Mapping Widget for Populations At Risk for Severe COVID-19

The team at created a widget for us to dispaly interactive maps showing the locations of populations at risk of severe COVID-19.  The at risk populations we mapped are: the number of people 65 years and older; number of people 75 years and older; the numbers of people with underlying chronic conditions linked to severe COVID-19 disease; and the number of hospital beds (note that at any given time in the U.S. about 66% of beds are occupied by patients).  Click the “New Map” button to see a drop down of the available data.

Data mapped by PolicyMap, an online GIS mapping tool.
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At Risk Populations for Severe COVID-19

The Built Environment and Health Research Group has been creating maps showing where in the U.S. there are populations at high risk for severe COVID-19.  By county, they mapped the number of people 65 years and older, 75 years and older and the numbers of people with underlying chronic conditions linked to severe COVID-19 disease.  Counts of people rather than percentages of the population were mapped because it is the number of people, not the percent, that can strain the health care system.  They also mapped the number of hospital beds by count (note that at any given time in the U.S. about 66% of beds are occupied by patients) and identified counties with large populations of adults 65+ years old and low numbers of hospital beds.  More details are at

Counties in purple have large populations of older adults and low numbers of hospital beds.  Maps created using

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Do employees receive recommended preventive health services?

Large numbers of Americans receive their health care through insurance and wellness plans sponsored by their employers.  New work by Rundle and colleagues (full text here) describes a method that employers can use to analyze their medical claims data to determine whether their workforce is receiving a package of high-priority preventive services. The 2010 Patient Protection and Affordable Care Act (ACA) mandated that, other than certain “grandfathered” health care plans, all new health care policies, beginning on or after September 23 2010, must cover designated preventive health services without patient cost-sharing.  Discussions of the ACA preventive services rule are often framed by noting that preventive health care services can help prevent nine of the top 10 leading causes of death and that annually 100,000 deaths could be prevented if persons received recommended preventive care.

Rundle and colleagues applied their method to 87 million person-years of medical claims data from the IBM Watson Health MarketScan data to set benchmarks for the current rate of receipt of preventive services.  In the three-year period 2014 to 2016, 29% of men and 36% of women received all of the age and sex appropriate preventive health services at least once, and 33% of men and 13% of women received none of these services.  The figures below show the percent of women and men, by age group, who received the complete package of high-priority preventive services by 3 year rolling windows.

Large employers routinely analyze de-identified medical claims data from employees and covered family members to monitor and plan for expenditures, to motivate adjustments to insurance and wellness plan offerings, and to identify employee population health needs.  The analytical approach described by Rundle and colleagues can be integrated into routine analyses of medical claims data to monitor the delivery of high-priority preventive services. The utility of this approach is that it allows employers to assess continuously whether their health and wellness plans are under-performing in the delivery of these services.  Employers that find that their health plans are falling short of these benchmarks can work with their vendors to increase the delivery of high priority preventive services.

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City footprints and motor vehicle crashes

Cities around the world differ on countless dimensions. The glow of a sunset. The smell of a sea breeze. The gritty air from a thousand industrial chimneys. The hum of people and animals and machines and elements. As far as public health is concerned, some of these differences affect outcomes for people living in our cities, and where the differences can be changed, there are opportunities for intervention. One very important and tangible feature on which cities differ is their basic footprint: their size, their shape, their overall configuration. Assistant Professor Christopher Morrison, along with international collaborators, published a study in The Lancet Planetary Health to examine how the footprint of cities around the world are related the incidence of injuries due to motor vehicle crashes.

The authors used digital images for the largest 1700 cities from around the world to develop categories based on the layout of roadways, waterways, green space and other such features. They used a machine learning approach in which they trained a computer to recognize 750 images from each city, and then using another 250 images they developed a “confusion matrix” based on instances when the machine mistook the images of one city for another city. From this confusion matrix they identified 9 categories of city footprints, which they gave names such as checkerboard cities, motor cities, and high transit cities. The authors then compared injury incidence per 100,000 population for these cities footprints. They found that high transit cities, meaning cities in which a large proportion of land area was dedicated to rail infrastructure, had around half the crash injury incidence compared to the worst performing categories: cul-de-sac cities, informal cities, and sparse cities. The high transit cities tended to be in western Europe. Cul-de-sac cities were almost all in Indonesia. Informal cities were in India, Africa and the Middle East. Sparse cities predominated in eastern China.

