Heat and Cold Wave-Related Mortality Risk among United States Veterans with Chronic Obstructive Pulmonary Disease: A Case-Crossover Study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous pulmonary disease affecting 16 million Americans. Individuals with COPD are susceptible to environmental disturbances including heat and cold waves that can exacerbate disease symptoms. OBJECTIVE: Our objective was to estimate heat and cold wave-associated mortality risks within a population diagnosed with a chronic respiratory disease. METHODS: We collected individual level data with geocoded residential addresses from the Veterans Health Administration on 377,545 deceased patients with COPD (2016 to 2021). A time stratified case-crossover study was designed to estimate the incidence rate ratios (IRR) of heat and cold wave mortality risks using conditional logistic regression models examining lagged effects up to 7 d. Attributable risks (AR) were calculated for the lag day with the strongest association for heat and cold waves, respectively. Effect modification by age, gender, race, and ethnicity was also explored. RESULTS: Heat waves had the strongest effect on all-cause mortality at lag day 0 [IRR: 1.04; 95% confidence interval (CI): 1.02, 1.06] with attenuated effects by lag day 1. The AR at lag day 0 was 651 (95% CI: 326, 975) per 100,000 veterans. The effect of cold waves steadily increased from lag day 2 and plateaued at lag day 4 (IRR: 1.04; 95% CI: 1.02, 1.07) with declining but still elevated effects over the remaining 7-d lag period. The AR at lag day 4 was 687 (95% CI: 344, 1,200) per 100,000 veterans. Differences in risk were also detected upon stratification by gender and race. DISCUSSION: Our study demonstrated harmful associations between heat and cold waves among a high-risk population of veterans with COPD using individual level health data. Future research should emphasize using individual level data to better estimate the associations between extreme weather events and health outcomes for high-risk populations with chronic medical conditions. https://doi.org/10.1289/EHP13176.

The effect of heterogeneous severe drought on all-cause and cardiovascular mortality in the Northern Rockies and Plains of the United States.

Drought is a distinct and complicated climate hazard that regularly leads to severe economic impacts. Changes in the frequency and occurrence of drought due to anthropogenic climate change can lead to new and unanticipated outcomes. To better prepare for health outcomes, more research is needed to develop methodologies to understand potential consequences. This study suggests a new methodology for assessing the impact of monthly severe drought exposure on mortality in the Northern Rockies and Plains of the United States from 2000 to 2018. A two-stage model with the power prior approach was applied to integrate heterogeneous severe drought pattern and estimate overall risk ratios of all-cause and cardiovascular mortality related to multiple drought indices (the US Drought Monitor, 6- and 12-month Standardized Precipitation-Evapotranspiration Index, 6- and 12 month Evaporative Demand Drought Index). Under severe drought, the risk ratios of all-cause mortality are 1.050 (95 % Cr: 1.031 to 1.071, USDM), 1.041 (95 % Cr: 1.022 to 1.060, 6-SPEI), 1.009 (95 % Cr: 0.989 to 1.031, 12SPEI), 1.045 (95 % Cr: 1.022 to 1.067, 6-EDDI), and 1.035 (95 % Cr: 1.009 to 1.062, 12-EDDI); cardiovascular mortality are 1.057 (95 % Cr: 1.023 to 1.091, USDM), 1.028 (95 % Cr: 0.998 to 1.059, 6-SPEI), 1.005 (95 % Cr: 0.973 to 1.040, 12-SPEI), 1.042 (95 % Cr: 1.005 to 1.080, 6-EDDI), and 1.004 (95 % Cr: 0.959 to 1.049, 12-EDDI). Our results showed that (i) a model with properly accounted for heterogeneous exposure pattern had greater risk ratios if statistically significant; (ii) a mid-term (6-month) severe drought had higher risk ratios compared to longer-term (12-month) drought; and (iii) different severe droughts affect populations in a different way. These results expand the existing knowledge of drought relationship to increasing mortality in the United States. The findings from this study highlight the need for communities and policymakers to establish effective drought-prevention initiatives in this region.

Individual Risk Factors of PM2.5 Associated With Wintertime Mortality in Urban Patients With COPD.

