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A comparison of health-related expenditures: a multi-country comparison.

INTRODUCTION

A country's ability to provide for its population's health care needs is influenced by financial abilities, priorities of the government, and the political environment. Throughout history, various organizational research has demonstrated improved outcomes of health care variables such as infant mortality, fertility rates, and morbidity and mortality rates related to a combination of improved socio-economic conditions and public health measures (Guyer, Freedman, Strobino & Sondik, 2000). In response to these findings, many countries have improved health care, education and allocated financial support to programs aimed at continued improvement of socioeconomic conditions and public health.

These improvements are critical as the financial health of a country is important to its success in other areas. The supply of adequate health care, health care demands of citizens and the presence of funds available for health care are important factors in the health of a country. For individual citizens, the presence of an effective, efficient, high quality low-cost health care system is important for health and wellness. Without government-provided, or employersponsored, health care, few individuals can afford the ever rising costs of doctor visits, surgeries, or medications.

The purpose of this paper is to analyze data from North American and European countries and contrast multiple health care and health status variables along with economic and social variables within each selected country to determine the link between expenditures and improvements in such health measures. The specific countries were chosen for study as they all published data from the 1990-2000 decade regarding health expenditures, fertility and mortality rates, population ratios, and per capita Gross Domestic Product (GDP). The United States (US), Canada, and Mexico represent North America while Germany, France, and the United Kingdom (UK) represent Europe. This information is useful in comparing nations based on growth or decline and how this change relates to a country's expenditure on health care. The findings are also important to determine how health care expenditures impact key measures of health.

REVIEW OF LITERATURE

Since the pioneering paper on international health comparison is by Newhouse in 1977, there has been much research on health expenditure determinants. This interest in understanding health care expenditure has led to the creation of theories as well as the analysis of health care expenditures (Gerdtham and Jonsson 2000). There is an ongoing debate in the U.S. about possible universal coverage as a way to decrease the costs of care for the poor and the elderly. Recent statistics show much variation in spending among countries with no correlation between expenditure and performance goals (www.oecd.org).

While France is often touted as a model of health care efficiency for study, all do not agree. Heath, Dhalla, and Thomson (2008) agree it is misleading to state that "the World Health Organization anointed the French health care system as the best in the world" and that it "ranked Canada 30th in the same survey." France only ranks first in efficiency and this was calculated by relating a country's overall health achievement to its health system expenditures. Lopes (2007) supports the Organization for Economic Co-operation and Development's view of France's total expenditure on health as a percentage of gross domestic product which was 10.5% in 2006 and began to match and then surpass Canada's at 9.5%. Reforms in 1996 through 1998 also sought to widen the financial base of the French social security system. The French national insurance system has maintained constant deficits since 1985; the deficit now tops $14.77 billion.

Rodwin (2003) agrees the French health system combines universal coverage with a public-private mix of hospital and ambulatory care and a higher volume of service provision than in the United States. Although the system is far from perfect, its indicators of health status and consumer satisfaction are high; its expenditures, as a share of gross domestic product, are far lower than in the United States; and patients have an extraordinary degree of choice among providers.

Kjellstran, Kovithavongs, and Szabo (1998) found the U.S. had the most inefficient outcomes in relation to money spent on health care and that one third of the retail price of prescription medications was spent on administrative costs and investors. However, the advent of "managed care" in the United States is having a favorable influence on the value of health care provided through private health insurance. Gains have been made by increasing the level of preventive care spending and reducing unnecessary care. However, overbearing managed care systems can cause a backlash by consumers and are subject to governmental intervention ("Private Health Insurance," 2004).

Frenk (2006) supports the need for Mexican health care reform because its health system has not kept pace with the pressures of disease, malnutrition, common infections, and reproductive health problems along with non-communicable disease and injury. Despite the fact that life expectancy at birth in Mexico has improved from 42 years in 1940 to 73 years in 2000, major inequalities persist in health and access to health care (Barraza-Llorens, Bertozzi, Gonzalez-Pier, and Gutierrez, 2002.

Tanne (2008) agrees the cost of common medical, surgical, and dental procedures varies widely across nine countries of the European Union, based on HealthBASKET (Health Benefits and Service Costs in Europe) studies but found the total cost of care for each procedure was lower than the average due in part to lower wage levels.

