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Global Healthcare

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Flexible Cover and Hour Support for Expats Living or Working Abroad. Get a Quote Today! JPMORGAN FUNDS - GLOBAL HEALTHCARE FUND A FONDS Fonds (WKN A0RPE0 / ISIN LU) – Aktuelle Kursdaten, Nachrichten, Charts und. Mit der innovativen Technik von GHC ist kompetente medizinische Hilfe auch an den entlegensten Orten der Welt direkt und sofort erreichbar. Der Global Healthcare Fund von J.P. Morgan Asset Management identifiziert für den Fonds attraktive Unternehmen aus den Bereichen Pharma, Medizintechnik. Fondspreis für JPMorgan Funds - Global Healthcare Fund A (acc) - USD zusammen mit Morningstar Ratings und Research, Fondsperformance und Charts.

Global Healthcare

iShares Global Healthcare ETF. NAV per Jun USD 52W-​Bandbreite - ; NAV per Jun (%); NAV Total Return as of Der Global Healthcare Fund von J.P. Morgan Asset Management identifiziert für den Fonds attraktive Unternehmen aus den Bereichen Pharma, Medizintechnik. Global Health Care Outlook Die Evolution von „Smart Health Care“. Das Gesundheitswesen des Jahrhunderts wird wesentlich von Qualität. Many more become sick and are hospitalized. Inthe Lancet Commission on Global Surgery was released describing the large burden of surgical disease impacting low- and middle-income countries LMICs. Retrieved 7 Apr In the preceding pity, Beste Spielothek in Oberteisendorf finden and we discussed health outcomes, as measured only from data on mortality. Frederic Gomer 12 06, 5 min read.

Kayleigh Shooter 05 06, 12 min read. William Smith 01 07, 4 min read. Matt High 29 06, 9 min read. William Smith 26 06, 4 min read. Leigh Manning 24 04, 21 min read.

Leigh Manning 17 03, 43 min read. Amber Donovan-Stevens 12 03, 17 min read. Shaun Bowie 15 06, 23 min read. William Smith 15 06, 4 min read.

William Smith 08 06, 4 min read. William Smith 05 06, 5 min read. Richard Hibbert 13 06, 10 min read. Frederic Gomer 12 06, 5 min read.

Jason Chester 07 06, 10 min read. William Girling 24 06, 7 min read. Kayleigh Shooter 16 06, 8 min read. We can see that in less than years the UK doubled life expectancy at birth.

And the data shows that similarly remarkable improvements also took place in other European countries during the same period.

The chart also shows large historical changes in life expectancy estimates for other countries. You can switch to the map view in this visualization by clicking on the corresponding tab, in order to compare life expectancy across countries.

The map shows that, despite long-run cross-country convergence, there are still huge differences between countries: people in some sub-Saharan African countries have a life expectancy of less than 50 years, compared to 80 years in countries such as Japan.

The increase in life expectancy happened to a significant extent because of changing mortality patterns at a young age, but this was not the only reason: life expectancy increased for people at all ages.

The visualization here shows the cumulative share of the world population horizontal axis against the corresponding life expectancy vertical axis at different points in time colored lines.

For red line we see that the countries on the left — including India and also South Korea — have a life expectancy of around 25 years.

And on the very right we see that in no country had a life expectancy above 40 Belgium had the highest life expectancy with just 40 years.

In life expectancy in all countries was higher than in , but we can see that inequality grew substantially. This happened because very large improvements in health outcomes took place in some countries mainly the richer countries in Europe and North America , while others notably India and China made only little progress.

In green line , we can see again an improvement in life expectancy across all countries; yet interestingly, improvements in this last period implied a reduction in inequality.

This happened through very large recent improvements in life expectancy across developing countries.

The conclusion is that the world developed from equally poor health in , to great inequality in , and back to more equality today — but equality at a much higher level.

The inequality in years of life between people within the same country can be measured in the same way that we measure, for example, the inequality in the distribution of incomes.

The following visualization presents estimates of the inequality of lifetimes as measured by the Gini coefficient.

A high Gini coefficient here means a very unequal distribution of years of life — that is, large within-country inequalities of the number of years that people live.

These estimates are from Peltzman 2 , where you can find more details regarding the underlying sources and estimation methodology.

As can be seen in the chart, inequality in health outcomes has fallen strongly within many countries.

In the preceding section we studied life expectancy at birth as a key measure of aggregate health in a population. This measure provides an overview of health outcomes for the average person in a country.