BACKGROUND: Air pollution contributes to premature mortality, but potential impacts differ in populations with existing disease, particularly for individuals with multiple risk factors. Although COPD increases vulnerability to air pollution, individuals with COPD and other individual risk factors are at the intersection of multiple risks and may be especially susceptible to the effect of acute outdoor air pollution. RESEARCH QUESTION: What is the association between wintertime air pollution and mortality in patients with COPD and the modifying role of individual risk factors? STUDY DESIGN AND METHODS: This study evaluated 19,243 deceased veterans with prior COPD diagnosis who had resided in 25 US metropolitan regions (2016-2019). Electronic health records included patient demographic characteristics; smoking status; and comorbidities such as asthma, coronary artery disease (CAD), obesity, and diabetes. Using geocoded addresses, individuals were assigned wintertime fine particulate matter (particulate matter smaller than 2.5 µg in diameter [PM2.5]) and nitrogen dioxide air pollution exposures. Associations between acute air pollution and mortality were estimated by using a time-stratified case-crossover design with a conditional logistic model, and individual risk differences were assessed according to stratified analysis. RESULTS: A 1.05 (95% CI, 1.02-1.09) mortality risk was estimated for each 10 µg/m3 increase in daily wintertime all mentions of PM2.5 PM2.5). Older patients and Black individuals displayed elevated risk. Obesity was a substantial air pollution-related mortality risk factor (OR, 1.11; 95% CI, 1.01-1.23), and the estimated risk for individuals with obesity plus CAD or obesity plus diabetes was 16% higher. INTERPRETATION: Wintertime PM2.5 exposure was associated with elevated mortality risk in people with COPD, but individuals with multiple comorbidities, notably obesity, had high vulnerability. Our study suggests that obesity, CAD, and diabetes are understudied modifiers of air pollution-related risks for people with existing COPD.

Sociodemographic and Geographic Risk Factors for All-Cause Mortality in Patients with COPD.

Background: Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality worldwide. Identifying both individual and community risk factors associated with higher mortality is essential to improve outcomes. Few population-based studies of mortality in COPD include both individual characteristics and community risk factors. Objective: We used geocoded, patient-level data to describe the associations between individual demographics, neighborhood socioeconomic status, and all-cause mortality. Methods: We performed a nationally representative retrospective cohort analysis of all patients enrolled in the Veteran Health Administration with at least one ICD-9 or ICD-10 code for COPD in 2016-2019. We obtained demographic characteristics, comorbidities, and geocoded residential address. Area Deprivation Index and rurality were classified using individual geocoded residential addresses. We used logistic regression models to assess the association between these characteristics and age-adjusted all-cause mortality. Results: Of 1,106,163 COPD patients, 33.4% were deceased as of January 2021. In age-adjusted models, having more comorbidities, Black/African American race (OR 1.09 [95% CI: 1.08-1.11]), and higher neighborhood disadvantage (OR 1.30 [95% CI: 1.28-1.32]) were associated with all-cause mortality. Female sex (OR 0.67 [95% CI: 0.65-0.69]), Asian race (OR 0.64, [95% CI: 0.59-0.70]), and living in a more rural area were associated with lower odds of all-cause mortality. After adjusting for age, comorbidities, neighborhood socioeconomic status, and rurality, the association with Black/African American race reversed. Conclusion: All-cause mortality in COPD patients is disproportionately higher in patients living in poorer neighborhoods and urban areas, suggesting the impact of social determinants of health on COPD outcomes. Black race was associated with higher age-adjusted all-cause mortality, but this association was abrogated after adjusting for gender, socioeconomic status, comorbidities, and urbanicity. Future studies should focus on exploring mechanisms by which disparities arise and developing interventions to address these.

The Association between Drought Exposure and Respiratory-Related Mortality in the United States from 2000 to 2018.

Climate change has brought increasing attention to the assessment of health risks associated with climate and extreme events. Drought is a complex climate phenomenon that has been increasing in frequency and severity both locally and globally due to climate change. However, the health risks of drought are often overlooked, especially in places such as the United States, as the pathways to health impacts are complex and indirect. This study aims to conduct a comprehensive assessment of the effects of monthly drought exposure on respiratory mortality for NOAA climate regions in the United States from 2000 to 2018. A two-stage model was applied to estimate the location-specific and overall effects of respiratory risk associated with two different drought indices over two timescales (the US Drought Monitor and the 6-month and 12-month Evaporative Demand Drought Index). During moderate and severe drought exposure, respiratory mortality risk ratio in the general population increased up to 6.0% (95% Cr: 4.8 to 7.2) in the Northeast, 9.0% (95% Cr: 4.9 to 13.3) in the Northern Rockies and Plains, 5.2% (95% Cr: 3.9 to 6.5) in the Ohio Valley, 3.5% (95% Cr: 1.9 to 5.0) in the Southeast, and 15.9% (95% Cr: 10.8 to 20.4) in the Upper Midwest. Our results showed that age, ethnicity, sex (both male and female), and urbanicity (both metro and non-metro) resulted in more affected population subgroups in certain climate regions. The magnitude and direction of respiratory risk ratio differed across NOAA climate regions. These results demonstrate a need for policymakers and communities to develop more effective strategies to mitigate the effects of drought across regions.

Positive associations matter: Microbial relationships drive tick microbiome composition.

