We recently published a paper in Current Epidemiology Reports describing how the case-only design is commonly misinterpreted in injury epidemiology. Due to the availability of registries and Emergency Department medical record databases, case-only studies are common in the injury epidemiology literature. The term “case-only design” covers a variety of epidemiologic designs, with two of the designs being prominent in the injury epidemiology literature; (1) studies to measure exposure effect modification, and (2) studies to uncover etiological heterogeneity. Although the mechanics of conducting these two study designs are quite similar, the two designs produce results that require completely different interpretations and rely upon different assumptions. Despite this, in the literature it is common for the results of studies using these two designs to be interpreted in the same way and it is rare that the papers address whether the key assumptions are met.
We show that the key assumption of case-only designs for exposure effect modification, the more commonly used of the two designs, does not commonly hold for injuries and so results from studies using this design cannot be interpreted. Our paper includes a series of recommendations for the conduct and reporting of case-only designs seeking to test for exposure effect modification. However, we are quite pessimistic that this design can be effectively used to understand the etiology of injuries or for designing interventions.
Although less commonly used, case-only designs to identify etiological heterogeneity in injury risk are interpretable but only when the case-series is conceptualized as arising from an underlying cohort. However, in the literature the results of studies of this design are often not interpreted correctly. But we do expect that if these studies are interpreted correctly, they can be used to understand the etiology of injuries or for designing interventions.
After almost exactly three years of work we are closing down our online interactive COVID-19 data visualization and mapping tools that show the Shaman Lab’s weekly projections of COVID-19 case-loads for the coming six weeks. We are doing this, not because we think the pandemic is over, but because data that are required to make these projections are no longer being systematically collected. As such the Shaman Lab has halted its work creating COVID-19 case load projections and we are shutting down our visualization tools. Now we will all need to make do with the CDC’s cumulative data on cases and deaths that are updated weekly, and daily data on hospitalizations.
Back on March 16th 2020 we began posting county-level maps of the U.S. depicting areas with large populations vulnerable to severe COVID-19 due to older age or having underlying health conditions. On the 17th, in partnership with PolicyMap, we set up our first online interactive mapping tool for COVID-19 vulnerability, and on March 24th we used ESRI’s online mapping tool to launch our own online interactive mapping tool. Our mapping portal initially depicted populations at high risk of severe COVID-19 due to older age or underlying health conditions, the availability of ICU beds, the ratios of high risk populations to ICU beds and the numbers of ICU beds that could be made available under various surge response scenarios (our paper on this work is here). On April 13th we released an online interactive mapping tool providing data on food insecurity and SNAP distribution schedules and policies (our paper on this work is here).
By April 2020, we had partnered with the Shaman lab to provide online data visualizations and interactive maps depicting the lab’s 6-week look forward projections of coming COVID-19 case loads, for every county in the U.S. Since then we have refreshed our data visualizations and map data every Monday with the latest COVID-19 projections from the Shaman lab. These tools and the underlying data were used by local, state and federal government, Congress, the military, corporations and individual users to understand the likely coming course of the pandemic in their area. We also provided customized data analytics, data visualization and interactive mapping tools to City of Stamford CT to support their COVID-19 response efforts (our paper on these efforts is here).
We recently published a paper in Current Epidemiology Reports on the use of registry data in injury epidemiology. Injury data are frequently captured in registries that form a census of 100% of known cases that meet specified inclusion criteria. We reviewed study designs commonly used with data extracted from injury registries: (1) Description, (2) Ecologic (with Ecologic Cohort as a particularly informative sub-type), (3) Case-control (with location-based and culpability studies as salient subtypes), (4) Case-only (including case-case and case-crossover subtypes), and (5) Outcomes. Epidemiologic analyses of registry data rest on the understanding that over time an underlying, unenumerated, dynamic population cohort generates cases and data from these cases are recorded into a register. Seeing the registry as the result of an underlying cohort aids in designing studies using the registry data.
Understanding registry data as being derived from a cohort study aides in study design.