In this section we focus on health outcomes specifically for children. An analysis of mortality for children provides important information regarding aggregate health in a country, because the first years of life are characterised by important health-related challenges.

Consequently, life expectancy increases substantially conditional on surviving the first years of life.

Indeed, as we shall show, an important part of the gains in life expectancy at birth are precisely due to large reductions in child mortality.

Child mortality is usually measured as the probability per 1, live births that a newborn baby will die before reaching age five under current age-specific mortality patterns.

Further in-depth information on child mortality, including definitions, data sources, historical trends and much more, can be found in our dedicated entry on Child Mortality.

The interactive time-series plot shows how child mortality has changed over the long run. As we can see, child mortality in industrialised countries today is below 5 per 1, live births — but these low mortality rates are a very recent development.

In pre-modern countries child mortality rates were between and per 1, live births. In developing countries the health of children is quickly improving — but child mortality is still much higher than in developed countries.

This is partly because the data quality is improving over time, but also because health crises were more frequent in pre-modern times.

In our entry on food price volatility you find empirical evidence of how frequent food crises were.

In the following plot you can see what these and other crises — epidemics or wars, for example — meant for the health of the population.

You can switch to the map view in this visualization by clicking on the corresponding tab, in order to compare child mortality estimates across countries and time.

The map reinforces what we already noted: all countries have reduced child mortality in the long run, but there are still large differences between developed and developing countries.

The fact that developing countries have made particularly fast improvements to reduce child mortality in the last fifty years, has meant that cross-country gaps have been closing.

The following visualization shows child mortality estimates by income level of countries for the period We can see a clear downward trend across all groups.

And since high-income countries have seen the slowest progress due to their already high health outcomes we can see that the gap between these countries and the rest of the world has been narrowing.

Upper middle income countries are in fact close to catching up. Nevertheless, the latest figures show the important challenges that remain: low-income countries have, on average, child mortality rates that are still more than ten times higher than in high-income countries.

The remaining gap is still large. The chart focuses on the five most lethal infectious diseases. It shows the number of child deaths caused by these diseases from onwards.

From onwards the deaths caused by each of these diseases is declining. Similarly to child mortality, maternal mortality provides important information regarding the level of health in a country.

Maternal mortality is usually defined as the number of women dying from pregnancy-related causes while pregnant, or within 42 days of pregnancy termination typically expressed as a ratio per , live births.

Further in-depth information on maternal mortality, including definitions, data sources, historical trends and much more, can be found in our dedicated entry on Maternal Mortality.

The visualizations above highlight the drastic long-term improvements that countries have made to reduce child mortality.

But have these health improvements also materialized for mothers? The chart shows long-run maternal mortality estimates for a selection of mainly high income countries.

We can see that a hundred years ago, out of , child birth, between and 1, ended with the death of the mother. Since women gave birth much more often than today , the death of the mother was a common tragedy.

Today, these countries have maternal mortality rates close to 10 per , live births. The decline of maternal mortality to around 10 per , births can be attributed to our modern scientific understanding of the causes leading to maternal mortality.

It was the physician Ignaz Semmelweis who first noticed the link between hygiene and the survival of mothers in the middle of the 19th century, but it was only until the germ theory of disease became known that appropriate practices became widely adopted.

The same chart also shows that different countries have achieved progress in maternal mortality at different points in time.

Malaysia in contrast achieved this progress in only a few decades. Recent data on maternal mortality shows improvements around the world.

The following interactive visualization presents a world map of maternal mortality rates for the period You can switch to the chart view to explore country-specific trends.

As before, the conclusion here is that despite recent widespread improvements in the developing world, there are huge challenges ahead: in sub-Saharan Africa more than mothers die per , live births.

This is more than 60 times higher than the figure for countries in the European Union. In the preceding sections we discussed health outcomes, as measured only from data on mortality.

This does not take the morbidity from disease and disability into account. The burden of disease is a related, but different measure of health outcomes that accounts for both the mortality and the morbidity of disease.

This variable is calculated as the sum of years of potential life lost due to premature mortality, and the years of healthy life lost due to disease and disability.

You can read more about the definition and calculation of DALYs in the technical report WHO methods and data sources for global burden of disease estimates.

Further in-depth information on burden of disease can be found in our dedicated entry on Burden of Disease.

This map shows DALYs per , people of the population. It is thereby measuring the distribution of the burden of both mortality and morbidity around the world.