Untangling how factors such as environment, host, associations among bacterial species and dispersal predict microbial composition is a fundamental challenge. In this study, we use complementary machine-learning approaches to quantify the relative role of these factors in shaping microbiome variation of the blacklegged tick Ixodes scapularis. I. scapularis is the most important vector for Borrelia burgdorferi (the causative agent for Lyme disease) in the U.S. as well as a range of other important zoonotic pathogens. Yet the relative role of the interactions between pathogens and symbionts compared to other ecological forces is unknown. We found that positive associations between microbes where the occurrence of one microbe increases the probability of observing another, including between both pathogens and symbionts, was by far the most important factor shaping the tick microbiome. Microclimate and host factors played an important role for a subset of the tick microbiome including Borrelia (Borreliella) and Ralstonia, but for the majority of microbes, environmental and host variables were poor predictors at a regional scale. This study provides new hypotheses on how pathogens and symbionts might interact within tick species, as well as valuable predictions for how some taxa may respond to changing climate.

Cognitive factors influenced physical distancing adherence during the COVID-19 pandemic in a population-specific way.

Even early in the COVID-19 pandemic, adherence to physical distancing measures was variable, exposing some communities to elevated risk. While cognitive factors from the Health Belief Model (HBM) and resilience correlate with compliance with physical distancing, external conditions may preclude full compliance with physical distancing guidelines. Our objective was to identify HBM and resilience constructs that could be used to improve adherence to physical distancing even when full compliance is not possible. We examined adherence as expressed through 7-day non-work, non-household contact rates in two cohorts: 1) adults in households with children from Minnesota and Iowa; and 2) adults ≥50 years-old from Minnesota, one-third of whom had Parkinson's disease. We identified multiple cognitive factors associated with physical distancing adherence, specifically perceived severity, benefits, self-efficacy, and barriers. However, the magnitude, and occasionally the direction, of these associations was population-dependent. In Cohort 1, perceived self-efficacy for remaining 6-feet from others was associated with a 29% lower contact rate (RR 0.71; 95% CI 0.65, 0.77). This finding was consistent across all race/ethnicity and income groups we examined. The barriers to adherence of having a child in childcare and having financial concerns had the largest effects among individuals from marginalized racial and ethnic groups and high-income households. In Cohort 2, self-efficacy to quarantine/isolate was associated with a 23% decrease in contacts (RR 0.77; 95% CI 0.66, 0.89), but upon stratification by education level, the association was only present for those with at least a Bachelor's degree. Education also modified the effect of the barrier to adherence leaving home for work, increasing contacts among those with a Bachelor's degree and reducing contacts among those without. Our findings suggest that public health messaging tailored to the identified cognitive factors has the potential to improve physical distancing adherence, but population-specific needs must be considered to maximize effectiveness.

The effect of pre-aggregation scale on spatially adaptive filters.

Choropleth mapping continues to be a dominant mapping technique despite suffering from the Modifiable Areal Unit Problem (MAUP), which may distort disease risk patterns when different administrative units are used. Spatially adaptive filters (SAF) are one mapping technique that can address the MAUP, but the limitations and accuracy of spatially adaptive filters are not well tested. Our work examines these limitations by using varying levels of data aggregation using a case study of geocoded breast cancer screening data and a synthetic georeferenced population dataset that allows us to calculate SAFs at the individual-level. Data were grouped into four administrative boundaries (i.e., county, Zip Code Tabulated Areas, census tracts, and census blocks) and compared to individual-level data (control). Correlation assessed the similarity of SAFs, and map algebra calculated error maps compared to control. This work describes how pre-aggregation affects the level of spatial detail, map patterns, and over and under-prediction.

Spatio-Temporal Dynamics of Tick-Borne Diseases in North-Central Wisconsin from 2000-2016.

Lyme disease is a well-recognized public health problem in the USA, however, other tick-borne diseases also have major public health impacts. Yet, limited research has evaluated changes in the spatial and temporal patterns of non-Lyme tick-borne diseases within endemic regions. Using laboratory data from a large healthcare system in north-central Wisconsin from 2000-2016, we applied a Kulldorf's scan statistic to analyze spatial, temporal and seasonal clusters of laboratory-positive cases of human granulocytic anaplasmosis (HGA), babesiosis, and ehrlichiosis at the county level. Older males were identified as the subpopulation at greatest risk for non-Lyme tick-borne diseases and we observed a statistically significant spatial and temporal clustering of cases (p < 0.05). HGA risk shifted from west to east over time (2000-2016) with a relative risk (RR) ranging from 3.30 to 11.85, whereas babesiosis risk shifted from south to north and west over time (2004-2016) with an RR ranging from 4.33 to 4.81. Our study highlights the occurrence of non-Lyme tick-borne diseases, and identifies at-risk subpopulations and shifting spatial and temporal heterogeneities in disease risk. Our findings can be used by healthcare providers and public health practitioners to increase public awareness and improve case detection.