Sox (2008) agrees that reforming health care will not be easy, but argues that it's not impossible. Other countries have done it, and they have lower costs and better overall systematic performance than the U.S. Garber, Goldman, and Jena (2007) go so far as to suggest that spending could be reduced by as much as 30 percent without adversely affecting American's health. According Garber, et. al. (2007), the RAND Health Insurance Experiment (HIE) showed that U.S. spending on health care could be reduced by 30 percent if we improve the efficiency of the health care and if we delivered it in an efficient manner. The National Center for Policy Analysis (at www.ncpa.org) argues that the US devotes a significant amount of its health care expenditures to advanced cases and the elderly that use proportionately more resources, effecting the overall quality of health care.

Health Care Expenditures

Total health care expenditure continues to grow in United States at a fairly substantial rate although recent growth rate has shown some signs of moderating. Significant trends within the last few years are toward increased spending for medications that now comprise over 16% of all health care related expenses. However, capital spending is the area seeing the most significant level of growth. Investment in new technology has more than doubled in the last seven years ("Health spending", 2003). Technological investment in molecular biology, computer and medical science, electrical, mechanical, genetics and biomedical engineering (including cell, molecule and tissue), instrumentation, and advances in early detection that have allowed successful treatment of conditions that were previously untreatable. Ironically, this can serve to add even more cost to the health care system, as a larger segment of the population will live longer and utilize greater amount of health care services in the future (Holtz-Eakin, 2004).

In considering world health care, industrialization has brought wealth, prosperity, and improvements to many economies as proven by a general decrease in mortality rates. The recent past has seen breakthroughs in the prevention and treatment of conditions such as heart disease, cancer, stroke, and premature birth. In most countries, universal health care coverage, whether public or privately financed, provides financial security against the costs of serious illness, thus promoting access to better treatments and preventative services.

Health care expenditure has risen in many countries, with the US in the lead (Avorn, 1998; Kjellstrand, Kovithavongs, and Szabo, 1998). These rising health care costs are causing a shift in how care is administered and financially covered. Tighter controls and increased scrutiny on unnecessary procedures are becoming the norm, not just in the United States' private health sector but also in the more heavily government-sponsored systems in other countries. Notably, as the cost of care increases (and thus the cost of private insurance increases), a growing number of patients are moving to government sponsored health care programs where available. In countries where both private and government sources exist, cost sharing between the two entities is a growing practice. As technology, demographics, and socioeconomics continue to change, the level and type of health care spending will, by necessity, evolve ("Private Heath Insurance," 2004). Narayan (2007) examined per capita health expenditures in various countries and found evidence of convergence of per capita health expenditures of countries including the UK, and Canada to spending levels of the U.S. and also pointed out this is due to rising service costs of providing health care.

In comparing health care expenditure by per capita GDP in each country, Europe spends notably more on public health care and considerably less on private health care than both the US and Mexico. The focus of the expenditure on public health care as opposed to private health care typically correlates with a country's health care policies and laws. European nations provide a public health system for their population that is funded by taxes. Therefore, a smaller portion of the total percent of health care expenditure is allocated to the private sector. Germany, by international comparison, spends the largest amount of its GDP on health care services of the European countries studied (www.oecd.org).

In terms of health expenditure as a percentage of GDP, the U.S. (the only country in the study without public health care for the general population) spends more on health care than the other five countries. For example, United States total expenditure on health, as a percent of GDP, was about 13.58% for the period from 1998-2002. Mexico (at 6.04%) and the UK (at 7.3%) had the lowest health expenditure percentage. Canada, while in North America, has health care policies that are more closely aligned with European countries. Canada (at 9.3%) spends almost the same on health care as Germany (at10.7%) and France (at 9.5%).

In comparing the per capita total expenditure (in U.S. dollars) for health care for European countries from 1998-2002, Germany had the highest expenditure at 39.20%, followed by France at 33.52%, whereas the UK had the lowest expenditure at 27.3%. The per capita total expenditure in France and Germany fluctuated during these years while expenditures in the UK increased steadily. This would seem to indicate that UK is pays more attention to its health sector than France and Germany.

The per capita total expenditure (in U.S. dollars) for health care in North America for the same period, 1998-2002, shows the United States had the highest expenditure at 66.17% whereas Mexico was the lowest at 4.49%. Canada was at 29.34%, thus there is a significant gap between the highest and lowest expenditures in North America. While Europe saw cyclical changes in its rates, North America experienced percentage increases throughout 1998-2002. The spread of the per capita expenditures for all countries studied highlights significant differences in the level of spending between the six countries. For example in 2002, the U.S. per capital total expenditure on health care was $5,274, followed by Germany at $2,631, France at $2,348, Canada at $2,222, the U.K. at $2,031, and finally Mexico at $379. This seems to indicate that US health spending which exceeds that of other countries with much older populations is in part due to higher U.S. per capita gross domestic product (GDP).

