PRB-Oldest-Pop-Background

Which U.S. States Have the Oldest Populations?

While Southern states are regarded as retirement magnets, eight of the 10 states with the highest percentages of older residents are not in the South. What’s driving these regional patterns?

More than 55 million Americans are age 65 or older, according to the Census Bureau’s 2020 population estimates. One-fourth of these older Americans live in one of three states: California, Florida, and Texas. Seven other states—Georgia, Illinois, Michigan, New York, North Carolina, Ohio, and Pennsylvania—account for roughly another quarter of the 65+ population.

These 10 states are also the most populous and include over half of the total U.S. population. Sparsely populated states such as Alaska, North Dakota, Wyoming, and Vermont also have very small older adult populations—less than 130,000 each in 2020.

But the states with the most adults age 65 or older do not necessarily have the oldest population age profiles. California is a relatively young state even though it has the largest number of older residents: Only 15% of the state’s total population was age 65 or older in 2020. In contrast, Maine’s relatively small number of older adults represent 22% of its total population, the highest share of older residents in any state.

States Ranked by Percent of Population Age 65 or Older, 2020

wdt_ID Rank State Total Resident Population (thousands) Population Ages 65+ (thousands) Population Ages 65+ (percent of state population)
1 1 Maine 1,350 294 21.8
2 2 Florida 21,733 4,638 21.3
3 3 West Virginia 1,785 374 20.9
4 4 Vermont 623 129 20.6
5 5 Delaware 987 198 20.0
6 6 Montana 1,081 213 19.7
7 7 Hawaii 1,407 275 19.6
8 8 New Hampshire 1,366 263 19.3
9 9 Pennsylvania 12,783 2,448 19.1
10 10 South Carolina 5,218 976 18.7

Note: Older adults (ages 65+) made up 13% of the District of Columbia’s population and 22% of Puerto Rico’s population in 2020.
Source: U.S. Census Bureau, Vintage 2020 Population Estimates.

 

While southern states are regarded as retirement magnets, partly due to their warmer weather and tax benefits for seniors, states in the Northeast and Midwest have among the largest shares of older adults. What’s driving these regional patterns?

Migration, both internal and international, has a large impact on the distribution of older adults. States that have attracted older retirees, such as Arizona, Florida, New Mexico, and South Carolina, have larger proportions of older residents. Many states in the Midwest and Northeast also have large shares of older adults, but for different reasons. As young adults in these states have moved south and west looking for educational and job opportunities, the older population has been left to age in place. In contrast, Texas has been a popular destination for state-to-state and international migrants, which has kept its population relatively young.  Austin-Round Rock-Georgetown was the second-fastest growing metropolitan area in the country between 2010 and 2020, trailing only The Villages in Florida.

The share of older adults will continue to increase as more members of the large baby boom cohort reach retirement age. By 2030, 26 states are projected to have age profiles similar to those of Florida and Maine today, with at least 20% of their residents age 65 or older. This demographic shift has implications for many federal and state programs that support older adults. As more Americans become eligible for federal entitlement programs like Medicare and Social Security, spending reductions and tax increases may be inevitable.


Excerpted from PRB’s Population Bulletin, “Elderly Americans,” by Christine L. Himes, and updated in 2021.

 

map-washington-12-21-b

Making Sense of Geospatial Data with R

The R software environment allows researchers to create custom maps to help answer important questions.

Many questions asked by social science, behavioral science, and public health researchers have underlying geospatial components. That is, the phenomenon of interest is correlated across geography; if characteristics are found in one location, the likelihood of finding them in neighboring locations increases.

Spatial data can seem intimidating, as a different set of tools and software may be needed for analyzing or mapping geographic patterns. In years past, making sense of spatial data required specialized, complex geographic information system (GIS) software that could be intimidating to nonexperts. Additionally, GIS software often comes with costly licenses or investments in information technology (IT), along with more features than one might need to produce visualizations. Fortunately, today’s free online tools make it easier and less costly to process, analyze, and communicate findings from spatial data.

