Wright State University Economics of Health Care vs Socialized Medicine HW write Summary report 150 words on the topic “Economics of Health Care vs sociali

Wright State University Economics of Health Care vs Socialized Medicine HW write Summary report 150 words on the topic “Economics of Health Care vs socialized medicine” from the below 5 articles.

1) A Hybrid Approach to Estimating the Efficiency of Public Spending on Education in Emerging and Developing Economies

2) Government Health Expenditure and Public Health Outcomes: A Comparative Study among 17 Countries and Implications for US Health Care Reform

3) Methods for the estimation of the National Institute for Health and Care Excellence cost-effectiveness threshold. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4781395/

4) Health expenditures, health outcomes and the role of good governance https://www.jstor.org/stable/23352541

5) Estimating the relationship between economic growth and health expenditures in ECO countries using panel cointegration approach American International Journal of Contemporary Research
Vol. 3 No. 9; September 2013
Government Health Expenditure and Public Health Outcomes: A Comparative Study
among 17 Countries and Implications for US Health Care Reform
Tae Kuen Kim, PhD
Assistant Professor
Adelphi University School of Social Work
1 South Ave. Garden City, NY 11530, USA
Shannon R. Lane, MSW, PhD
Assistant Professor
Adelphi University School of Social Work
1 South Ave. Garden City, NY 11530, USA
Abstract
This research empirically analyzed the relationship between public health expenditure and national health
outcomes among developed countries. The data was collected from 17 OECD countries between 1973 and 2000.
Two public health outcome indicators, infant mortality rate and life expectancy at birth, were used as dependent
variables. To analyze cross-country panel data, we used a mixed-effect model. A statistically significant
association was found between government health expenditure and public health outcomes. Particularly, the
findings showed a negative relationship between government health expenditure and infant mortality rate, and a
positive relationship between government health expenditure and life expectancy at birth. The results suggest that
higher government spending on medical goods and services can be shown to provide better overall health results
for individuals. Based on these results, we discussed the policy implication of recent changes in healthcare policy
in the United States as well as future research direction.
Key Words: Government health expenditure, Infant mortality rate, Life expectancy, US healthcare reform
1. Introduction
The diversity of healthcare systems across countries is explicitly reflected in the degree of public health
expenditure. Continuous debates result regarding the association between public health expenditures and
national health outcomes, but the exact nature of the relationship remains unclear. This research empirically
analyzed the relationship between public health expenditure and national health outcomes among developed
countries in order to inform this debate. Based on the findings, we discuss the policy implications of current
healthcare reform in the United States, and future research directions in this area.
Previous discussions regarding the impact of public health expenditure on national health outcomes do not agree
whether increasing health spending is a positive, negative, or non-significant factor. Theoretical reasons that
spending increases might not improve outcomes include the concern that public spending crowds out private
sector provision (Rajkyman & Swaroop, 2007; Bokhari et al, 2007); that institutional inefficiencies intervene as
services are being delivered (Rajkyman & Swaroop, 2007, p. 97); and that in some of the countries studied,
particularly the less developed countries, the infrastructure needed to access health care may not exist, making the
increased health care spending ineffective (Bokhari et al, 2007).
Health spending in general and public health spending in particular vary widely among countries. Some
governments spend less than 1% of their gross domestic product (GDP) on these services (Rajkuman & Swaroop,
2007, p. 99), while in 1998, the US spent 14% of GDP on health care services (Anderson, Hurst, Hussey & JeeHughes, 2000). This spending includes both preventative and intervention services and is distributed through
service delivery systems that vary significantly across countries (Rajkyman & Swaroop, 2007). Many factors
contribute to public health outcomes of a country, including “biology, environment, lifestyles, and the health care
system” (Elola, Daponte & Navarro, 1995, p. 1397), each of which is in turn affected by the country’s
development, egalitarianism, socioeconomic level, political system, and other factors (Elola et al, 2005).
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Political factors include governance, such as a predictable, open, and transparent process, professional
bureaucracy, accountable executive, and civil society that participates in public affairs, (Rajkyman & Swaroop,
2007, p. 96), as well as the ideology, political system, and resulting welfare state laws (Chung & Muntaner,
2006).
1.1. Public spending and health
Research on the results of government spending on health is mixed, but leans toward positive outcomes from
increased public spending. Bokhari, Gai & Gottret (2007) found increased government spending contributed to
positive outcomes in under-five and maternal mortality (p. 257). Elola et al (1995) found high values of both
country’s GDP and health care expenditures were associated with higher life expectancy for females and
“inversely associated with potential years of life lost to females” in Western Europe (p. 1399). Health care
expenditure explained infant mortality better than GDP. Or (2000/1) also found that increasing health
expenditures had a statistically significant improvement in outcomes for women, but not for men (if GDP is
controlled for). This may be explained by “contrasting mortality patterns” (p. 66), where male mortality causes
