8.2: Health Metrics
- Page ID
- 212710
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Health Metrics
How healthy people are in a country, a state, or neighborhood is both critical and complicated. There are various measures of well-being called health metrics, that one could use to compare the physical and/or emotional well-being of persons and/or groups. A health metric that combines several measures known as a health index is probably the most useful type of health metric for a group of people. Experts disagree though on which factors are most important to include in a health index, who should be included in a health index, and what sort of other factors (like poverty or war) should be included in a health index.
Figure : World Map - Infant Mortality Rate. Sub-Saharan Africa has rates ten times that found in the developed world. Source: World Bank
Infant Mortality Rate
One of the most useful health metrics is Infant Mortality Rate (IMR), which is a count of the number of children who die during their first year of life per 1,000 live births in that region.
Global disparities in IMR are substantial. Much of the variation in infant mortality can be traced to poverty, and the various problems associated with being poor, especially malnutrition. Other factors, including disease, lack of access to quality health care, and unsanitary living conditions also contribute to poor survival rates for infants. For these reasons, the infant mortality rate is an excellent indicator of the overall health of a population.
Figure US Map - The Infant Mortality Rate in the United States is twice as high in Mississippi than it is in New Hampshire. Source: CDC
Stats of the States - Infant Mortality (cdc.gov)
Infant mortality rates in the United States are low on average. They should be in the country that spends more on health care by a wide margin than any other country in the world. However, despite the extremely high medical costs in the US, the IMR for American babies is three times higher (6 vs 2 per 1000) than it is for babies born in Finland or Japan. Shockingly, the American IMR is also higher than it is in Botswana, Lebanon, and Cuba. The reason for the high IMR in the US is complex, but poverty and the peculiar American health care system are clearly major factors. The role of women in society also affects a country’s IMR. That piece of the puzzle is explored later in the chapter on gender.
The map of IMR by US state makes it evident that poverty, ethnicity and the local political climate are key predictors of infant health in the US. Babies born in liberal, white and prosperous Vermont are half as likely to die as those born in states with a large number of poor, minority and politically conservative voters – like Mississippi and Alabama. Studies show that the problem lies not so much in the hospital care provided babies but in the 10 months or so after babies come home from the hospital. It is during these later months that the health care system breaks down for infants from poor families, especially if their parents are African-American. But the map shows this is not just a matter of poverty or ethnicity. Cuba, for example has much less wealth than the US, and a higher percentage of people of color, but the Cuban government’s willingness to delivery cheap, accessible healthcare for everyone keeps their IMR lower than United States’ IMR.
Figure : World Map - Life expectancy ranges from a low of less than 45 years in parts of Africa to 82.5 in Japan. Source: World Bank.
Life Expectancy
Like IMR, life expectancy is a useful indicator of the overall health of a population. This metric is an estimate of how long a person is expected to live on the day they were born. Calculations are largely based on observed death rates, plus additional considerations. The life expectancy of any group of people can be greatly affected by things like war, or the outbreak of diseases. In Africa, for example, after years of improvement, the life expectancy recently stagnated as AIDS swept across the continent. Iraq and Syria have seen life expectancy downgraded in recent years as wars in the region continue to wear on indefinitely.
In the United States, babies born today can expect to live to be about 79 years old. Again, this isn’t great considering that life expectancy generally is a product of national wealth and health care expenditure. Almost every developed country in the world and even some developing nations (Chile, Cuba, Costa Rica, e.g.) better life expectancies than Americans. Depressingly, in recent years, life expectancy in the US has gone down which indicates troubling trends in our society. Rising poverty and associated lifestyles are generally cited as reasons for the shrinking life span in the US.
Figure : US Map - Life expectancy in Mississippi is 75 years, but in Hawaii, it is over 81. Substantial variation exists within states too. Poverty and ethnicity are key causal variables in the differences. Source: Measure of America / CDC Interactive Map
Measure of America
An interactive mapping and data tool with a host of display and download options.
The geography of poverty, government policies, and cultural practices all affect longevity. In McDowell County, West Virginia, where Americans have the shortest life expectancy (76.5 males; 81.2 females), you’ll find that their median income is around $25,000 a year, about 20% don’t have health insurance, few people complete college, over 16% suffer from diabetes, and over 35% of adults smoke. Even the homicide rate is exceptionally high McDowell County. As a result, babies born in wealthy and health-conscious Marin County, California can expect to live nearly 15 years longer than babies born in McDowell County, West Virginia.
County Health Rankings
An interactive mapping/database of health outcomes and quality of life indicators
Ethnicity plays an important role in health too, but it appears to be less important than geography. For example, Asians, perhaps because of their dietary practices, better access to health care, and maybe genetics, can expect to live about 13 years longer than African-Americans in general. When you combine ethnicity, gender, and location, the difference becomes even greater. For example, African American boys born in Washington DC today have a life expectancy of only 66.5 years, whereas Asian-American girls born in Boston are expected to live, on average, to almost 92! On the other hand, African-American boys born today in Minnesota may expect to live to be about 80 years old on average, which is about the same Asian-American boys born in Hawaii. The point is that lifestyles, dietary practices are affected by both ethnicity and geography. Indeed, what may seem a common “ethnic behavior” in one part of the US may not be so common among the same ethnicity in another part of the country.