Using R Software to Process and Map Spatial Data

R is free statistical programming software environment used by scholars, scientists, and researchers from an array of fields and disciplines. Although it has a steep learning curve, it rewards those willing to learn with its openness and seemingly endless features. R works with many file types and can automate rote tasks, perform intensive calculations, and even create impressive data visualizations and presentations ready for sharing on the internet.

The power of the R programming environment is in its packages, which extend its capabilities. For instance, if you have a problem you are looking to solve, but R cannot solve it, another R user has likely built a package (a collection of functions, code, and data) that perform the task. And if a package has not already been developed, R allows users to develop a package and share it with others.

The R developer community has ported over many of the core libraries for working with spatial data to R (including GDAL and PROJ), and multiple visualization packages are available to create both static and interactive web-based maps. These include JavaScript libraries such as plotly, highcharter (R wrapper for the High Charts visualization library), and leaflet. Coupled with the R programming environment, these packages allow for a seamless integration of data processing, analysis, and visualization.

Tutorial: Creating A Map of Census Data

Following is an example of how to create a simple and web-ready map with R using the RStudio integrated development environment (IDE). The tutorial includes excerpted examples of R code; complete annotated code for the tutorial is available on GitHub. For this tutorial, let’s say we’re interested in understanding how geographic location is related to median income within a city. Specifically, are higher-income and lower-income households concentrated in specific regions of Washington, D.C.?

First, we can obtain American Community Survey (ACS) data on median household income at the census tract-level for Washington using the R package tidycensus. Tidycensus provides the ability to access specific U.S. Census Bureau data via their API (application programming interface), while simultaneously providing access to the underlying associated geographic data, making it a handy, all-in-one tool for mapping Census data with R.

Below is the code we could use to request relevant data from the Census Bureau API. Here, we request median income by census tract in Washington, based on 2015-2019 ACS 5-year estimates:

data <- get_acs(geography=”tract”,
state=”DC”,
variables=c(
medianIncome=”B19013_001″),
year = 2019,
survey = “acs5”,
output=”wide”,
geometry=TRUE)

In the R code above, year refers to the last year of the 5-year period.

Examining the first six rows of the ACS data from tidycensus, we see that each row includes census tract-level data for Washington. For each tract, information is available for FIPS code (GEOID), location name, median household income, and margin of error.

GEOID Location Median Household Income Margin of Error
11001009509 Census Tract 95.09, District of Columbia, District of Columbia 75515 19621
11001010100 Census Tract 101, District of Columbia, District of Columbia 94861 16089
11001008301 Census Tract 83.01, District of Columbia, District of Columbia 138487 30838
11001002101 Census Tract 21.01, District of Columbia, District of Columbia 67984 11327
11001004100 Census Tract 41, District of Columbia, District of Columbia 156625 27218
11001008001 Census Tract 80.01, District of Columbia, District of Columbia 154423 28910

Table note: In this example, the ACS data were requested with the corresponding geographic data using the geometry argument of the get_acs function. The corresponding geometry column is omitted from the example above.

 

While the Census API makes it easy to extract data, the resulting table doesn’t answer our research question on the relationship between location and household income. Visualizing these data in a map will help us answer that question.

A thorough spatial examination of our data can be achieved using the package mapview, which allows us to quickly overlay our dataset in an interactive web map:

mapviewMap <- mapview(data, zcol=c(“medianIncomeE”),
legend = TRUE, hide = TRUE)

Looking at the map, the relationship between location and median household income is more apparent. Households with lower median incomes are concentrated in the southern and eastern areas of Washington, while households with higher incomes are concentrated in the northwest. With our mouse, we can move around the map and zoom in to get a better look at streets, local parks, and other neighborhood characteristics from information gathered via the base map.

Mapview allows users to interact with and explore their spatial data, but it may not be the ideal choice for creating high-quality interactive maps for sharing on a webpage. Other interactive web mapping packages for R, like leaflet, allow greater customizability for those interested in creating maps that are ready for large audiences. Leaflet employs a variety of base maps that can bring more useful details into our visualization. Visualizations can be easily saved as standalone HTML files and posted to a webpage or shared with others.