such as violence and accidents may be less sensitive to medical interventions, and increased public health
programs (such as those for breast and cervical cancer) can be more effective in changing outcomes for women
than men. In addition, some suggest that women consume health services more regularly than men, increasing
their exposure to system changes.
Or (2000/1) did find that public financing of health care lowered premature
mortality for men and women. He also found higher per-capita income, higher proportion of white-collar
workers, lower amounts of air pollution (in developed countries), and lifestyle factors such as alcohol and tobacco
consumption to be significant contributors to mortality.
Rajkuman and Swaroop (2007) examined data from 1990, 1997, and 2003 for effects of public health spending on
the mortality of children under five, using corruption and bureaucratic quality as indicators of governance level.
In countries with good governance, increasing public health spending by 1 percentage point increases the under-5
mortality rate by .32%. This effect decreases to .20% in countries with average governance and has no effect in
countries with weak governance (p. 97). Chung & Muntaner (2006) also considered a number of variables,
including political environment (ideology and participation), welfare state policies (social security transfer and
percentage of population under public medical coverage), health care system, income inequality, gross national
product, and the Gini coefficient, and their effect on infant mortality rate, under 5 mortality rate, and low birth
weight rate. The Gini coefficient was not significantly associated with infant mortality or low birth weight. This
suggests income inequality is not itself causing bad health outcomes, but is a result of something else that directly
impacts population health. Provision of public health services was the only variable that showed a consistent
relationship with infant mortality.
In a discordant finding, Berger & Messer (2002) considered health care inputs, health behaviors, age, education,
health care expenditures, Gini coefficient, public share of health expenditures, and population covered by public
sources for inpatient and outpatient services in OECD (Organisation for Economic Co-operation and
Development) countries. They found that “increases in the share of health expenditures that are publicly financed
are significantly associated with higher mortality rates (Berger & Messer, 2002, p. 5).” This may be because of a
less productive mix of services or less efficient service provision. Increases in insurance coverage are correlated
with lower mortality rates.
1.2. US Health Care System and Spending
In 1998, the US spent $4,270 per capita (14% of GDP), compared to the next highest OECD country (Switzerland
at $2,740), and a median of $2,000 among 28 OECD peers (Anderson, et al, 2000). Only the US, Switzerland,
and Germany spent more than 10% of GDP on health care. Part of this differentiation is recent: 1997 data
suggested that US spending increased at the same rate as the OECD median from 1960 to 1990, but from 1990 to
1997, US spending increased 4.3% per year, compared to 3.8% for the OECD median (Anderson & Poullier,
1999). In addition, the US had the highest spending in comparison to GDP: 13.5% compared to a low of 4% in
Korea and Turkey and 7.5% in the OECD median.
From 1990 to 1997, the percentage of GDP spent on health
care in six countries (Canada, Denmark, Finland, Italy, Norway, and Sweden) declined, while it rose in the US
from 12.6 to 13.5 (Anderson & Poullier, 1999). In 1997, 24 (of 29) OECD countries ensured health insurance to
at least 99% of their citizens. Of those without universal health insurance in 1997, in two countries (Germany and
the Netherlands) nearly all those not required to purchase health insurance do, for de facto universal coverage.
In 1997, only Mexico, Turkey, and the US had no universal coverage.
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American International Journal of Contemporary Research
Vol. 3 No. 9; September 2013
The US also had the largest percentage of citizens without government-assured health insurance; in 1997, 43
million Americans were without any health insurance, and that number rose to 50 million by 2009 (Kaiser Family
Foundation, 2010). High US spending levels can be partially explained by a higher volume of patient/physician
contacts (six visits per capita, compared with OECD average of 5.9), higher number of FTE staff per hospital bed
(3.9 US, 2.0 OECD median), higher capacity of technology (more MRI units, CT scanners, etc), and first-adapting
of medical innovations (Anderson et al, 2000). The outcomes of these expenditures are mixed. The US does
not do better on life expectancy or infant mortality than others, but is more successful in life expectancy at age 80.
The US has a slightly higher 5-year relative survival rate for breast cancer than other countries (Anderson et al,
2000). In addition, US patients have the shortest wait times for coronary artery bypass graft in the eight
countries that gathered that data (p. 155). One study also suggests that waiting times for nonemergency
surgeries may be less in the US (Anderson et al, 2000, p. 156). In 1996, the US was below the OECD median of
80.3 years for life expectancy (79.4 years). The OECD median for infant mortality was 5.8 deaths/thousand live
births. Only Hungary, Korea, Mexico, Poland, and Turkey had higher rates than the US’ 7.8. That rate is also
declining for the OECD faster than for the US (Anderson & Poullier, 1999).
2. Limitations of Prior Research
Some studies (Akinkugbe & Mohanoe, 2009; Gani, 2009; Kabir, 2008; Leiyu, 1997) have empirically
investigated the impact of public health expenditures on national health outcomes. Previous research includes
two major limitations.
First, some studies analyze the longitudinal relationship within only one country (e.g. 20 years in the US). This
does not provide a comparative picture among different countries. Other studies compare different countries at