In a similar, prior study, Reinhardt, Hussey, and Anderson (2004) examined health spending using data published by the Organization for Economic Cooperation and Development (OECD). Specifically, they explored reasons why U.S. health spending towers over that of other countries (with much older populations). They cite the higher U.S. per capita gross domestic product and a highly complex, fragmented, payment system with high administrative costs.

Health Status--Fertility and Mortality Rates

Costa-Font, et al. (2008) studied the sensitivity of future long-term care demand and expenditure estimates to official demographic projections in four selected European countries including Germany and the United Kingdom, and found significant differences in assumptions about demographic change and its effect on the demand for long-term care, and on relative and absolute long-term care expenditure. They concluded mortality-rate assumptions can have a considerable influence on welfare policy planning.

In comparing data from the six countries for this study, the distribution of health care expenditure did not have an effect on infant or adult mortality rates. The mortality rates are almost identical among the six countries studied. Interestingly mortality rates remained similar in the U.S. and the UK despite different patterns of public and private health care spending. As the most recent data demonstrate, health-related spending is more than 8% of the GDP on average for France, the UK, and Canada while expenditures in the U.S. and Germany exceed 10% of GDP (www.oecd.org).

Yet the relationships between mortality, life expectancy, medical progress, and the economy are not clear. For example Kjellstran, Kovithavongs, and Szabo (1998) analyzed the affiliation between mortality rates in avoidable (with medical treatment) and unavoidable diseases over a decade in six countries. Variables in the study were the cost of medicine both in dollars and as a fraction of the GDP, the relation between different mortality rates, and the amount of money spent by countries on health care from 1980-1990. In their study, death rates in the U.S., Canada, Germany, and France declined, while life expectancy increased, thus decreasing avoidable deaths by 38% and unavoidable deaths by 10%.

The data on life expectancy shows that it was the highest in Canada and Germany and it is the lowest in the U.S and Mexico. While health expenditures as measured by dollars per capita has increased in every country, the percent of GDP spent on health has increased very little except in the U.S. and Canada where it has increased. Interestingly the U.S. spends more on health care than any country, yet it has lower life expectancy than Canada, France, Germany, or UK. With Medicare and Medicaid , the U.S. spends a significant amount of money on care for the elderly and the indigent, with an inadequate relationship between spending and the clinical outcomes.

For all countries studied, the total fertility rate had an effect on health care expenditures. France, Germany, and the UK had an average total fertility rate of 2.15% in 1993 versus 1.63% in 2003; whereas Canada, Mexico, and the U.S. had an average fertility rate of 1.83% in 1993 versus 2.03% in 2003. The increase in Mexico's fertility rate (from 1.65% in 1993 to 2.5% in 2003) could produce a boost for their economy given a growing population.

This line of reasoning leads us to the following two hypotheses:

H1: There is a positive relationship between total health expenditure variables and total fertility rate.

H2: There is a positive relationship between total health expenditure variables and infant mortality.

Economic and Social Issues--GDP, Elderly Population and Rural Population

The rising cost of health care may be related to the aging population (Binstock, 1993). Evidence suggests as people grow older, more money is spent on medical care. As an example, the "baby boom" was a phenomenon restricted to developed countries least impacted by World War II (Foot, 1992) when birthrates and fertility peaked. Baby boomers are commonly defined as those individuals born between 1946 and 1964. In 2009, that would have made them between 45 and 63 years old. There are about 76 million boomers in the U.S., representing about 29 percent of the population. In Canada, they are known as "Boomies;" six million reside there. In Britain, the boomer generation is known as "the bulge" (http://www.u-s-history.com/pages/h2061.html). Immigrants too added to the population of the U.S. and Canada between 1946 and 1964 peaking in 1958.

Both the U.S. and Canada experienced huge population growth from the 1940's to 1960's. These "Baby Boomers" have always had an effect on market direction because of supply and demand principles. The size of the population aged 60 and over has increased dramatically in the six countries studied from 1990-2000 while GDP consistently climbed over the same decade. In 2003, Germany's population age sixty and over was 24.4% followed by the UK at 20.8%, France at 20.5%, Canada at 17.4%, the U.S. at 16.3%, and Mexico at 7.4%.