As with mapview, we can use leaflet to produce a map of median household income by census tract for Washington. But here, we can extensively modify and customize components of our map, such as the information displayed when clicking on or hovering over a census tract or the legend:


leafletMap <-leaflet() %>%
addProviderTiles(“CartoDB.Positron”, group = “Positron”) %>%
addPolygons(data = data,
fillColor = ~pal(data$medIncCat),
color = “#5e5c5c”,
fillOpacity = 0.55,
weight = 0.4,
smoothFactor = 0.2,
label = labels,
highlightOptions = highlightOptions(
color=”#666″,
bringToFront = TRUE,
weight = 2
)) %>%
addLegend(pal = pal,
values = data$medIncCat,
position = “bottomright”)

This map above has a customized hover-over message; when we hover over a tract, it displays relevant information. We can adjust the display to show specific data in a specific format. In addition, the map’s breakpoints and legend have been tailored to show income distribution by quartile.

RStudio’s viewer makes it easy to save the interactive map as a HTML file. By clicking “Export,” we can access a dropdown menu and save the displayed map as a standalone HTML file, ready to be shared with others. It is as simple as uploading the file to a website and embedding it in a webpage or sending it to colleagues via email. No additional files or software are required.

R offers multiple paths for working with spatial data, far beyond the examples above. This tutorial only scratches the surface of R’s capabilities, which are extensive and constantly evolving through user contributions. To learn more about the basics of R, check out the free R for Data Science book. Or, for a thorough introduction to spatial data analysis and visualization using R, read the free Spatial Data Science book. With these tools, you’ll be developing your own maps in no time.

Annotated code for obtaining, processing, and visualizing the data for these examples can be found here.

I already have a strong bond with my growing baby

Black Women Over Three Times More Likely to Die in Pregnancy, Postpartum Than White Women, New Research Finds

Stark racial disparities in U.S. maternal deaths underscore the need to address equity.

In the United States, Black-white disparities in maternal mortality—deaths related to pregnancy or childbirth—may be larger than previously reported, new research shows. Closing the gap involves addressing structural racism—that is, those aspects of social, political, economic, and health care systems that reinforce inequity, researchers say.

Because pregnancy is riskier to women’s health than abortion, state initiatives to restrict abortion could lead to more deaths, particularly among Black women, new estimates suggest.

Black Women Five Times More Likely to Die from Pregnancy-Related Cardiomyopathy, Blood Pressure Disorders Than White Women

By thoroughly reexamining death certificates from 2016 and 2017, researchers found that the maternal mortality rate among non-Hispanic Black women was 3.5 times that of non-Hispanic white women.1 This is a dramatic increase from previous analyses, based on standard medical codes, that found that Black women faced a maternal death rate 2.5 times that of white women, according to Marian MacDorman of the Maryland Population Research Center (MPRC) at the University of Maryland, who led the study.

The new analysis also revealed that these disparities were concentrated among a few causes of death. Postpartum cardiomyopathy (a form of heart failure) and the blood pressure disorders preeclampsia and eclampsia were leading causes of maternal death for Black women, with mortality rates five times those of white women. Pregnant and postpartum Black women were also more than two times more likely than white women to die of hemorrhage (severe bleeding) or embolism (blood vessel blockage).

In the researchers’ analysis, embolism and preeclampsia/eclampsia tied for the leading cause of maternal death across all racial and ethnic groups during pregnancy or within six weeks after pregnancy (see figure). These were followed by postpartum cardiomyopathy, hemorrhage, and complications from obstetric surgeries such as cesarean sections.

Among White and Hispanic women, causes of maternal death ranked somewhat similarly. However, for Black women, preeclampsia/eclampsia was the leading cause of maternal death, followed by postpartum cardiomyopathy, embolism, and hemorrhage. Ectopic pregnancy, the fifth leading cause of maternal death for Black women, was not a leading cause for white or Hispanic women.