two or three time points by employing a “pooled time series analyses”. This does not show trajectory of a given
country. To overcome the limitations of previous research, this study longitudinally analyzed 17 countries for a
27-year period. The resulting analysis shows within-country as well as between-country dynamics regarding the
relationship between public health expenditure and national health outcomes. In addition, this study employed a
“mixed-effect model” to adjust for cross-sectional and time specific idiosyncrasies in cross-country panel data.
3. Data and Methodology
To investigate the impact of public health expenditure on the public health outcome, data was used from 17
developed countries collected between 1973 and 2000, including Australia, Austria, Belgium, Canada, Denmark,
Finland, France, Germany, Ireland, Italy, Japan, Netherlands, New Zealand, Sweden, Switzerland, the United
Kingdom, and the United States. The dataset used in this study was created through the integration of three data
sources: OECD statistics, the World Health Organization (WHO) database and a Quality of Government Study
dataset. The dependent variable of this study is public health outcomes. To measure public health outcomes in a
given country, we used two indicators, infant mortality rate and life expectancy at birth. Infant mortality rate is the
number of infants who die before reaching one year of age, per 1,000 live births in a given year. Life expectancy
at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of
its birth were to stay the same throughout its life. We conceptualized the independent variable as the public
expenditure on health as a percentage of total health expenditure in a given country. We included other socioeconomic covariates that may affect public health outcome. These control variables include real GDP per capita,
the Gini coefficient, unemployment rates, and the rate of the aging population (over 65). Data was analyzed using
LINEAR MIXED MODEL in SPSS (Statistical Package for the Social Sciences) version 19.0.
The dataset used has multi-level structures where repeated measures (e.g., each year’s infant mortality and life
expectancy) are nested within a given country. As a result, individual-year cases of each country are clustered into
17 countries (country-level). Individual-year cases from the same country tend to be more similar due to their
closeness in space and/or time. 1 This interdependency among individual-year cases is called intra-class
correlation, or group homogeneity (Kreft & de Leeuw, 2004). The multilevel structure of cross-country panel data
can be efficiently analyzed with the mixed-effect model. An analysis of panel data using the mixed effect model
has several advantages compared to pooled-time estimation or other multivariate statistics. First, the time points at
which measurements are obtained need not be constant for all subjects.
1
For example, this year’s infant mortality of a given country may be more closely related to last year’s infant mortality of
the country rather than other countries.
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Second, cases with incomplete observations can be included in the analysis without biased estimation. There are
important considerations in many cross-country panel datasets, since some countries have missing variables for
several years2. Finally, researchers can efficiently specify the between and within country-level effects (Norusis,
2004, p. 250).
In our dataset, each country has a maximum of 27 individual-year cases, representing 27 years of annual public
health outcomes (dependent variables). The change in annual public health outcomes for a given country can be
represented through a two-level structure. At level 1, annual public health outcomes of each country (independent
variable) are affected by time-varied variables, annual public health expenditures of each country (independent
variable), and other covariates such Gini and unemployment rate of each year, making a 27-year trajectory of a
given country. At level 2, the trajectories of each country depend on the unique traditions of each country
(Raudenbush & Bryk, 2002). Incorporating these two-level into one equation, mixed-effect model effectively
adjusts for intra-class correlation. Thus, our study appropriately analyzed the unique nature of longitudinal crosscountries data with robust statistical methodology.
4. Results
Table 1 shows the results of mixed-effect model analysis. We analyzed two separate models because our study
includes two dependent variables. The dependent variable of model 1 is infant mortality rate. The results show a
negative relationship between public health expenditure and infant mortality rate. Specifically, a one percent
increase in public health expenditure decreases infant mortality rate by .077, controlling for the effects of other
covariates. Model 2 tests the effect of public health expenditure on life expectancy at birth. The results show a
positive association between these two variables. A one percent increase in public health expenditure increases
life expectancy by .026. These results indicate that a higher level of public health expenditure significantly
decreases infant mortality and increases life expectancy, controlling for other socio-economic conditions of a
given countries. The results also reveal that public health expenditure is a very strong predictor for public health
outcomes. In both models, the p-value of public health expenditure is less than .001, showing high statistical
significance. The results imply that expanding public health expenditures is an efficient strategy to improve
overall health condition among citizens.
Table 1. The results of mixed-effect model
Public health expenditure
(% of Total health expenditure)
Real GDP per capita
Gini coefficient
Unemployment rate
Rate of aging population (Over 65)
-2 Log Likelihood
(Wald z)
Dependent variable
Model 1
Model 2
-.077***
.026***
-.001**
.019
.026
.122**
1357.28
(12.35)
Infant mortality
.001***
-.023**
-.020
-.039
1031.81
(12.01)
Life expectancy
*p
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