. We estimated the direct costs of medical, nursing home (NH), and supportive services and the indirect costs of absenteeism, lost household production, reduced labor force participation, and informal care by age group, sex, and state in aggregate and per person with VL. We estimated an economic burden of VL of $134.2 billion: $98.7 billion in direct costs and $35.5 billion in indirect costs. The largest burden components were NH ($41.8 billion), other medical care services ($30.9 billion), and reduced labor force participation ($16.2 billion), all of which accounted for 66% of the total. Those with VL incurred $16 838 per year in incremental burden. Informal care was the largest burden component for people 0 to 18 years of age, reduced labor force participation was the largest burden component for people 19 to 64 years of age, and NH costs were the largest burden component for people 65 years of age or older. New York, Connecticut, Massachusetts, Rhode Island, and Vermont experienced the highest costs per person with VL. Sensitivity analyses indicate total burden may range between $76 and $218 billion depending on the assumptions used in the model. Self-reported VL imposes a substantial economic burden on the United States. Burden accrues in different ways at different ages, leading to state differences in the composition of per-person costs based on the age composition of the population with VL. Information on state variation can help local decision makers target resources better to address the burden of VL.

Crisis and federalism: trends and regional patterns of health revenues and expenditures in the brazilian states.

This study aims to analyze regional trends and patterns of health revenues and expenditure in the Brazilian states from 2006 to 2016. This is an exploratory and descriptive study based on secondary national data and selected indicators. Higher per capita net current revenues for all states and regions, with decreasing levels in specific years associated with the crises of 2008-2009 and 2015-2016 were observed. Per capita health expenditure showed an increasing trend, even in times of economic crisis and declining collection. Diversity of sources and heterogeneity of health revenues and expenditures, as well as different impacts of the crisis on the regional budgets, were observed. The results suggest the protective effect of constitutional health linkage, spending commitments and priorities, and compensation mechanisms of fiscal federalism revenue sources in state health expenditures. However, challenges remain for the implementation of a transfer system that reduces inequalities and establishes greater cooperation among entities, in a context of austerity and strong public health financing constraints in Brazil.

Social Expenditure and Economic Growth in Gujarat- Econometric Exploration

Increase in health and educational expenditure has an immense impact on the socio-economic conditions in any country at large and state at regional level. There is no definite agreement as to whether the rising health and educational expenditure are causally affecting to economic growth. This study endeavors Gujarat State as a case to examine empirically a causal relationship among health expenditure, educational expenditure and economic growth using time series data from 1980-81 to 2016-17 for State gross domestic product (SGDP), health expenditure and educational expenditure. The findings from the Vector Error Correction Model (VECM) indicated that there exist a short-run relationship among State health expenditure (HEX), educational expenditure (EXP) and GDP. We also confirmed the Granger unidirectional causality from log State level GDP to log Educational Expenditure and log of Health Expenditure. These results were further confirmed by findings from the impulse response function. This indicates that Gujarat represent an example of a developed state where the size of health and educational expenditure expands in the process of economic transformation.

Suicide, sentiment and crisis

A lack of clarity surrounds the precise nature of the transmission mechanism by which an economic crisis actually affects suicide. This study posits the hypothesis that this influence broadly translates as emotional reaction, ‘gut feelings’ and as such explicitly considers the use of subjective factors of economic performance to better explain variations in suicide rates. Alongside traditional economic indicators we use a ‘consumer sentiment’ measure, a sense of how economic factors are perceived to be impacting on individuals, to explain suicide rates. Furthermore, we explicitly consider the impact of the global financial crisis and test the impact of state public and health expenditures. Results show that consumer sentiment is found to offer a significantly greater explanatory role in exploring variations in the suicide rate compared to traditional economic indicators. Moreover, the effect of consumer sentiment is greater for females than for males, with some nuances in explaining this result. State public and health expenditures do not seem to have any significant influence on suicide rates.