The baby boom is an example of how the socio-economic standing of a country can affect and create a trend for years to come. Data shows that fertility rates in the United States declined sharply between the Baby Boom years of the 1950s and early 1960s and 1980. For example total fertility fell from 118 births per 1,000 women aged 15 to 44 in 1960 to 68 births per 1,000 women in 1980 (www.childtrendsdatabank.org ). If this decline in total fertility persists, then this can lead to a rapidly aging population and, in the long-run, this may place a burden on the Social Security system because the pool of younger workers responsible for supporting the older population is smaller while at the same time the dependent elderly population is getting larger. Thus when the first boomers reach the age for Social Security in the U.S. they will require more health care which will lead to an increase in health care costs.

A Canadian study in 2003 reported the total per capita expenditures in that country to be just under $4,000. However, when expenditures were further defined by age groups, the study revealed that average expenditures for persons 85 years old and older jumped to a staggering $17,000 per person ("Health Spending," 2003). In comparison, the citizens of Great Britain are also publicly insured yet care is rationed for the elderly within this system (Rivlin, 1999); still more money is spent on the aged population. In the U.S., the tax-funded Medicare program is the only source of universal public insurance for the elderly.

For all countries studied, the population age 60 and over is expected to have an effect on health care expenditures as people typically need more health services as they age. In addition, the number of doctors per capita varies substantially across countries. For example, in U.S. there are 5.5 practicing physicians per every 1,000 persons, in Canada it is 2.1 physicians per 1,000 persons, in France it is 3.3 and in Germany it is 3.6 physicians per 1,000 persons (www.who.int and www.oecd.org). These average numbers, too, understate the clustering of physicians in major metropolitan areas and do not highlight the frequent scarcity of medical care in rural areas. Thus, one would expect population in rural areas will be less healthy than the population in metropolitan areas due to the shortages of medical care facilities. The issue of the influence of per capita income; the population of those aged 65 and above; and the total percentage of total rural population leads one to the next three hypotheses:

H3: There is a positive relationship between total health expenditures and economic (per capita income) and social status.

H4: There is a positive relationship between total health expenditures and the total population ages 65 and above.

H5: There is also a positive relationship between total health expenditures and the percentage of rural populations.

METHODOLOGY AND VARIABLE DEFINITIONS

The large body of literature on the subject of health expenditure determinants points to a number of demographic and economic variables. Considering these relationships, we define [DSV.sub.i] as the demographic status matrix ([TFR.sub.i], [IMR.sub.i]), where the fertility rate is [TFR.sub.i], and [IMR.sub.i] is infant mortality rate. We also define [ESV.sub.i] as the Economic and Social Status variable Matrix ([PC.sub.1,i], [P65.sub.1,i],[PRUL.sub.l,i]), where [PC.sub.1,i] is the gross domestic product per capita at constant 1995 U.S. dollars prices, [P65.sub.1,i] is the total population ages 65 and above, and [PRUL.sub.1,i] is the percentage of total rural population. Finally, we define the per capita health expenditure matrix [HEV.sub.i], as our dependent variable. This variable ([HEV.sub.i]) is defined as the sum of public and private health expenditures as a ratio of total population, and

Assuming there is significant linear correlation between the independent variables [DSV.sub.i], and [ESV.sub.i], there are matrices [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], and numbers [[alpha].sub.i]. The linear function is:

[HEV.sub.i] = [[alpha].sub.i] + {DSV.sub.i] x [[beta].sub.i] + [ESV.sub.i] x [[GAMMA].sub.i] (1)

The estimated coefficients for the above function are:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)

and

[dHEV.sub.i]/[dTFR.sub.i] = [[beta].sub.1,i] [dHEV.sub.i]/[dIMR.sub.i] = [[beta].sub.2,i] [dHEV.sub.i]/[dPC.sub.1,i] = [[gamma].sub.1,i]

[dHEV.sub.i]/[dP65.sub.1,i] = [[gamma].sub.2,i] [dHEV.sub.i]/[dPRUL.sub.1,i] = [[gamma].sub.3,i]

Where [[beta].sub.1,i] > 0 [[beta].sub.2,i] > 0 [[gamma].sub.1,i] > 0 [[gamma].sub.2,i] > 0 [[gamma].sub.3,i] > 0

In analyzing panel data (see Mundlak, 1978 and Mundlak & Yahav, 1981 for a discussion of panel data), cross-sectional and period specific effects can be handled using the fixed effects and the random effects models. Applications of the classic fixed and random effects models for panel data are common in the literature. A primary advantage of these models is the ability to control for time-invariant omitted variables that may bias observed relationships. The major differences between the random effects model and the fixed effects models is that in the former the omitted time-invariant variables are assumed to be uncorrelated with the included timevarying covariates, while in the later they are allowed to correlate (Mundlak 1978). In this paper, we used both the fixed- effects model, and the random-effects model to estimate the health expenditure model (equation 2) and we compare their results with those of the pooled regression model. The model which gives better P-values is considered more efficient estimator of our equation.