Different design choices in analysis of these registries’ data affect the results’ interpretation. When using registry data the key first step for a researcher is to choose which counterfactual (if any) within which unit of analysis is of interest – that is, what does the researcher imagine could be changed and at what level of organization (e.g. person, neighborhood, state, etc.) to prevent injuries or improve injured parties’ outcomes. Working from this hypothetical counterfactual, units might be individual people (e.g. when studying characteristics of the injured party or an at-fault party) interventions on individual people (e.g. when studying treatments received in post-injury care) or individual places (e.g. when studying the physical environment at the location of the injury event). Analytic units could also be groups of people or places, (e.g. when studying states included in an ecological cohort). The choice of counterfactual and unit of analysis is fundamental to the scientific process, impacting the conceptualization of the underlying at-risk population, the comparison of interest, whether selected variables should be considered confounders, mediators or effect modifiers, and the interpretation of any estimated effects.
Continuing our partnership with the NYC Department of Health and Mental Hygiene to study how urban built environments influence health during pregnancy we recently published research showing that higher neighborhood walkability is associated with lower risk of gestational diabetes. Gestational diabetes (GD) is a form of diabetes that develops during pregnancy and in 2020 was diagnosed in 7.8% of pregnancies in the United States. GD increases infants’ risk for being large for gestational age, may increase risk of unhealthy weight gain during childhood, and increases the pregnant individual’s risk for future type 2 diabetes. Physical activity prior to and during pregnancy are associated with lower risk of GD and data suggest that even light physical activity and walking have protective effects.
The analyses included data from 109,863 births recorded in NYC in 2015, and 7.5% of pregnant individuals in the data set experienced GD. We measured walkability using our Neighborhood Walkability Index and using the Density of Walkable Destinations (see here) for 1Km radius circular areas around the residential Census block of the pregnant individuals. Risk of GD was significantly lower (RR = 0.81) for pregnant individuals living in neighborhoods in the fourth and compared to the 1st quartile of Neighborhood Walkability Index score and was significantly lower (RR = 0.77) for pregnant individuals living in neighborhoods in the 4th compared to the 1st quartile of Density of Walkable Destinations.
Given the long-lasting benefits of healthy pregnancies for the parent and the child, this research motivates the use of urban design to support healthy pregnancies. If further research replicates the findings presented here, supporting healthy pregnancies should be factored into cost-benefit analyses of built environment interventions to create walkable neighborhoods.
Her most recent work with BEH was leading a historical analysis of disparities in spatial access to social service agencies across the U.S. from 1990 to 2014 (available here). The work showed that a spatial mismatch emerged between the availability of social services and the expected need for social services as the population characteristics of neighborhoods changed. High poverty tracts that experienced further economic decline from 1990 to 2010, began the period with the lowest access to services and experienced the smallest increases in access to services. Access was highest and grew the fastest in high poverty tracts that experienced the largest increases in median household income. We theorize that agglomeration benefits and the marketization of welfare may explain the emergence of this spatial mismatch.
We recently published a review article in Current Epidemiology Reports describing the use of machine learning to measure neighborhood environments in epidemiologic studies. Innovations in information technology, initiatives by local governments to share administrative data, and growing inventories of data available from commercial data aggregators have immensely expanded the information available to describe neighborhood environments, supporting an approach to research we call Urban Health Informatics
To address patient’s unmet social needs and improve health outcomes, health systems have developed programs to refer patients in need to social service agencies. However, the capacity to respond to patient referrals varies tremendously across communities. To understand how disparities in spatial access to social service agencies arose we used the National Establishment Time Series (NETS) data set to analyze the density of social service agencies (agencies/Km2), annually, in all populated Census tracts in the U.S. from 1990 to 2014. Our paper describing this work was published in BMC Health Services Research.
Throughout the period, social service agencies/Km2 increased within tracts, with tracts experiencing the highest poverty rates in 1990 having the highest density of agencies through the 1990 to 2014 time-period. But from 1990 to 2014 a spatial mismatch emerged between the availability of social services and the expected need for social services as the population characteristics of neighborhoods changed. Tracts that experienced high poverty in 1990 and then experienced the steepest declines in household income through 2010, had the lowest access to social service agencies in 1990 and the smallest increases in access over the years. Conversely, high poverty tracts that experienced the largest gains in household income from 1990 to 2010 began the period with the highest density of agencies and gained the most agencies through the study time period.
