In collaboration with researchers from the New York City Department of Health and Mental Hygiene we recently published an article in the American Journal of Epidemiology showing that diabetics living in neighborhoods with more advantaged economic environments, greater walkability and healthier retail food environments have an improved ability to achieve glycemic control. Hemoglobin A1C data from the New York City A1C Registry for 182,756 adults who had 1,273,801 A1C tests from 2007 to 2013 were analyzed along with data describing the neighborhood contexts they lived in. The odds of individuals achieving glycemic control in the most advantaged residential neighborhoods (better economic conditions, greater walkability, healthier retail food profiles) was two and a half times greater than in the least advantaged. Furthermore, individuals who lived in the most advantaged residential neighborhoods achieved glycemic control in a shorter period of time than individuals who lived in the least advantaged neighborhoods. For those who moved during the 2007 to 2014 period, moving from less advantaged neighborhoods to more advantaged neighborhoods was associated with improved diabetes control, while moving from more advantaged areas to less advantaged areas was related to worsening diabetes control. This is the first longitudinal study to examine the relationship between residential neighborhood environments and individual’s ability to control their diabetes.
Undergraduate programs in public health are proliferating (and see here), and increasing numbers of undergraduate students are receiving training in epidemiology. James Stark, a BEH alum and now a Director of Epidemiology at Pfizer and Adjunct Professor at NYU’s College of Global Public Health, just published a paper in the American Journal of Epidemiology on approaches to teaching epidemiology at the undergraduate level.
Epidemiology courses introduce undergraduate students to a population health perspective and provide opportunities for these students to build essential skills and competencies such as ethical reasoning, teamwork, comprehension of scientific methods, critical thinking, quantitative and information literacy, ability to analyze public health information, and effective writing and oral communication.
The post-doc position will be at Columbia University, but we are a multi-disciplinary team of faculty at the Mailman School, the Columbia University School of Social Work, Drexel University, American University and the University of Washington. Strong candidates will have a Doctorate and experience and interest in social epidemiology, spatial epidemiology, neighborhood health research, GIS and/or urban health. Post-Docs will be able to collaborate on on-going and developing BEH projects and to develop their own research projects. On-going and developing BEH projects focus; 1) on the links between neighborhood built environments and obesity, physical activity, pedestrian safety, asthma, cancer, and cardio vascular disease, and 2) on developing new methods for characterizing neighborhood environments.
Please contact Andrew Rundle at email@example.com
Although many health determinants are outside of the health care sector, quality health care is crucial to population health. Recently, we included a look at perceptions of local health care as part of a community needs assessment (https://beh.columbia.edu/community-needs-assessment/) in Rio de Janeiro’s third largest favela. At the time of collecting data in 2015, only a geographic area with approximately 40% of the population was covered by the local family health clinic, and we thus expected that those outside of the coverage area might perceive greater barriers to care.
However, when talking about public health care with residents during 14 semi-structured interviews, our colleague Débora Castiglione noted other salient concerns. She led the qualitative analyses and coauthored with Dr. Lovasi a paper just published in Qualitative Health Research. This paper highlights that residents felt disrespected or dehumanized in the process of seeking health care from the public system. Substantial delays and appointments missed due to the doctor’s absence were perceived as exacerbating vulnerability faced by pregnant women or during injury recovery, extending periods of uncertainty, elevated risk, or disability. Even in the face of scarce financial resources, residents would pay for private care if they could, in order to get timely care and feel well-received.
We’ve done a lot with Street View at the BEH, and we think the CANVAS application we developed to help teams do reliable and efficient virtual audits works pretty well. But we never really knew what we might be missing by not being on the street in person.
Fortunately, we stumbled across an opportunity to investigate what we might be missing. It happens that our friends at the Detroit Neighborhood Health Study (DNHS) had conducted an in-person systematic audit of Detroit streets only one year prior to when Google captured the imagery that we’d audited on Street View, and the DNHS team had constructed a physical disorder measure from their work.
So we took a look at how their measure aligned with ours, and together, we and the DNHS team recently wrote a paper on what we found. Spolier alert: both methods showed pretty similar spatial patterns of disorder — the final measures were significantly positively correlated at census block centroids (r=0.52), identified the same general regions as highly disordered (see attached image) and displayed comparable leave-one-out cross-validation accuracy.
But the two methods didn’t take the same amount of auditor time – the virtual audit required about 3% of the time of the in-person audit, largely because the virtual audit was able to take a more diffuse sample of the streets because travel time between segments was not a factor in developing an audit sample.
There were a number of other differences between the audit designs, including that the CANVAS audit included more disorder indicators and the DNHS audit aggregated street-level measures to create neighborhood area measures before interpolating. So it wasn’t a completely apples-to-apples comparison and the 97% of auditor time saved might not apply for other audit contexts. Nonetheless, virtual audits do appear to permit comparable validity with more diffuse samples.
Ultimately, we concluded that the virtual audit-based physical disorder measure could substitute for the in-person one with little to no loss of precision. Jackelyn Hwang wrote a thoughtful commentary on our paper and on technological innovation in neighborhood research more generally, and we responded to her thoughts.
Dr. Rundle’s March 2nd webinar for the ISBNPA webinar has been posted online at ISBNPA’s web site (Here and embedded below).
His talk covered different approaches to assessing neighborhood walkability and the link between urban design and resident’s physical activity using New York City as a case study. He highlighted the challenges to measuring neighborhood form across multiple municipal jurisdictions and retrospectively over the past three decades.