The panel data for this study were drawn from the World Development Indicators CDROM (2003) for 1990-2000. The data was collected for two groups of countries--France, the U.K., and Germany as the European group and the U.S., Canada, and Mexico as the North American group. The use of panel data has advantages over cross sectional data. First, the panel data allow us to capture the relevant relationships about variables over time. Second, it allows us to account for the individual country's' effect.

This ratio measures per capita health expenditure and covers the provision of health services (preventive and curative), family planning activities, nutrition activities, and emergency aid designated for health (but does not include provision of water and sanitation). Data are in current U.S. dollars (1).

The determinants used in this paper are as follows: two independent variables describe the health status variables were used. These are the total fertility rate [TFR.sub.i] and the infant mortality rate [IMR.sub.i]. The total fertility rate [TFR.sub.i] is defined as the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with prevailing age-specific fertility rates (2). The infant mortality rate [IMR.sub.i] is defined as the number of infants dying before reaching one year of age, per 1,000 live births in a given year (3). Total fertility rate and infant mortality rate are expected to have positive relationship with health expenditure.

Economic and Social Status Variables: Three independent variables describe the economic and social status variables were used. The main economic variable, per capita income [PC.sub.1,i], is computed as gross domestic product divided by midyear population. Gross domestic product is the sum of gross value added by all resident producers in the economy plus any product taxes but minus any subsidies not included in the value of the products. Data on gross domestic product are in constant 1995 U.S. dollars (4). Per capita income is expected to be positive relationship with health expenditure.

The main social status independent variables used are total population ages 65 and above [P65.sub.1], and the percentage of total rural population [PRUL.sub.1]. Population is based on the de facto definition of population, which counts all residents regardless of their legal status or citizenship--except for refugees not permanently settled in the country of asylum, which are generally considered part of the population of the country of origin (5). The old age dependency ratio ([P65.sub.1], ; total population aged 65 and above) is used here because it is more relevant for the sample countries. The expected sign of the population ages 65 and above would be positive with total health expenditure.

The rural population is calculated as the difference between the total population and the urban population (6). The variable used here [PRUL.sub.1], is constructed as the ratio of the rural population to the) total population.

ESTIMATION RESULTS

Results of estimating Equation 1 are shown in Table 1 for North America and Table 2 for Europe. Both tables show the results of (i) the pooled panel model regression, (ii) the fixed-effect model, and (iii) the random- effect model.

The result for North America, in the pooled model, show the estimated coefficient values for the infant mortality rate [IMR.sub.i] are positive and significant as expected at the 0.01 level in the random effect model and significant at the 0.05 level in the fixed- effect model and the pooled model in supporting H1 through H5.

North American Results

Per capita income level [PC.sub.1,i] has a positive sign and is significant at the 0.01 level in all the three models indicating income level is an important determinant of health expenditure in the North America countries studied. In particular, a percentage point increase in per capita income increases health expenditure by over 5.29 percentage points.

The coefficient for the total population ages 65 and above, [P65.sub.1] is positive and significant at the 0.05 level in the random effect and the pooled models. Thus a one percentage increase in the elderly population will increase health expenditures by over 4.15 percentage points in the random- effect and the pooled model.

The coefficient variable for the total rural population [PRUL.sub.1] is positive but statistically insignificant in explaining health expenditures. The result show lagged health expenditure HEVt--1 is positive and statistically significant at the 0.01 level in the random- effect model and at the 0.05 level in the pooled and the fixed- effect models. The goodness-of-fit of the model was reasonably high (Squared=0.80) in the random effect model and the fixed income effect. Overall results show the random- effect model and the fixed- effect model are preferred to the pooled model.

European Results

The result for Europe shows the estimated coefficient values for the infant mortality rate [IMR.sub.i] is positive and significant as expected at the 0.01 percent level in the random- effect model and significant at the 0.05 level in the pooled model. The per capita income level [PC.sub.1,i] is positive and significant at the 0.01 percent level in the random- effect model and significant at the 0.05 level in the fixed- effect model and the pooled model. This indicates income level is a very important determinant of health expenditure in the European countries studied and is consistent with the results for the North American country group. For the European countries, a percentage point increase in per capita income increases health expenditure by over 3.41 percentage points.

The coefficient for the total population ages 65 and above, [P65.sub.1] is positive and significant at the 0.10 level only in the fixed- effect model. The coefficient variable for the total rural population [PRUL.sub.1] is positive but statistically insignificant in explaining health expenditure.