Preeclampsia/Eclampsia Is the Leading Cause of Maternal Death Among Black Women

U.S. maternal deaths per 100,000 live births for five leading causes of maternal death by race/ethnicity, 2016-2017


Notes: Maternal deaths include those during pregnancy and up to 42 days postpartum. For Hispanic women, the values for preeclampsia/eclampsia, obstetric hemorrhage, postpartum cardiomyopathy, and other complications of surgery and procedures are not statistically reliable, based on 10-19 deaths in the numerator. 
Source: Table 2, Marian F. MacDorman et al., “Racial and Ethnic Disparities in Maternal Mortality in the United States Using Enhanced Vital Records, 2016‒2017,” American Journal of Public Health 111, no. 9 (2021): 1673-81.

The study also found that late maternal deaths—those occurring between six weeks and one year postpartum—were 3.5 times more likely among Black women than white women. Postpartum cardiomyopathy was the leading cause of late maternal death among all races, with Black women having a six-times-higher risk than white women.

The prominence of cardiovascular conditions—eclampsia, preeclampsia, embolism, and cardiomyopathy—among the leading causes of maternal death, particularly for Black women, “highlights the importance of increased vigilance to improve early diagnosis and treatment of these complications,” MacDorman says.

The elevated risk of maternal mortality for Black women, from multiple causes, reflects the impact of structural racism on health and health care in the United States, argues study coauthor Marie Thoma of the University of Maryland School of Public Health and the MPRC.

“Further research into the experiences of people of color can inform efforts to improve health care systems and, thus, improve the birthing experience for all,” Thoma says. “We need new models of care before, during, and after birth to address these inequities.”

Promoting Equity in Women’s Health Requires Fundamental Changes in Approach

Addressing the stark racial disparities that MacDorman’s team highlighted requires fundamentally reorienting the current approach to health care, argues Rachel Hardeman of the Minnesota Population Center at the University of Minnesota. Research and action must be grounded in the understanding that racism is at the root of health inequities, she says.

“We must first ask, ‘How do systems, policies, and social structures combine to create the conditions for poor health?’ rather than asking, “’What’s wrong with people of color that makes them die younger and at higher rates and suffer more illnesses?’’” says Hardeman, who is also director of the new Center for Antiracism Research for Health Equity at the University of Minnesota.

As part of a four-person team, Hardeman documented that Black newborns’ in-hospital death rate was one-third lower when Black newborns were cared for by Black physicians rather than white physicians.2

Hardeman is now studying a model prenatal care program designed to reduce birth complications and maternal death that “treats a mother’s culture, racial identity, and background as assets during pregnancy, rather than pathology or a problem.”3 The Roots Community Birth Center is an African American-owned, midwife-led, freestanding birth center located in a Minneapolis neighborhood with one of the highest infant mortality rates in the city. The care plan includes regular prenatal classes on gestational diabetes, nutrition, and other topics, and longer and more frequent-than-typical prenatal and postnatal visits, including three home visits in the first week after birth.

Since opening in 2015, the birth center has cared for 284 families with no premature or low-birthweight babies and a high proportion of mothers successfully breastfeeding. Preliminary findings suggest this model shows promise for providing midwifery care in a culturally centered environment, according to Hardeman.

However, the birth center faces financial challenges due to current maternity care payment models and inadequate Medicaid reimbursement, Hardeman says. To support innovative centers such as Roots, Hardeman suggests policymakers increase Medicaid reimbursement rates. In the long run, the added expense will lead to increasing numbers of healthy babies and mothers who avoid expensive and dangerous complications, and—ultimately—reduce racial disparities, she says.

Banning Abortion Could Raise Pregnancy-Related Death Rates

Legislation designed to limit abortion could exacerbate racial disparities in women’s health and survival. New research by Amanda Stevenson of the CU Population Center at the University of Colorado Boulder shows that banning abortion nationwide would lead to a 21% increase in the number of pregnancy-related deaths for all women and a 33% increase among Black women, compared with rates for 2017.4

Published in the journal Demography, the study estimates only the portion of increased deaths from such a ban due to complications of being pregnant and delivering a baby. Any increases due to unsafe abortions or attempted abortions would be in addition to these estimates.