City types around the world

Lead researcher Jason Thompson, PhD, from the University of Melbourne’s Transport, Health and Urban Design Research Hub said the research emphasized the importance of urban design as a big picture determinant of health. City footprints that discourage public transit had greater injury incidence, possibly due to more motor vehicle use. City footprints that discourage safe road use (for example by combining pedestrian and motor vehicle thoroughfares) had similarly high injury incidence, possible due to more chaotic roadways. Preventive intervention is clearly a long term prospect – redesigning cities is not going to happen immediately – but over time, building cities that preference public transit, pedestrian traffic, and safe roadway use will reduce injuries.

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Heavy and binge alcohol drinking and parenting status in the United States from 2006 to 2018

Social and Spatial Epidemiology Unit members Sarah McKetta and Katherine Keyes recently published research in PloS Medicine regarding national trends in binge and heavy drinking.

A wave of recent media attention has raised alarms that women with children are drinking more.  So-called “mommy drinking” reports have largely circulated within the mommy blogosphere and lifestyle media, with reports citing concerns about “mommy wine time,” “mommy juice,” and associated branding of alcohol consumption as self-care. No empiric research has actually examined whether moms are actually drinking more.

McKetta and Keyes used data from the National Health Interview Survey (NHIS) from 2006 to 2018 to examine trends in drinking for moms. Because drinking trends in recent decades have varied by gender and age as well, they looked at 3 different age groups (18-29, 30-44, 45-55) as well as men and women, according to parenting status. The alcohol use outcomes they considered were past-year binge drinking—defined as 5 or more drinks per day for men, 4 or more for women—and heavy drinking, defined as 60 or more binge episodes per year, or an average of 5 or more per month. They also considered trends in abstaining from drinking, because measurement of binge drinking changed for women (from 5 to 4 or more drinks per day) during the study period, and looking at abstinence allowed us to confirm that binge trends weren’t an artifact of measurement variance.

They found that in general, men report higher rates of binge and heavy drinking than women, and people without kids report higher rates of binge and heavy drinking than those who have kids. So, moms overall had the lowest rates of binge and heavy drinking in all age categories.

However, looking at trends over time (figure below), women increased their rates of binge drinking from 2006 to 2018 at higher rates than men. In fact, the youngest men (ages 18-30) declined their binge drinking rates, while all women evidenced increases.

The increases in women’s binge drinking through time were not differential by parenting status. The probability of binge drinking increased among moms over the years, but these increases through time paralleled those seen among women without children.

Nearly everyone decreased heavy drinking or remained at the same rates over the study period, suggesting that while more women were binging at least once per year, they were not binging repeatedly. Trends in abstinence closely mirrored trends in binge drinking for all groups: as binge drinking increased, abstinence declined; abstinence increased only among young men, who also decreased binge drinking.

In sum, it seems that we don’t need to be worrying about binge drinking specifically for moms; we need to be worrying about binge drinking for all adults. Alcohol is a leading cause of preventable mortality and morbidity, and binge episodes increase risks of injury and death. The group traditionally considered high risk for binge drinking – young, college-aged men—are the only ones who have decreased rates of binge drinking, and childless young men nevertheless binge drink at the highest rates of anyone. These trends are worrying, and physicians should be screening all patients for alcohol consumption.

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Moving through the City: Understanding the neighborhoods transgender women live and socialize in.

Geospatial research can help scientists and public health officials better understand a diverse range of health risks and outcomes. In their recent paper published in Geospatial Health, however, Columbia’s Spatial Epidemiology Lab (led by Dustin Duncan) points out that the bulk of existing geospatial health research has focused solely on where people’s homes are located, and in doing so ignored all the other places where people carry out their lives. Think about your own movements over the past week: it’s likely that you have spent time in numerous neighborhoods that include where your house or apartment is located but also where you work, go to school, run errands and socialize.

Experts in geospatial health have referred to the tendency of researchers to focus on home neighborhoods as the ‘residential trap’. To escape from this trap, William Goedel (lab affiliate) conducted a small study that asked 14 transgender women based in New York City to carry global positioning system (GPS) devices for one week. During that week the devices used GPS’s satellite-based technology to log location coordinates every 10 seconds, creating a dataset with which to identify the neighborhoods that participants visited and moved through.

