OBJECTIVE: To investigate the association between individual-level and neighborhood-level risk factors and severe maternal morbidity. METHODS: This was a retrospective cohort study of all pregnancies delivered between 2010 and 2017 in the University of Pennsylvania Health System. International Classification of Diseases codes classified severe maternal morbidity according to the Centers for Disease Control and Prevention guidelines. Logistic regression modeling evaluated individual-level risk factors for severe maternal morbidity, such as maternal age and preeclampsia diagnosis. Additionally, we used spatial autoregressive modeling to assess Census-tract, neighborhood-level risk factors for severe maternal morbidity such as violent crime and poverty. RESULTS: Overall, 63,334 pregnancies were included, with a severe maternal morbidity rate of 2.73%, or 272 deliveries with severe maternal morbidity per 10,000 delivery hospitalizations. In our multivariable model assessing individual-level risk factors for severe maternal morbidity, the magnitude of risk was highest for patients with a cesarean delivery (adjusted odds ratio [aOR] 3.50, 95% CI 3.15–3.89), stillbirth (aOR 4.60, 95% CI 3.31–6.24), and preeclampsia diagnosis (aOR 2.71, 95% CI 2.41–3.03). Identifying as White was associated with lower odds of severe maternal morbidity at delivery (aOR 0.73, 95% CI 0.61–0.87). In our final multivariable model assessing neighborhood-level risk factors for severe maternal morbidity, the rate of severe maternal morbidity increased by 2.4% (95% CI 0.37–4.4%) with every 10% increase in the percentage of individuals in a Census tract who identified as Black or African American when accounting for the number of violent crimes and percentage of people identifying as White. CONCLUSION: Both individual-level and neighborhood-level risk factors were associated with severe maternal morbidity. These factors may contribute to rising severe maternal morbidity rates in the United States. Better characterization of risk factors for severe maternal morbidity is imperative for the design of clinical and public health interventions seeking to lower rates of severe maternal morbidity and maternal mortality.

The Covid-19 pandemic is more than a health crisis. It has worse outcomes among individuals with co-morbidities, has exposed fault lines in our societies, and amplified existing inequalities. This article draws on emerging evidence from low- and middle-income contexts to highlight how Covid-19 becomes syndemic when it interacts with local vulnerabilities. A syndemic approach provides a frame for understanding how Covid-19 is amplified when clustered with other diseases and how this clustering is facilitated by contextual and social factors that create adverse conditions. Public health responses to Covid-19 have also exacerbated these adverse conditions as many face social and economic crises as a result of some policies. These multiple challenges generate major implications for both the public health response and for broader development action: first, one size does not fit all and we must attend to local vulnerabilities; second, short-term public health response and longer-term development approaches must be integrated for improved intersectoral coordination and synergy. A synergised public health and development response will allow us to better prepare for the next pandemic.

Background Exposure to fine particulate matter (PM2.5) increases the risk of asthma exacerbations, and thus, monitoring personal exposure to PM2.5 may aid in disease self-management. Low-cost, portable air pollution sensors offer a convenient way to measure personal pollution exposure directly and may improve personalized monitoring compared with traditional methods that rely on stationary monitoring stations. We aimed to understand whether adults with asthma would be willing to use personal sensors to monitor their exposure to air pollution and to assess the feasibility of using sensors to measure real-time PM2.5 exposure. Methods We conducted semi-structured interviews with 15 adults with asthma to understand their willingness to use a personal pollution sensor and their privacy preferences with regard to sensor data. Student research assistants used HabitatMap AirBeam devices to take PM2.5 measurements at 1-s intervals while walking in Philadelphia neighborhoods in May–August 2018. AirBeam PM2.5 measurements were compared to concurrent measurements taken by three nearby regulatory monitors. Results All interview participants stated that they would use a personal air pollution sensor, though the consensus was that devices should be small (watch- or palm-sized) and light. Patients were generally unconcerned about privacy or sharing their GPS location, with only two stating they would not share their GPS location under any circumstances. PM2.5 measurements were taken using AirBeam sensors on 34 walks that extended through five Philadelphia neighborhoods. The range of sensor PM2.5 measurements was 0.6–97.6 μg/mL (mean 6.8 μg/mL), compared to 0–22.6 μg/mL (mean 9.0 μg/mL) measured by nearby regulatory monitors. Compared to stationary measurements, which were only available as 1-h integrated averages at discrete monitoring sites, sensor measurements permitted characterization of fine-scale fluctuations in PM2.5 levels over time and space. Conclusions Patients were generally interested in using sensors to monitor their personal exposure to PM2.5 and willing to share personal sensor data with health care providers and researchers. Compared to traditional methods of personal exposure assessment, sensors captured personalized air quality information at higher spatiotemporal resolution. Improvements to currently available sensors, including more reliable Bluetooth connectivity, increased portability, and longer battery life would facilitate their use in a general patient population.

Environmental disasters are anthropogenic catastrophic events that affect health. Famous disasters include the Seveso disaster and the Fukushima-Daiichi nuclear meltdown, which had disastrous health consequences. Traditional methods for studying environmental disasters are costly and time-intensive. We propose the use of electronic health records (EHR) and informatics methods to study the health effects of emergent environmental disasters in a cost-effective manner. An emergent environmental disaster is exposure to perfluoroalkyl substances (PFAS) in the Philadelphia area. Penn Medicine (PennMed) comprises multiple hospitals and facilities within the Philadelphia Metropolitan area, including over three thousand PFAS-exposed women living in one of the highest PFAS exposure areas nationwide. We developed a high-throughput method that utilizes only EHR data to evaluate the disease risk in this heavily exposed population. We replicated all five disease/conditions implicated by PFAS exposure, including hypercholesterolemia, thyroid disease, proteinuria, kidney disease and colitis, either directly or via closely related diagnoses. Using EHRs coupled with informatics enables the health impacts of environmental disasters to be more easily studied in large cohorts versus traditional methods that rely on interviews and expensive serum-based testing. By reducing cost and increasing the diversity of individuals included in studies, we can overcome many of the hurdles faced by previous studies, including a lack of racial and ethnic diversity. This proof-of-concept study confirms that EHRs can be used to study human health and disease impacts of environmental disasters and produces equivalent disease-exposure knowledge to prospective epidemiology studies while remaining cost-effective.

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