Similar results were obtained for the North America country group. The results show that the lagged health expenditure variable HEVt--1 is positive and statistically significant at the 0.01 level in the random- effect model and at the 0.05 level. The range of the goodness-of-fit as measured by [R.sup.2] is between 0.52 in the random effect model to 0.64 in the fixed- effect model. The overall result shows the random- effect model and the fixed- effect model are preferred to the pooled model for Europe country group in supporting H1 through H5.

DISCUSSION AND CONCLUSIONS

This paper has empirically examined the demographic and economic determinants of heath expenditure in a North American country group as well as a European country group. Findings indicate variables including the infant mortality rate; per capita income, percent of population aged 65 and over (old age dependency), and the lagged health expenditure are important determinants of health expenditure in the North American country group. The overall results support prior empirical literature, specifically Lopez-Casasnova and Saez's (2006) findings that increases in the percentage of population over 65 and of public health expenditures raised overall health care expenditures.

The empirical analysis found two interesting results. First, among the demographic variables, infant mortality rate is more important than the fertility rate in explaining total health care expenditures. Secondly, among the economic and social status variables, per capita income and the percent of population aged 65 and over (old dependency ratio) are important determinants of health care expenditures.

The findings of this paper have important policy implications. Higher per capita income level is an important determinant of health expenditure. To maintain a healthy population, the North American country group and European country group must follow economic policies to promote economic growth and increase per capita income. In addition, they must initiate policies to address other needs such as poverty reductions, and promotion of new programs for an active ageing, including life-long education and training. In addition, developing policies to reduce infant mortality rates in the North America country group would reduce the total health expenditure. Further research is needed to confirm these findings with a larger sample of countries and a larger group of longitudinal data for multiple decades.

To maintain a healthy population, North America and Europe must continue economic policies promoting economic growth to increase per capita income. Policies must be created to meet the needs of the increasing,

older population including the elimination of discriminations, financial, psychological and physical abuse and other crimes against older persons. Finally, policies to reduce infant mortality rates, such as increasing the proportion of mothers getting early prenatal care, reducing smoking by pregnant women, preventing child malnutrition, as well as medical research to better understand and prevent birth defects, and premature birth, are essential for reducing health care expenditures in the long run. Implications of the findings are presented along with heuristic implications.

Programs to improve health of citizens should be considered and these should focus on the benefit of personal management of wellness, the role of diet and exercise in health, and other programs to assist in controlling major risk factors (i.e., hypertension, high cholesterol, stroke, smoking cessation, effects of alcohol and drugs). Health expenditure on population aged 65 and above represents the lion shares of the total health expenditure. This segment needs to be given incentives to take advantage of the programs available to minimize risk factors.

Understanding the importance of health care spending is important. In order to facilitate such understanding businesses can offer in-house programs for employees to reduce their insurance costs in employer-sponsored plans. In countries with government provided health care, businesses and government must works together to implement wellness programs and encourage and support participation. Both systems need reforms to improve the efficiency and effectiveness of health care spending as these findings suggest.

For those individuals managing health care, attention should be paid to programs which are cost effective and those that are not. Long term data can yield important findings and point to needed changes in programs. On-site training, classes, and engaging in preventive care at businesses, community, and civic organizations, as well as a national or international marketing campaign for personal responsibility for personal health may be appropriate.

HEALTH CARE CHANGES PROPOSED FOR THE U.S

For some time there has been debate about whether there should be fundamental comprehensive health care reform in the United States. Some people ask why reform is needed, while others have been asking for a solution to healthcare problems for many years. The US government is at the time of writing working with the Congress in an uphill battle to pass comprehensive health reform. The Obama Administration believes their proposed reforms will help control rising health care costs, guarantee a patients' choice of doctor, and assure highquality, affordable, health care for nearly all Americans. At the same time, the Obama Administration is frantically trying to pass a reform bill that will affect millions of Americans in a very personal way. At the present time, the U.S. Senate has numerous bills circulating in various committees all involving restructuring the healthcare system; the House of Representatives also have several plans for restructuring healthcare in circulation. Senators and Representatives are also conducting town hall-type meetings with constituents and interest groups in an effort to sell different restructuring plan.

Despite the advantages of a universal health care system, the issue in the United States is highly politicized. Some of the advantages of a universal health care include facilitating the sustenance and growth of small businesses by decreasing the cost of providing health insurance benefits to employees (http://en.wikipedia.org/wiki/Health_care_reform_ debate_ in_the_ United_States).