The GPS devices proved to be very important. Participant’s had considerable mobility beyond their home neighborhoods. Every participant visited at least two different New York City boroughs and had ‘activity spaces’ (the area covered by their movements throughout the week) ranging from around 1.5 to 19 square miles. That is a huge! Manhattan itself is just under 23 square miles in size, and the research team concluded that neighborhood exposures would not have been fully captured if participants had merely reported their addresses and movement. The team also highlighted the utility of the GPS devices for understanding the movements of people who may not have stable housing and, therefore, no fixed home address, which was the case for nearly half of the study’s participants.

The research team took their geospatial analysis further by assessing how common it was for participants to visit neighborhoods prevalent with HIV. In the United States, transgender women have the highest HIV rates of almost any group, and so a geospatial understanding of the virus is particularly important. By linking participants’ movement data to publicly available data on HIV prevalence, the research team found that 58% of GPS coordinates were located in neighborhoods that, in New York City, have the highest prevalence of HIV.

Although the study authors were clear that their findings do not explain why HIV rates are so high among transgender women, they do hint at the potential relevance of considering neighborhood-level HIV prevalence. What this might mean, however, for HIV risk and prevention is difficult to say, especially because HIV rates in this study may have served as a proximal marker for the distribution of sexual and gender minority people in New York City more generally. Regardless, this study confirms the incredible value of using GPS to characterize neighborhood exposures, an approach that could prove useful in future research and public health efforts to reduce HIV and otherwise improve the health and well-being of transgender women.

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Criminogenic or criminalized? Testing an assumption for expanding criminogenic risk assessment.

Proponents of criminogenic risk assessment and algorithmic prediction for criminal recidivism have called for its widespread expansion throughout the criminal justice system, including for setting bail, pre-trial detention decisions, sentencing, and even policing. Its success in predicting recidivism is taken as evidence that criminogenic risks tap into the causes of criminal behavior even for first offenses, and that targeting these factors can reduce correctional supervision rates and even prevent crime.  However, recent work by Seth Prins suggests that these algorithms for predicting recidivism after release from incarceration cannot be applied wholesale across the various decision points in the criminal justice system.  His findings suggest that current risk assessments for recidivism cannot fully distinguish between individuals’ propensities for committing crime and the fact that they have already been criminalized by a runaway criminal justice system.  In the era of mass incarceration, the idea that risk factors for staying trapped in the criminal justice system are the same as the risk factors for initial exposure to the system ignores all the social, economic, and policy-related factors that have nothing to do with individual characteristics. He argues that we need to focus on what puts people at risk of criminogenic risk, and one of those things, arguably, is current criminal justice policy.

In related news, Seth and his colleague Brett Story also did an interview with WBAI on  how many of the policies and programs proposed in the Green New Deal can also put us on the path to decarceration and healthequity.

Seth also created the Mass Incarceration Mix on Social Epi Radio and the Mass Incarceration Info-Graphix hosted on this web-site.


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Body Mass Index across the Life-Course: Emergence of Race by Sex Disparities in Early Childhood.

In the U.S. 35% of adults aged 20 years or older are obese and the obesity epidemic represents a critical public health issue.  There are marked disparities in body mass index (BMI) and obesity prevalence by race/ethnicity and sex.  Among men the age adjusted prevalence of obesity is modestly higher among Non-Hispanic Blacks and Hispanics than among non-Hispanic Whites, while among women the prevalence of obesity is substantially higher among Non-Hispanic Blacks and Hispanics than among non-Hispanic Whites.  Non-Hispanic Black women have the highest prevalence of obesity of any racial, ethnic, sex group, a disparity that has been in place for several decades, with an age adjusted obesity prevalence of 57% in 2011-2012.

Work by Andrew Rundle and colleagues recently published in the Annals of Epidemiology used data from the Child Health and Development Studies (CHDS) to assess when in the life course the race by gender disparity in BMI for Blacks and Whites begins. CHDS participants were born in the early 1960s and height and weight data were collected at ages 5, 9-11 and 15-17.  Six hundred and five CHDS participants were recently follow-up again at age ~50 and height and weight were measured. Analyses of these data showed that the race by gender disparity in BMI was present by age 9-11 years and continued at ages 15-17 and 50 years, with Black women having the highest BMI scores.  A large proportion of the race by sex disparity in BMI at age 50 could be accounted for by the participant’s BMI at age 9-11.

