Spatial variation in the joint effect of extreme heat events and ozone on respiratory hospitalizations in California

Abstract

Extreme heat and ozone are co-occurring exposures that independently and synergistically increase the risk of respiratory disease. To our knowledge, no joint warning systems consider both risks; understanding their interactive effect can warrant use of comprehensive warning systems to reduce their burden. We examined heterogeneity in joint effects (on the additive scale) between heat and ozone at small geographical scales. A within-community matched design with a Bayesian hierarchical model was applied to study this association at the zip code level. Spatially varying relative risks due to interaction (RERI) were quantified to consider joint effects. Determinants of the spatial variability of effects were assessed using a random effects metaregression to consider the role of demographic/neighborhood characteristics that are known effect modifiers. A total of 817,354 unscheduled respiratory hospitalizations occurred in California from 2004 to 2013 in the May to September period. RERIs revealed no additive interaction when considering overall joint effects. However, when considering the zip code level, certain areas observed strong joint effects. A lower median income, higher percentage of unemployed residents, and exposure to other air pollutants within a zip code drove stronger joint effects; a higher percentage of commuters who walk/bicycle, a marker for neighborhood wealth, showed decreased effects. Results indicate the importance of going beyond average measures to consider spatial variation in the health burden of these exposures and predictors of joint effects. This information can be used to inform early warning systems that consider both heat and ozone to protect populations from these deleterious effects in identified areas.

Increased tropospheric ozone levels as a public health issue during COVID-19 lockdown and estimation the related pulmonary diseases

“…The number of hospital admissions for cardiovascular diseases (6.6, 10.6, and 8.3 per 10 5 cases), premature deaths for respiratory diseases (4.7, 5.4, and 4.9 per 10 5 cases), and long-term premature deaths for respiratory diseases (1.7, 2.6, and 2.2 per 10 5 cases) due to O 3 exposure above 5 ppb were obtained in 2019, 2020, and 2021, respectively ( Table S1 ). HA-CVD, MRD, and LT-MRD increased by 60.6%, 14.8%, and 52.9% per 10 5 cases in 2020, due to higher O 3 exposure ( Schwarz et al, 2021 ) and then decreased by 21.0%, 8.9%, and 15.4% per 10 5 cases in 2021 when annual mean of O 3 levels was reduced. In China, the authors observed a positive relationship between increase in O 3 levels and the risk of premature mortality, especial between exposure to O 3 and the risk of respiratory diseases and mortality ( Liu et al, 2017 ).…”

Exploring Health Effects under Specific Causes of Mortality Based on 90 Definitions of PM2.5 and Cold Spell Combined Exposure in Shanghai, China

“…26,27 The definition of air pollution usually considers its pollution threshold and duration for the determination of the pollution level. 17,28 In most combined exposure research, a single definition of exposure is used, and few studies have provided clear criteria for determining combined exposure events. As there are great variations in meteorological and pollution characteristics among different regions, the threshold of combined events should be defined with local geographic and meteorological characteristics.…”

“…One considers the effects of temperature variability (a daily mean/minimum temperature drop of 8/12 °C within 24/48 h), , which were applied to construct the Chinese cold spell warning system by the China Meteorological Administration (CMA). The other considers the effects of objective low temperatures (daily mean/minimum temperatures below the 2.5/5/10th percentile for at least 2 consecutive days). , The definition of air pollution usually considers its pollution threshold and duration for the determination of the pollution level. , In most combined exposure research, a single definition of exposure is used, and few studies have provided clear criteria for determining combined exposure events. As there are great variations in meteorological and pollution characteristics among different regions, the threshold of combined events should be defined with local geographic and meteorological characteristics.…”

“…Research on air pollution or extreme temperatures and their health risks has long been conducted independently. , Indeed, the environment we are exposed is mixed with various environmental factors acting synergistically or antagonistically with one another. , Recently, researchers and policymakers have recommended focusing on the combined effects on the health of populations to better protect the public. − Because of the physical and kinetic properties of the air pollutants themselves, the formation, persistence, and dissipation of pollution are significantly influenced by meteorological conditions . Influenced by the East Asian monsoon, PM 2.5 pollution has obvious seasonal characteristics and is more frequent in winter. , When a cold spell occurs, the low temperature or sudden temperature drop creates a certain obstacle to particulate matter dispersion, resulting in PM 2.5 pollution and cold spell combined exposure events. ,, Although some studies have included temperature as a confounding factor in air pollutant analysis models, the combined effects have been little studied and have mostly focused on the combined effects of heat waves and air pollution. ,, The health effects of PM 2.5 and cold spell combined exposure need to be further explored.…”

Population co-exposure to extreme heat and wildfire smoke pollution in California during 2020

“…These two studies, however, used concentration measurements that include all sources of PM 2.5 (versus smoke-specific PM 2.5 ) and relied on either unevenly distributed air quality stations or a coarse 10 km resolution, respectively. These spatially coarse measures of compound exposure constrain policymaking because they preclude investigation into the sociodemographic correlates of exposure (Schwarz et al 2021).…”