“The takeaway here is that if you deny people abortion, pregnancy-related deaths will increase because staying pregnant is more dangerous than having a safe, legal abortion,” Stevenson says.

Stevenson points out that media outlets and some supporters of abortion rights often raise the specter of dangerous “back alley” or self-induced abortions. But deaths from such incidents, which numbered in the hundreds annually prior to the 1973 Roe v. Wade decision, would be far less common today due to the advent of safe, self-managed abortions using medications, including misoprostol, available via prescription or online, Stevenson reports.

“We expect a lot of people will turn to these safer forms of self-managed abortions, but a lot of people will also just stay pregnant,” Stevenson says. “What happens then?”

Carrying a pregnancy to term is many times riskier than having an abortion—14 times riskier, according to one estimate.5

To predict the maternal mortality consequences of stricter abortion laws, Stevenson used published statistics on the number of abortions and births that occurred annually in recent years, calculated how many more pregnancies would be continued in the absence of legal abortion, then applied pregnancy-related mortality statistics to that number.

The study estimated that in the years following a national abortion ban, an additional 140 women would die annually from pregnancy-related causes, representing a 21% increase from 2017. Among non-Hispanic Black women, pregnancy-related deaths would increase by one-third.

Among white women, the lifetime risk of pregnancy-related death following a national ban would increase from 1 in 4,500 to 1 in 3,900, Stevenson found. By contrast, among Black women the lifetime risk of pregnancy-related death would increase from 1 in 1,300 to 1 in 1,000.

Research also shows that those most likely to seek abortion care, including Black and Hispanic women, women with lower income, and those with chronic or acute health conditions, are also more likely to encounter serious complications during pregnancy, Stevenson explains.

Black women are more likely to seek an abortion for a variety of reasons, including unequal access to housing, education, jobs, and health care, Stevenson says. Meanwhile, the mortality risk of carrying a pregnancy to term is more than three times higher for non-Hispanic Black women compared with non-Hispanic white women.6

“Increasing Black women’s exposure to the risk of pregnancy-related mortality by denying them access to abortion would exacerbate an existing public health crisis,” Stevenson says.

_____________________________________________________

This article was produced under a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). The work of researchers from the following NICHD-funded Population Dynamics Research Centers was highlighted: University of Maryland (5P2CHD041041-18), University of Minnesota (5P2CHD041023-19), and University of Colorado Boulder (5P2CHD066613-10). Lisa Marshall of the University of Colorado Boulder and Charlie Plain of the University of Minnesota contributed to this report.

References

1 Marian F. MacDorman et al., “Racial and Ethnic Disparities in Maternal Mortality in the United States Using Enhanced Vital Records, 2016-17,” American Journal of Public Health 111, no. 9 (2021): 1673-81.

2 Brad N. Greenwood et al., “Physician–Patient Racial Concordance and Disparities in Birthing Mortality for Newborns,” Proceedings of the National Academy of Sciences 117, no. 35 (2020): 21194-200.

3 Rachel Hardeman et al., “Roots Community Birth Center: A Culturally-Centered Care Model for Improving Value and Equity in Childbirth,” Healthcare 8, no. 1 (2020).

4 Amanda Jean Stevenson, “The Pregnancy-Related Mortality Impact of a Total Abortion Ban in the United States: A Research Note on Increased Deaths Due to Remaining Pregnant,” Demography (2021).

5 Elizabeth G. Raymond and David A. Grimes, “The Comparative Safety of Legal Induced Abortion and Childbirth in the United States,  Obstetrics and Gynecology 119, no. 2 Pt 1 (2012): 215-9.

6 U.S. Centers for Disease Control and Prevention, Pregnancy Mortality Surveillance System, accessed on Nov. 30, 2021.