1. Socio-demographic covariates were: paternal and maternal education, maternal pre-pregnancy BMI, participant obtained a college degree, participant’s smoker/non-smoker status at age 50 and participant age of assessment

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“You probably can’t feel as safe as normal women”: Hispanic women’s reactions to breast density notification

A new qualitative study by Alsacia Pacsi-Sepulveda, Rachel Shelton, Carmen Rodriguez, Arielle Coq, and Parisa Tehranifar that explored the understanding of, and reactions to, New York State’s breast density notification language was recently published in the journal Cancer. In 2013, New York State enacted legislation requiring that women receive a written letter if a mammogram reveals they have heterogeneously or extremely dense breasts. The letter is intended to notify them of their density status, increased risk of breast cancer, and lower sensitivity of mammography screening. A paragraph of mandated text containing this information is specified in the legislation.

The researchers asked 24 self-identified Hispanic women who had a history of dense breasts about their understanding of the NYS mandated breast density information, directly after reading them the mandated notification text. All respondents had received screening mammograms since the law went into effect, but most did not recall receiving notification and showed low levels of understanding of breast density.

Using inductive content analysis, the researchers identified five overarching themes that arose in their interviews, including: confusion about, and lack of understanding of, what ‘dense breasts’ means; the perception that dense breasts are an abnormal and serious condition; and worries about breast cancer risk and the need for additional tests for screening. Additional themes found were a “reliance on faith and acceptance of destiny” – that dense breasts are something predetermined, and that the information learned in the notification was important and actionable – that more diligent screening or additional testing was important for them.

Importantly, respondents also provided recommendations for communicating breast density information, stating that health care providers were the best source of this information, but that written informational materials, videos, ads, or community programs, were also recommended.

The researchers suggest that revising the notification text to lower the literacy level and include a definition of breast density or other clarifying information could assist women in their understanding of this notification.

In Feb 2019, a US federal law was passed requiring the FDA to develop mandatory reporting language that must be included in patient and provider mammography reports across the US. [source]. The minimum information that is required includes:

  • The effect of breast density in masking the presence of breast cancer on a mammogram
  • The qualitative assessment of [breast density by] the provider who interprets the mammogram, and
  • A reminder to patients that individuals with dense breast tissue should talk with their providers if they have any questions or concerns about their summary.

Though the mandated information is specified, specific language and text to be used is not. The authors’ findings and recommendations have implications for informing the specific language used in notification letters directed towards patients to ensure that the information is understood and actionable.

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Neighborhood Health Effects: Does The Way We Define “Neighborhood” Alter the Effect?

There are many different ways that aspects of the social and physical environment can affect a person’s health. For example, body mass index and chronic disease are associated with the walkability of the area where a person lives. Spending more time near fast food outlets is associated with greater saturated fat intake. Being present in neighborhoods with more decay and disorder is related to increased risks for assault. The studies that identified these associations all link individuals to their neighborhood environments in some way. But if the researchers had used different approaches to make this link, would they have arrived at the same findings?

Christopher Morrison and his colleagues from the Prevention Research Center in Berkeley, CA, published a paper in Epidemiology that addressed this question. They used one month of GPS data for 231 adolescents aged 14 to 16, and tested whether exposure to retail alcohol outlets was associated with increased alcohol consumption. The group used three different approaches to link the environmental condition (alcohol outlets) to the individuals. They measured exposure around the person’s home, around the places they attended most frequently, and around the full GPS route path.

The researchers found that the different measures of exposure to alcohol outlets were, at best, moderately correlated, which means they probably all measure different constructs. Perhaps more importantly, the different measures produced different associations with alcohol outlets—in some cases the exposure was positively related to the outcome, and in others there was no association. It seems the way we measure individuals’ exposures to environmental conditions could very substantially affect the results of a given study.

Activity space captured by GPS depicted in space and time. Residence-based measures, activity location-based measures, and activity path-based measures of exposure to alcohol outlets can be derived from the data.

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