Estimating the Economic Burden Related to Older Adult Falls by State.

Unintentional falls in older adults (persons 65 years of age and older) impose a significant economic burden on the health care system. Methods for calculating state-specific health care costs are limited. This study describes 2 methods to estimate state-level direct medical spending due to older adult falls and explains their differences, advantages, and limitations. The first method, partial attributable fraction, applied a national attributable fraction to the total state health expenditure accounts in 2014 by payer type (Medicare, Medicaid, and private insurance). The second method, count applied to cost, obtained 2014 state counts of older adults treated and released from an emergency department and hospitalized because of a fall injury. The counts in each state were multiplied by the national average lifetime medical costs for a fall-related injury from the Web-based Injury Statistics Query and Reporting System. Costs are reported in 2014 US dollars. United States. Older adults. Health expenditure on older adult falls by state. The estimate from the partial attributable fraction method was higher than the estimate from the count applied to cost method for all states compared, except Utah. Based on the partial attributable fraction method, in 2014, total personal health care spending for older adult falls ranged from $48 million in Alaska to $4.4 billion in California. Medicare spending attributable to older adult falls ranged from $22 million in Alaska to $3.0 billion in Florida. For the count applied to cost method, available for 17 states, the lifetime medical costs of 2014 fall-related injuries ranged from $68 million in Vermont to $2.8 billion in Florida. The 2 methods offer states options for estimating the economic burden attributable to older adult fall injuries. These estimates can help states make informed decisions about how to allocate funding to reduce falls and promote healthy aging.

US state health expenditure convergence: A revisited analysis

This paper studies the evolution of US state health expenditure for a sample that covers 1966–2014. Our results provide evidence against the existence of a single pattern of behavior of personal health care expenditure across the US states. Rather, we can observe the existence of two statistically different convergence clubs. We cannot find evidence of convergence when we disaggregate health expenditure into its three main payers: Medicare, Medicaid and private health insurance expenditure, whilst we again find evidence of convergence clubs. However, the estimated clubs for Medicaid and private health insurance expenditure are statistically different that estimated for total health expenditure. Consequently, our results offer strong evidence of heterogeneity in the evolution of US health expenditure. The analysis of the forces that drive club creation shows that economic situation and some supply-side factors are important. We can also appreciate that some healthcare outcome variables are only related to private insurance health expenditure. The other health expenditures, thus, show a certain lack of efficiency which may be due to practices that have little benefit for patient health.

Economic Costs Attributable to Diabetes in Each U.S. State.

To estimate direct medical and indirect costs attributable to diabetes in each U.S. state in total and per person with diabetes. We used an attributable fraction approach to estimate direct medical costs using data from the 2013 State Health Expenditure Accounts, 2013 Behavioral Risk Factor Surveillance System, and the Centers for Medicare & Medicaid Services' 2013-2014 Minimum Data Set. We used a human capital approach to estimate indirect costs measured by lost productivity from morbidity (absenteeism, presenteeism, lost household productivity, and inability to work) and premature mortality, using the 2008-2013 National Health Interview Survey, 2013 daily housework value data, 2013 mortality data from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research, and mean wages from the 2014 Bureau of Labor Statistics. Costs were adjusted to 2017 U.S. dollars. The estimated median state economic cost was $5.9 billion, ranging from $694 million to $55.5 billion, in total and $18,248, ranging from $15,418 to $30,915, per person with diabetes. The corresponding estimates for direct medical costs were $2.8 billion (range $0.3-22.9) and $8,544 (range $6,591-12,953) and for indirect costs were $3.0 billion (range $0.4-32.6) and $9,672 (range $7,133-17,962). In general, the estimated state median indirect costs resulting from morbidity were larger than costs from mortality both in total and per person with diabetes. Economic costs attributable to diabetes were large and varied widely across states. Our comprehensive state-specific estimates provide essential information needed by state policymakers to monitor the economic burden of the disease and to better plan and evaluate interventions for preventing type 2 diabetes and managing diabetes in their states.