Another argument for a national health care system is that universal health care would provide for uninsured adults who may forgo treatment that they need for a chronic health condition (http://en.wikipedia.org/wiki/Health_care_reform_debate_in_the_United_States .) Advocates of health care reform argue that moving to a single-payer system would re-allocate the money currently spent on the administrative overhead. Those who support the health reform argue that United States currently spends more money on health care than any other country in the world and still we have millions with no health coverage (Reference http://www.healthreform.gov/about/index.html). To counter those who call the new approach "rationing" President Obama argued the US healthcare is already rationed, based on income, type of employment, and medical pre-existing conditions. He argued that millions of Americans are denied coverage or face higher premiums as a result of medical pre-existing conditions (http://en.wikipedia.org/wiki/Health_care_reform_debate_in_the_United_States). Those who contest the publicly funded health care argue that the system would not work. They point to Canada, United Kingdom and Germany as examples (http://www.healthreform.gov/about/ index.html). They argue these publicly controlled systems have poor quality. These free market advocates argue that the current system in the US leads to faster development of newer drugs for cancer patients. Those against a national health care system argue that health care is not a constitutional right, free health care could lead to an overuse of medical services which eventually will further increase the overall cost.

The suggestions for reform have not been met with overt enthusiasm from most Americans. A big challenge to the ongoing debate on health care reform is to provide people with more information about the advantages and the disadvantages of full coverage. Although the US has a very large and complicated health care system, it is still the only industrialized nation that does not guarantee access to health care as a right of citizenship. Town-hall meetings, interviews through various media outlets, and even televised speeches from President Obama have all been focused on trying to inform the average American about the positives, what the reform will do for them as Americans. Today, the big issue of reform is centered around who should have access to health care and under what circumstances, and the quality of services received as compared to the high premiums which are paid. The President believes that under the concept of "total coverage", premiums would be lowered (http://www.msnbc.msn.com/id/ 33138097/ns/politics-health_care_reform). Because America has some of the best doctors, drugs, and technology in the world, we should also have the best health care system where everyone is able to get treatment. However, despite the effects of politicians, many Americans are still undecided about the issues surrounding healthcare reform efforts. Even for well-informed and educated citizens, finding unbiased information can be difficult as ever voice in the debate seems to have a great stake in the tax dollars which such a program will generate.

AREAS FOR FUTURE RESEARCH

Future research should include a variety of countries for comparison, particularly data from less-wealthy countries for comparison in the European group as it is available. Additional data beyond the 10-year period should also be studied for a richer, longitudinal data set for comparison. Other countries too should be studied beyond North America and a small sample of Europe to include Asia and developing areas, particularly in Russia, China, Brazil, and India.

Additional, studies should examine the ethics of managed care and the effect of limiting or rationing health care on high- risk groups, including the elderly and those with chronic illnesses, are appropriate or in the best interest of the public. Further ethical studies of international data can assess the view of quality, affordable health care for all citizens as a human right. Comparisons and contrasts between national health care and private systems should be made to determine the benefits of each model. Studies should compare the administrative costs of delivering health care across systems and countries to learn best practices for benchmarking. Other comparisons between the U.S. model and national health care should be made to assist the U.S. in developing solutions to spiraling health costs and the rising number of uninsured populations. Further research should include additional variables beyond life expectancy and fertility, and mortality and move to quality of life issues.

AUTHORS' NOTE

The authors would like to thank Dr. Fariboz Asadian for his excellent assistance in developing the mathematical model used in this paper.

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Marilyn M. Helms, Dalton State College

ENDNOTES

(1) Source: World Health Organization, World Health Report and subsequent updates and from the OECD for its member countries, supplemented by World Bank country and sector studies.

(2) Source: World Bank staff estimates from various sources including the United Nations Statistics Division's Population and Vital Statistics Report, country statistical offices, and Demographic and Health Surveys from national sources and Macro International.

(3) Source: World Bank staff estimates using data from the United Nations and UNICEF, State of the World's Children.

(4) Source: World Bank national accounts data, and OECD National Accounts data files.

(5) Source: World Bank staff estimates from various sources including the United Nations Statistics Division's Population and Vital Statistics Report, country statistical offices, and Demographic and Health Surveys from national sources and Macro International.

