Characterizing Spatial Variability of Climate-Relevant Hazards and Vulnerabilities in the New England Region of the United States

Pulmonary, inflammatory, and oxidative effects of indoor nitrogen dioxide in patients with COPD

Introduction

Indoor nitrogen dioxide (NO2) sources include gas heating, cooking, and infiltration from outdoors. Associations with pulmonary function, systemic inflammation, and oxidative stress in patients with chronic obstructive pulmonary disease (COPD) are uncertain.

Methods

We recruited 144 COPD patients at the VA Boston Healthcare System between 2012 and 2017. In-home NO2 was measured using an Ogawa passive sampling badge for a week seasonally followed by measuring plasma biomarkers of systemic inflammation (C-reactive protein [CRP] and interleukin-6 [IL-6]), urinary oxidative stress biomarkers (8-hydroxy-2'deoxyguanosine [8-OHdG] and malondialdehyde [MDA]), and pre- and postbronchodilator spirometry. Linear mixed effects regression with a random intercept for each subject was used to assess associations with weekly NO2. Effect modification by COPD severity and by body mass index (BMI) was examined using multiplicative interaction terms and stratum-specific effect estimates.

Results

Median (25%ile, 75%ile) concentration of indoor NO2 was 6.8 (4.4, 11.2) ppb. There were no associations observed between NO2 with CRP, 8-OHdG, or MDA. Although the confidence intervals were wide, there was a reduction in prebronchodilator FEV1 and FVC among participants with more severe COPD (FEV1: -17.36 mL; -58.35, 23.60 and FVC: -28.22 mL; -91.49, 35.07) that was greater than in patients with less severe COPD (FEV1: -1.64 mL; -24.80, 21.57 and FVC: -6.22 mL; -42.16, 29.71). In participants with a BMI <30, there was a reduction in FEV1 and FVC.

Conclusions

Low-level indoor NO2 was not associated with systemic inflammation or oxidative stress. There was a suggestive association with reduced lung function among patients with more severe COPD and among patients with a lower BMI.

The Potential of Surveillance Data for Dengue Risk Mapping: An Evaluation of Different Approaches in Cuba

To better guide dengue prevention and control efforts, the use of routinely collected data to develop risk maps is proposed. For this purpose, dengue experts identified indicators representative of entomological, epidemiological and demographic risks, hereafter called components, by using surveillance data aggregated at the level of Consejos Populares (CPs) in two municipalities of Cuba (Santiago de Cuba and Cienfuegos) in the period of 2010-2015. Two vulnerability models (one with equally weighted components and one with data-derived weights using Principal Component Analysis), and three incidence-based risk models were built to construct risk maps. The correlation between the two vulnerability models was high (tau > 0.89). The single-component and multicomponent incidence-based models were also highly correlated (tau ≥ 0.9). However, the agreement between the vulnerability- and the incidence-based risk maps was below 0.6 in the setting with a prolonged history of dengue transmission. This may suggest that an incidence-based approach does not fully reflect the complexity of vulnerability for future transmission. The small difference between single- and multicomponent incidence maps indicates that in a setting with a narrow availability of data, simpler models can be used. Nevertheless, the generalized linear mixed multicomponent model provides information of covariate-adjusted and spatially smoothed relative risks of disease transmission, which can be important for the prospective evaluation of an intervention strategy. In conclusion, caution is needed when interpreting risk maps, as the results vary depending on the importance given to the components involved in disease transmission. The multicomponent vulnerability mapping needs to be prospectively validated based on an intervention trial targeting high-risk areas.

Agreement in extreme precipitation exposure assessment is modified by race and social vulnerability

Epidemiologic investigations of extreme precipitation events (EPEs) often rely on observations from the nearest weather station to represent individuals' exposures, and due to structural factors that determine the siting of weather stations, levels of measurement error and misclassification bias may differ by race, class, and other measures of social vulnerability. Gridded climate datasets provide higher spatial resolution that may improve measurement error and misclassification bias. However, similarities in the ability to identify EPEs among these types of datasets have not been explored. In this study, we characterize the overall and temporal patterns of agreement among three commonly used meteorological data sources in their identification of EPEs in all census tracts and counties in the conterminous United States over the 1991-2020 U.S. Climate Normals period and evaluate the association between sociodemographic characteristics with agreement in EPE identification. Daily precipitation measurements from weather stations in the Global Historical Climatology Network (GHCN) and gridded precipitation estimates from the Parameter-elevation Relationships on Independent Slopes Model (PRISM) and the North American Land Data Assimilation System (NLDAS) were compared in their ability to identify EPEs defined as the top 1% of precipitation events or daily precipitation >1 inch. Agreement among these datasets is fair to moderate from 1991 to 2020. There are spatial and temporal differences in the levels of agreement between ground stations and gridded climate datasets in their detection of EPEs in the United States from 1991 to 2020. Spatial variation in agreement is most strongly related to a location's proximity to the nearest ground station, with areas furthest from a ground station demonstrating the lowest levels of agreement. These areas have lower socioeconomic status, a higher proportion of Native American population, and higher social vulnerability index scores. The addition of ground stations in these areas may increase agreement, and future studies intending to use these or similar data sources should be aware of the limitations, biases, and potential for differential misclassification of exposure to EPEs. Most importantly, vulnerable populations should be engaged to determine their priorities for enhanced surveillance of climate-based threats so that community-identified needs are met by any future improvements in data quality.