We see that rates across the regions with the best health are below 20, DALYs per , individuals. Here we discuss trends showing how the fight against these diseases is evolving.

The most common way of measuring the evolution of diseases is to estimate the number and frequency of deaths caused by the diseases; as well as the number of new people suffering from them.

Between and more than 3 million people were infected with HIV ever year. Since then the number of new infections began to decline and in it was reduced to below 2 million.

The lowest number of new infections since The number of AIDS-related deaths increased throughout the s and reached a peak in , when in both years close to 2 million people died.

Since then the annual number of deaths from AIDS declined as well and was since halved. The chart also shows the continuing increase in the number of people living with HIV.

The rate of increase has slowed down compared to the s, but the absolute number is at the highest ever with more than 36 million people globally living with HIV.

As such, health is often thought of as an individual characteristic beginning with inherited conditions e. More information about the provision of healthcare can be found in our entry on Financing Healthcare.

One of the most important inputs to health is health care. Here we study cross-country evidence of the link between aggregate healthcare consumption and production, and health outcomes.

One common way of measuring national healthcare consumption and production is to estimate aggregate expenditure on healthcare typically expressed as a share of national income.

This visualization shows the cross-country relationship between life expectancy at birth and healthcare expenditure per capita.

The chart shows the level of both measures at two points in time, about a generation apart and respectively.

The arrows connect these two observations, thereby showing the change over time of both measures for all countries in the world.

As it can be seen, countries with higher expenditure on healthcare per person tend to have a higher life expectancy. And looking at the change over time, we see that as countries spend more on health, life expectancy of the population increases.

This means the proportional highest gains are achieved in poor countries with low baseline levels of spending. This pattern is similar to that observed between life expectancy and per capita income.

The countries are color-coded by world region, as per the inserted legends. Many of the green countries Sub-Saharan Africa achieved remarkable progress over the last 2 decades: health spending often increased substantially and life expectancy in many African countries increased by more than 10 years.

The most extreme case is Rwanda, where life expectancy has increased from 32 to 64 years since — which was one year after the Rwandan genocide.

The two most populous countries of the world — India and China — are emphasized by larger arrows. It is interesting to see that in China achieved already relatively good health outcomes at comparatively low levels of health spending.

The association between health spending and increasing life expectancy also holds for rich countries in Europe, Asia, and North America in the upper right corner of the chart.

The US is an outlier that achieves only a comparatively short life expectancy considering the fact that the country has by far the highest health expenditure of any country in the world.

The following visualization presents the relationship between child mortality and healthcare expenditure per capita.

Global data on health expenditure per capita is available since and in this chart we show the level of both measures in the first and last year for which data is available.

The arrows connect these two observations, thereby showing the change over time for all countries in the world. We can see that child mortality is declining as more money is spent on health.

Focusing on changes over time, we can see a particularly striking fact: while there is huge inequality in levels — child mortality in the best-performing countries is almost times lower than in the worst — inequality in trends is surprisingly stable.

Specifically, if you look at the paths over time it is surprising how little heterogeneity there is between very different countries in the world.

No matter whether it is a rich country in Europe or a much poorer country in Africa, the proportional decline in child mortality associated with a proportional increase in health expenditure is remarkably similar.

The visualization also shows the very high global inequality in health spending per capita that is still prevalent today. The ratio between the two countries is ; on average Americans spent more on health per day than a person in the Central African Republic spends in an entire year.

You can also explore this relationship between healthcare spending and child mortality in this interactive visualization. At a cross-country level, the strongest predictor of healthcare spending is national income you can find more about measures of national income in our entry on GDP data.

The following visualization presents evidence of this relationship. The correlation is striking: countries with a higher per capita income are much more likely to spend a larger share of their income on healthcare.

In a seminal paper, Newhouse 4 showed that aggregate income explains almost all of the variance in the level of healthcare expenditure specifically, Newhouse showed that among a group of 13 developed countries, GDP per capita explained 92 percent of the variance in per capita health expenditure.

Other studies have confirmed that this strong positive relationship remains after accounting for additional factors, such as country-specific demographic characteristics.

In the preceding section we provide evidence supporting the fact that there are potentially large health returns to healthcare investments.

Here we explore empirical evidence regarding how healthcare investments are financed around the world.

Global Healthcare

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Find global health experts, publications, news releases, multi-media, story ideas and more. William Smith 01 07, 4 min read.

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William Girling 24 06, 7 min read. Kayleigh Shooter 16 06, 8 min read. William Smith 10 06, 4 min read.

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