We theorize that agglomeration economics benefits and the marketization of welfare may explain the emergence of this spatial mismatch between expected population need for services and the availability of services. Agglomeration economics posits that there are advantages when similar businesses and institutions locate near one another and near to other physical and commercial resources that will support the mission of these institutions. Agglomerative effects may create centers of gravity that increasingly concentrate service providers in certain neighborhoods through time. The marketization of welfare describes social service providers’ increasing budgetary reliance on fee for service activities. Service providers who partially rely on fee for service activities may be particularly attracted to high poverty areas that are on an upwards economic trajectory and gaining residents who can afford to pay for services.
Agglomeration benefits predict that social services will spatially cluster and further analyzes of our data suggested that clustering of agencies was linked to other elements of urban built form, such as a more robust retail, commercial and institutional environment and access to rail transit. As hospitals and health care systems are increasingly becoming stakeholders in local urban planning, zoning and economic development decisions, they should consider how decisions about urban form may influence spatial access to social services. Hospital system’s advocacy for transit-oriented design and mixed-land use may create the conditions that attract social service agencies into hospital catchment areas. Given the significant role that market forces play in determining the placement of services, attention to interdisciplinary theories across urban planning, economics and social and health services research is needed to improve spatial access to social services.
In partnership with the NYC Department of Health and Mental Hygiene we have been studying how neighborhood environments influence health during pregnancy and birth outcomes, with recent work focusing on weight gain during pregnancy. In 2009, the Institute of Medicine (IOM) issued revised recommendations for healthy gestational weight gain (GWG). However, despite the new guidelines, most pregnant individuals in the U.S. still do not gain the recommended amount of weight during pregnancy; almost 50% of pregnant individuals gain more weight than is recommended for a healthy pregnancy. Excessive GWG is associated with higher risk of pregnancy complications, including pregnancy-related hypertension and greater long-term postpartum weight retention. Excessive GWG is also associated with increased odds of child asthma, obesity, and greater percent body fat and abdominal adiposity.
Using birth record data from all births in New York City in 2015 we found that higher neighborhood walkability was associated with lower risk of excessive gestational weight gain. This protective effect was seen after controlling for the pregnant individual’s, age, race, place of birth, and education and the poverty rate in the residential neighborhood. Further analyses that adjusted for pre-pregnancy body mass index suggest that the link between neighborhood walkability and lower risk of excessive gestational weight gain was due to differences in physical activity patterns, especially walking, during pregnancy. This interpretation is consistent with past studies that find pregnant individuals favor lower intensity forms of exercise such as walking and that walking activity during mid-pregnancy is associated with lower risk of excessive gestational weight gain. We have previously shown that higher neighborhood walkability is associated with more walking and more total physical activity. The paper describing our research on gestational weight gain was published in the journal Obesity.
Multiple guidelines exist for planners and architects on how to design for health, including the NYC Active Design Guidelines, the WELL Community Standard, the American Institute of Architects Healthy Design Research Consortium, and the Department of Health and Human Service’s Healthy People 2020 guidelines. However, due to limited research on the implications of active design for health during pregnancy, few such guides consider pregnant individuals and their infants. Given the long-lasting benefits of healthy pregnancies for parental and child health, this research provides further impetus for the use of urban design to support healthy weight and reduce the risk of excessive gestational weight gain and associated health risks.
Imagine if a clinical researcher were to disclose a list of patient addresses to a third-party – government agency, for profit company or not-for-profit entity – that was outside of their hospital or health system. Imagine the researcher then publicly announced they disclosed the addresses to the third party, that the addresses belonged to patients with a specific disease, and that those patients were being treated at a specific hospital. The researcher’s Institutional Review Board (IRB) and Health Insurance Portability and Accountability Act (HIPAA) compliance office would be outraged at these violations of patient privacy. Yet this sequence of events can happen inadvertently when studying how neighborhood conditions such as access to medical facilities or neighborhood food environments affect clinical outcomes in specific patient populations. A quick search of Google Scholar shows many articles that, through this sequence of events, have disclosed patient health data.
In a recent pre-press publication we show how geocoding patient or study subject addresses using a variety of R packages, STATA, SAS and QGIS can set of a cascade of events that discloses Personal Identifying Information (PII) and Protected Heath Information (PHI) in violation of usual IRB and HIPAA rules. We also show the flaws in several approaches proposed to protect PII and PHI in neighborhood health effects research and propose best practices to protect patient and study subject confidentiality in studies on neighborhood health effects.