Has the IPCC's revised vulnerability concept been well adopted?

In the Third and Fourth Assessment Reports (TAR and AR4, respectively) by the Intergovernmental Panel on Climate Change (IPCC), vulnerability is conceived as a function of exposure, sensitivity, and adaptive capacity. However, in its Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) and Fifth Assessment Report (AR5), the IPCC redefined and separated exposure, and it reconceptualized vulnerability to be a function of sensitivity and capacity to cope and adapt. In this review, we found that the IPCC's revised vulnerability concept has not been well adopted and that researchers' preference, possible misinterpretation, possible confusion, and possible unawareness are among the possible technical and practical reasons. Among the issues that need further clarification from the IPCC is whether or not such a reconceptualization of vulnerability in the SREX/AR5 necessarily implies nullification of the TAR/AR4 vulnerability concept as far as the IPCC is concerned.

Association of Area-Level Heat and Social Vulnerability With Recurrent Hospitalizations Among Individuals With Rheumatic Conditions

OBJECTIVE

Climate and social vulnerability contribute to morbidity and health care utilization. We examined associations between the neighborhood Social Vulnerability Index (SVI) and the Heat Vulnerability Index (HVI) and recurrent hospitalizations among individuals with rheumatic conditions.

METHODS

Using a Massachusetts multihospital centralized clinical data repository, we identified individuals ≥18 years of age with a rheumatic condition who received rheumatology care within 3 years of April 2021. We defined the index date as 2 years before the last encounter and the baseline period as 1 year pre-index date. Addresses were geocoded and linked by census tract to the SVI and the HVI. We used multilevel, multinomial logistic regression to examine the odds of 1-3 and ≥4 hospitalizations (reference = 0) over 2 years post index date by vulnerability index, adjusting for age, gender, race/ethnicity, insurance, and comorbidities.

RESULTS

Among 14,401 individuals with rheumatic conditions, the mean ± age was 61.9 ± 15.7 years, 70% were female, 79% White, 7% Black, and 2% Hispanic. There were 8,251 hospitalizations; 11,649 individuals (81%) had 0 hospitalizations, 2,063 (14%) had 1-3, and 689 (5%) had ≥4. Adjusting for individual-level factors, individuals living in the highest versus lowest SVI areas had 1.84 times higher odds (95% confidence interval [95% CI] 1.43-2.36) of ≥4 hospitalizations. Individuals living in the highest versus lowest HVI areas had 1.64 times greater odds (95% CI 1.17-2.31) of ≥4 hospitalizations.

CONCLUSION

Individuals with rheumatic conditions living in areas with high versus low social and heat vulnerability had significantly greater odds of recurrent hospitalizations. Studies are needed to determine modifiable factors to mitigate risks.

Making the climate crisis personal through a focus on human health

Climate change-driven health impacts are serious, widespread, and costly. Importantly, such damages are largely absent from policy debates around the costs of delay and inaction on this crisis. While climate change is a global problem, its impacts are localized and personal, and there is growing demand for specific information on how climate change affects human health in different places. Existing research indicates that climate-fueled health problems are growing, and that investments in reducing carbon pollution and improving community resilience could help to avoid tens to hundreds of billions of dollars in climate-sensitive health impacts across the USA each year, including those stemming from extreme heat, air pollution, hurricanes, and wildfires. Science that explores the underappreciated local health impacts and health-related costs of climate change can enhance advocacy by demonstrating the need to both address the root causes of climate change and enhance climate resilience in vulnerable communities. The climate crisis has historically been predominantly conceived as a global environmental challenge; examination of climate impacts on public health enables researchers to localize this urgent problem for members of the public and policymakers. In turn, approaches to climate science that focus on health can make dangerous climate impacts and the need for cost-effective solutions more salient and tangible.