The economic costs and its predictors for childhood autism spectrum disorders in Ireland: How is the burden distributed?

Autism spectrum disorders are associated with a substantial economic burden; although little is known about the relationship between state and family out-of-pocket expenditure. The objective of this study is to estimate the societal cost of childhood autism spectrum disorders and explain the variation in costs between state and family out-of-pocket expenditure. A bottom-up prevalence based cost-of-illness methodology was implemented using data from a combination of multiple convenience samples in Ireland of 195 parents of 222 children aged between 2 and 18 years of age with a clinically diagnosed autism spectrum disorder collected in 2014/2015. The findings show the average annual cost per child for families amounted to €28,464.89 related to private autism spectrum disorder services, lost income and informal care. By comparison, annual state expenditure per child on autism spectrum disorder-related health, social and educational resources was €14,192. Regression analyses indicate that autism spectrum disorder severity is significantly associated with higher out of pocket expenditures but not state health expenditures. The results suggest that parents are central to meeting the needs of young people with autism spectrum disorders in Ireland. Policy implications of these findings suggest that significant investment and commitment is needed to address the needs of individuals living with autism spectrum disorders and their families.

State cigarette taxes and health expenditures: Evidence from dynamic spatial lag panel models

AbstractThis study explores the relationship between state cigarette taxes and state expenditures on health and hospitals. We address two major sources of endogeneity from (i) the relationship between tax rates and expenditure decisions and (ii) spatial dependence in expenditure policies by using tobacco production as an instrument for cigarette tax rates and through a dynamic spatial Durbin model. We estimate the cigarette tax rate expenditure elasticity to be 0.03 (SR) and 0.87 (LR) for state health spending and 0.05 (SR) and 0.79 (LR) for state hospital spending. Increases in cigarette taxes did not reduce state spending on health over this period.

Demographic and Welfare State Predictors of Out-of-Pocket Health Expenditures: A Path Analytic Model

ABSTRACTOut-of-pocket (OOP) payments are principal components of financing healthcare and have a significant effect on poverty in numerous developing countries. The present study seeks to ascertain the relation among demographic, welfare state, and OOP health expenditure indicators using a path analysis. National representative household budget data from the Turkish Statistical Institute for 2015 were used. To test the goodness of fit of the model, multiple fit indices were utilized. The model fit for redefined path analytic model data was good (X2/df = 70.20/9 = 7.8; RMSEA = 0.032; GFI = 1.00; AGFI = 0.99; CFI = 0.99). The results of the analysis revealed that demographic and welfare indicators are causally related to OOP health expenditures, and income was a mediating factor for this interrelationship. Designing of socially inclusive policies on the basis of the values of equity is essential to combat poverty due to OOP health expenditures in developing countries.

Diabetes-Attributable Nursing Home Costs for Each U.S. State.

To estimate the diabetes-attributable nursing home costs for each state. We used a diabetes-attributable fraction (AF) approach to estimate nursing home costs attributable to diabetes (in 2013 dollars) in aggregate and per person with diabetes in each state. We calculated the AFs as the difference in diabetes prevalence between nursing homes and the community. We used the Centers for Medicare & Medicaid Services 2013-2015 Minimum Data Set to estimate the prevalence of diabetes in nursing homes and to adjust for the intensity of care among people with diabetes in nursing homes. Community prevalence was estimated using the Behavioral Risk Factor Surveillance System (BRFSS). State nursing home expenditures were from the 2013 State Health Expenditure Accounts. The fraction of total nursing home expenditures attributable to diabetes ranged from 12.3% (Illinois) to 22.5% (Washington, DC; median AF of 15.6%, New Jersey). The median AF was highest in the 19-64 years age-group and lowest in the 85 years or older age-group. Nationally, diabetes-attributable nursing home costs were $18.6 billion. State-level diabetes-attributable costs ranged from $21 million in Alaska to $2.0 billion in California. Diabetes-attributable nursing home costs per person ranged from $374 in New Mexico to $1,610 in Washington, DC (median of $799 in Maine). Our estimates provide state policymakers with an improved understanding of the economic burden of diabetes in each state's nursing homes. These estimates could serve as critical inputs for planning and evaluating diabetes prevention and management interventions that can keep people healthier and living longer in their communities.