On Thursday March 2nd at 3pm EST, Dr. Rundle will give a webinar entitled “Urban Informatics: Studying How Urban Design Influences Health in New York City” for the International Society of Behavioral Nutrition and Physical Activity. You can register for the webinar HERE.
Dr. Rundle will discuss different approaches to assessing neighborhood walkability and the link between urban design and resident’s physical activity using New York City as a case study. His talk covers the usage of large administrative and commercial datasets and geospatial tools to characterize neighborhood built environment features; global positioning systems (GPS) and accelerometers to measure individual’s behaviors; and epidemiologic methods to understand how differences in neighborhood characteristics influence the health of residents. He will highlight challenges to measure neighborhood form across multiple municipal jurisdictions and retrospectively over the past three decades.
Through the years, we have done a fair amount of work to collect and validate measures of neighborhood physical disorder – urban deterioration – using our CANVAS/Google Street View system. Neighborhood disorder is controversial construct and measure, not only because perceptions of what constitutes disorder can vary sharply between people – one person’s chaotic urban jungle is another person’s lively street scene – but also because the impacts of disorder – does disorder induce crime or is it just correlated with crime because the two share common causes such as neighborhood disadvantage? – are unclear.
One possible impact of disorder may be deterring physical activity, especially walking. In walk-along studies with older adults – wherein a researcher takes a walk with a study participant and asks about the neighborhood characteristics and how they affect the walker’s experience –many participants report that they don’t like or are threatened by indicators of disorder, such as graffiti, poor building maintenance, and similar signs of abandonment. But there has been relatively little rigorous longitudinal, population-based research on the extent to which disorder is a barrier to physical activity in practice. This is important, because if disorder is a major barrier to activity, then removing disorder, perhaps through aggressive blight removal and related clean-up programs, may have substantially positive implications for the health of aging populations trying to maintain active lifestyles.
We recently published a study using our disorder measure with data from the New York City Neighborhood and Mental Health in the Elderly Study (NYCNAMES-II), a three-wave cohort study of about 3,500 adults aged 65-75 at baseline, to see whether disorder seems to be impeding physical activity among older New York City residents. Too few study participants moved over the two years of follow-up for us to reliably assess the impact of moving from a neighborhood at one level of disorder to another, but we did find that, comparing across subjects, each standard deviation increase in neighborhood disorder (the difference between a neighborhood with no litter or graffiti to one where both were prevalent) was associated with an decrease in self-reported activity equivalent to about 6 min of walking per day. However, neighborhood physical disorder was not related to changes in physical activity over the two years of follow-up.
Ultimately, it seems that there is some meaningful association between living in a more disordered place and being less physically active, but that neighborhood disorder was not a major cause of decline in physical activity among these older adults. We hope to explore the relationship between disorder and physical activity more deeply in future research using datasets with longer follow-up and more dynamic neighborhood conditions.
As we launched another multifaceted geographic data linkage study our multi-institution team, that includes researchers at Drexel University, Columbia University and the University of Washington, has developed a set of commandments to streamline and harmonize our data management, variable naming and data coding processes.
- Thou shalt not transmit HIPAA/IRB protected data, nor data protected by licensing agreement without PI approval.
Clearly, we both want to be responsible custodians of the data entrusted to us, and avoid getting into trouble. For additional discussion of cautions around the common practice of using online tools to characterize addresses, see our recent commentary.
- Thou shalt always use YYYYMMDD when formatting date variable values, stored as a string.
The date storage was much discussed by our group, but ultimately we wanted a solution that would sort chronologically, be readable to humans, and be usable seamlessly across software that use a different sentinel date.
- Thou shalt always use YYYY when using a year in a variable name.
Given that our studies of adult health frequently span both the 1990s and 2000s, using 4 digits (versus 2 digit) for year when possible allows for easier conversion from wide to long format, and sorting in chronological order.
- Thou shalt prefer use of tall rather than wide data formats to avoid storing empty data and simplify query expressions.
As we move to using longitudinal data on where people live, and how their environment has changed over time, the structure of data becomes more complex. Long format avoids storing fields for which many observations have no data. However, the overarching goal is efficiency and usability, which may at times favor a wide format instead.
- Thou shalt always use lowercase for variable names to avoid case sensitivity issues when jumping between software.
Inconsistent capitalization in variable names is a source of frustration for users of software such as STATA. A typical scenario is that you have working syntax, receive an updated dataset with differences in capitalization (which a user of less case sensitive software packages such as SAS may not be attentive to), and have to spend time troubleshooting and editing to get it to work again. While conventions vary, we decided the simplest thing would be to use only lowercase in our variable names. Continue reading
Following up on its two recent articles about neighborhood walkability, including an editorial co-authored by Andrew Rundle, JAMA today published a Medical News and Perspectives article entitled “As Walking Movement Grows, Neighborhood Walkability Gains Attention”. The article notes the various Federal Agencies that are working on improving neighborhood walkability including: the US Department of Health and Human Services which launched an initiative “Step It Up! The Surgeon General’s Call to Action to Promote Walking and Walkable Communities.”; the CDC funded National Physical Activity Plan Alliance’s forthcoming (expected early 2017) “Walking and Walkability Report Card”; and the collaboration of the USDOT, the CDC, and the American Public Health Association to release the online Transportation and Health Tool, which provides access to data on the health effects of transportation systems and includes a focus on active transport.
In regards to the lack of randomized trial data on neighborhood walkability and the paucity of longitudinal studies in the literature, the article quotes Jim Sallis Sallis saying that even without direct evidence of causality, “the correlational evidence is really piling up.” and that “the risk of improving walkability appears very low, whereas the benefits could be very substantial.”