(6) Source: The data on urban population shares used to estimate rural population come from the United Nations, World Urbanization Prospects. Total population figures are World Bank estimates.
Table 1
North America Health Expenditure Determinants Dependent Variable:
[DELTA] Log Hev

Method: Panel Least Square

                    Pooled Model
Variable               (Common        Fixed Effect      Random Effect
(Coefficient)       Coefficient)          Model             Model

Constant              -0.70322                            -0.617961
                     (-1.372366)                         (-1.334697)
                      [ 0.1851]                           [0.1970]

[DELTA] Log TFR        0.17539          0.155035          0.167897
                     (1.615761)        (1.407019)        (1.610233)
                      [0.1218]          [0.1765]          [0.1230]

LogIMRt -1           0.301626 **       0.471158 **       0.258509 *
                     (2.232753)        (2.432671)        (2.013448)
                      [0.0372]          [0.0256]          [0.0577]

[DELTA] Log PC      5.293816 ***      5.752653 ***      5.583269 ***
                     (7.990743)        (7.744511)        (8.368994)
                      [ 0.0000]          [0.000]          [0.0000]

[DELTA] Log p65      4.488554 **        1.818342         4.155822 **
                     (2.274950)        (0.278929)        (2.377267)
                      [0.0341 ]         [0.7835]          [0.0275]

[DELTA] Log PRUL      0.290488          0.486169          0.311675
                     (0.199287)        (0.302156)        (0.246367)
                      [0.8441]          [0.7660]          [0.8079]

[DELTA] Log HEVt -   0.285782 **       0.268681 **      0.323811 ***
                     (2.687750)        (2.159595)        (3.078757)
                      [0.0142]          [0.0445]          [0.0059]

                                  Effect Specifications

                                     Cross-section fixed
                                     (dummy variables)  Period Random

R-squared             0.777713          0.805113          0.805378

Adjusted R-squared    0.711026          0.718496          0.746991

DW stat               1.852329          2.067804          1.893583

F-statistics        11.66227 ***      9.295133 ***       13.79389 **

Prob(F-stat)         (0.000012)        (0.000050)        (0.000003)

Notes: T-statistics are in parentheses and P values in squared
bracket. ***, **, and * indicates significance at 1, 5, and 10 percent
level respectively

Table 2 Europe: Health Expenditure Determinants Dependent Variable:
[DELTA] Log HEV

Method: Panel Least Square

Variable                     Pooled Model         Fixed Effect Model
(Coefficient)            (Common Coefficient)

Constant                       -0.968950
                              (-1.037611)
                               [0.3140]

[DELTA] Log TFRt - 1           -0.856786               -0.517403
                              (-1.032373)             (-0.602863)
                               [0.3164 ]               [0.5556]

Log IMRt - 1                  0.537154 **              0.160057
                              (2.706473)              (0.597477)
                               [0.0150]                [0.5591]

[DELTA] Log PCt - 1           3.919096 **             3.411901 **
                              (2.792921)              (2.494853)
                               [0.0125]                [0.0248]

[DELTA] Log P65t - 1           5.098205               52.04604 *
                              (1.713329)              (1.776200)
                               [0.1048]                [0.0960]

[DELTA] Log PRULt - 1          15.82828                70.97697
                              (0.756929)              (1.130085)
                               [0.4595]                [0.2074]

Log HEVt - 3                  0.062851 *              0.079839 **
                              (1.924869)              (2.427477)
                               [0.0711]                [0.0283)

Effect Specifications

                                                  Cross-section fixed
                                                   (dummy variables)

R-squared                      0.526708                0.637680

Adjusted R-squared             0.359664                0.444443

DW stat                        1.858947                2.163157

F-statistics                  3.153103 **             3.299990 **

Prob(F-stat)                  (0.028890)              (0.022162)

Variable                  Random Effect Model
(Coefficient)

Constant                       -0.968950
                              (-1.632203)
                               [0.1210]

[DELTA] Log TFRt - 1           -0.856786
                              (-1.623964)
                               [0.1228]

Log IMRt - 1                 0.537154 ***
                              (4.257390)
                               [0.0005]

[DELTA] Log PCt - 1          3.919096 ***
                              (4.393376)
                               [0.0004]

[DELTA] Log P65t - 1          5.098205 *
                              (2.695136)
                               [0.0153]

[DELTA] Log PRULt - 1          15.82828
                              (1.190679)
                               [0.2501]

Log HEVt - 3                 0.062851 ***
                              (3.027897)
                               [0.0076]

Effect Specifications

                             Period Random

R-squared                      0.526708

Adjusted R-squared             0.359664

DW stat                        1.858947

F-statistics                  3.153103 **

Prob(F-stat)                  (0.028890)

Notes: T-statistics are in parentheses and P values in squared
bracket. ***, **, and * indicates significance at 1, 5, and 10 percent
level respectively
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