The Relationship between Infant Mortality Rate and Health Expenditure in India: A Bayesian Approach

This paper uses a Bayesian approach to fit a linear regression analysis to panel data. The dependent variable is Infant Mortality Rate and the explanatory variable is state health expenditure for the individual states in India. Various strands of previous literature are examined in order to provide a context for the relationship between state health expenditure and Infant Mortality Rate. Owing to the highly heterogeneous nature of Indian states across time, a panel data model has been considered. The time and state effects are captured in separate intercept terms and the coefficient of regression reflects only the responsiveness of Infant Mortality Rate to an increase in state health expenditure. The underlying assumption here is that the distribution of the explanatory variable affects the dependent variable only through the explanatory variable itself and the conditional distribution of the dependent variable is thus independent of the marginal distribution of the explanatory variable. Another assumption is that state and time effects are isolated from the coefficient of regression but this can be modified with an appropriate prior distribution. Proportion of state GDP allocated to healthcare is used as a proxy for state health expenditure and all calculations are performed with deviations from the mean. The underlying methodology is a Bayesian approach and all the intercept and slope coefficients are modeled with non-informative or uniform prior distributions. Further the completely pooled regression is considered, that excludes time and state heterogeneity and the pooled regression coefficient is used as a prior for the actual slope coefficient and the two coefficients of regression (i.e with a uniform prior and a normal prior with a fixed mean and uniformly distributed variance) are compared. The inference is that increasing state health expenditure has a negative effect on Infant Mortality Rate.

The Evolution and Influencing Factors of Chinese Out-of-Pocket Healthcare Expenditure Efficiency: DEA-Tobit Analysis Based on Panel Data

Based on the panel data from 2002 to 2009 and with the method of DEA-Tobit, firstly we get the conclusion that the state health expenditure efficiency is increasing and personal health expenditure efficiency is descending since health reform and that total health expenditure efficiency is not rising so the problem of inadequate and overly expensive medical services is not solved. Then we analyze the influencing factors of personal health expenditure efficiency by panel Tobit model. The result shows that there are efficiency differences among different regions in China. The influencing factors include population structure, per local financial expenditure, urbanization ratio, per GDP and population scale.

The Evaluation of the Effects of Socio-Demographic Factors on Oral and Dental Health: A Study on the Ages 6.-12

Dental caries are known to represent one of the most important causes of severe health problems both in children and in adolescents. Poor oral hygiene is closely associated with the child's feeding, sleeping, leaning and self-confidence. In this study, we aimed to identify the relationship of oral hygiene with the social life experienced by 6-12 years-old children, and to introduce preventive treatment to these children at an early stage. Study data were obtained from a questionnaire containing demographic questions and conducted on parents of 120 children, and also from results of oral finding following oral medical examinations performed by the dentist. Bacteria plaque index (BPI) which was determined following examination performed by the dentist was also considered as research data. According to results of this research, we are of the opinion that health authorities in Turkey should encourage preventive and implementation of development programs towards public and school-based mouth-dental health care, and that early preventive treatments measures acquired by children would greatly help to reduce state health expenditure on oral health. Furthermore, this study is considered as a preliminary investigation for a project to be carried out by the researchers.

Changes in state policies related to oral health in the United States, 2002-2009.

Examining state policies in oral health, including changes over time, helps inform the degree to which states fulfill public health dentistry functions and deliver essential services. This study examines changes in state policies affecting oral health in the United States between 2002 and 2009. We reviewed 43 oral health policies in three domains (public dental insurance; workforce capacity; and infrastructure, programs, and surveillance). Data sources included federal, state, and private foundation reports and databases. Fifteen of 43 policies had data available for both time points and were analyzed. We examined national and regional changes over time using McNemar's test and Wilcoxon matched pairs signed ranks test. Between 2002 and 2009, the number of states offering Medicaid reimbursement to nondental professionals increased, more states had 12-month continuous coverage in CHIP, income eligibility for children on Medicaid expanded, and the number of licensed dentists per 10,000 population per state increased. However, the percent of public and private state health expenditures going toward dental services declined. Though nationally no other state policies significantly changed, the proportion of population on public water system with fluoridated water increased in Western states, and administration of needs assessments or oral health surveys decreased in the Northeast. Efforts are needed to systematically track the status of state policies to promote the public's oral health. Further research can determine if changes in state policies have led to improvements in the provision of oral health services and oral health status and reductions in disparities.

Public health spending on infant and child mortality in India during the years 1980–2006

BackgroundIndia has achieved a substantial decline in its infant mortality rate from 110 to 47 deaths per 1000 in the last two decades. But, in 2011 there were still 1·7 million deaths in children under-5 in India, accounting for 24% of global under-5 child deaths. On the one hand, per-capita public spending on health has doubled during the same period, but out-of-pocket health expenditure still constitutes 70% of total health spending. In this context, the present study investigated the association between public health spending and infant and child mortality in India. MethodsIn the study, data from the first, second, and third National Family Health Survey were used to create a birth cohort for the years 1980 to 2006 that provided individual death history. The mortality data for each individual were merged with yearly state health expenditure, income, fiscal deficit, and the Gini coefficient for the 27 years, 1980–2006. As health expenditure varies over time by state but not by individual, a state-level fixed effects model was adopted for probit estimation, along with a time fixed effect probit estimation to give the time varying effect. FindingsState-level fixed effect regression results show public spending on health has a marginal effect of −0·077 (SE 0·021) on infant mortality and −0·126 (0·025) on under-5 mortality. Marginal effects of per-capita income and its distribution are statistically insignificant in a state-level fixed model, but they become significant in a time fixed model. Mother's education and age at the birth of the child are significant determinants of infant and under-5 mortality in both of the specified models. InterpretationEven though overall public health expenditure has a marginal effect, its effect is greater on under-5 mortality than on infant mortality. Maternal characteristics, such as age at the birth of the child and education, are significantly associated with infant and child mortality. Given the large variation in public health expenditure across the state, this study suggests that a substantial targeted investment in public health is required to improve health outcomes in the laggard states of India. FundingNone.

Modeling per capita state health expenditure variation: state-level characteristics matter.

In this paper, we describe the methods underlying the econometric model developed by the Office of the Actuary in the Centers for Medicare & Medicaid Services, to explain differences in per capita total personal health care spending by state, as described in Cuckler, et al. (2011). Additionally, we discuss many alternative model specifications to provide additional insights for valid interpretation of the model. We study per capita personal health care spending as measured by the State Health Expenditures, by State of Residence for 1991-2009, produced by the Centers for Medicare & Medicaid Services' Office of the Actuary. State-level demographic, health status, economic, and health economy characteristics were gathered from a variety of U.S. government sources, such as the Census Bureau, Bureau of Economic Analysis, the Centers for Disease Control, the American Hospital Association, and HealthLeaders-InterStudy. State-specific factors, such as income, health care capacity, and the share of elderly residents, are important factors in explaining the level of per capita personal health care spending variation among states over time. However, the slow-moving nature of health spending per capita and close relationships among state-level factors create inefficiencies in modeling this variation, likely resulting in incorrectly estimated standard errors. In addition, we find that both pooled and fixed effects models primarily capture cross-sectional variation rather than period-specific variation.