Quantifying the short-term effects of air pollution on health in the presence of exposure measurement error: a simulation study of multi-pollutant model results

Comparison of personal exposure to black carbon levels with fixed-site monitoring data and with dispersion modelling and the influence of activity patterns and environment

BACKGROUND

Short-term studies of health effects from ambient air pollution usually rely on fixed site monitoring data or spatio-temporal models for exposure characterization, but the relation to personal exposure is often not known.

OBJECTIVE

We aimed to explore this relation for black carbon (BC) in central Stockholm.

METHODS

Families (n = 46) with an infant, one parent working and one parent on parental leave, carried battery-operated BC instruments for 7 days. Routine BC monitoring data were obtained from rural background (RB) and urban background (UB) sites. Outdoor levels of BC at home and work were estimated in 24 h periods by dispersion modelling based on hourly real-time meteorological data, and statistical meteorological data representing annual mean conditions. Global radiation, air pressure, precipitation, temperature, and wind speed data were obtained from the UB station. All families lived in the city centre, within 4 km of the UB station.

RESULTS

The average level of 24 h personal BC was 425 (s.d. 181) ng/m3 for parents on leave, and 394 (s.d. 143) ng/m3 for working parents. The corresponding fixed-site monitoring observations were 148 (s.d. 139) at RB and 317 (s.d. 149) ng/m3 at UB. Modelled BC levels at home and at work were 493 (s.d. 228) and 331 (s.d. 173) ng/m3, respectively. UB, RB and air pressure explained only 21% of personal 24 h BC variability for parents on leave and 25% for working parents. Modelled home BC and observed air pressure explained 23% of personal BC, and adding modelled BC at work increased the explanation to 34% for the working parents.

IMPACT

Short-term studies of health effects from ambient air pollution usually rely on fixed site monitoring data or spatio-temporal models for exposure characterization, but the relation to actual personal exposure is often not known. In this study we showed that both routine monitoring and modelled data explained less than 35% of variability in personal black carbon exposure. Hence, short-term health effects studies based on fixed site monitoring or spatio-temporal modelling are likely to be underpowered and subject to bias.

Increase in sick leave episodes from short-term fine particulate matter exposure: A case-crossover study in Stockholm, Sweden

Air pollution's short-term effects on a wide range of health outcomes have been studied extensively, primarily focused on vulnerable groups (e.g., children and the elderly). However, the air pollution effects on the adult working population through sick leave have received little attention. This study aims to 1) estimate the associations between particulate matter ≤2.5 μm3 (PM2.5) and sick leave episodes and 2) calculate the attributable number of sick leave days and the consequential productivity loss in the City of Stockholm, Sweden. Individual level daily sick leave data was obtained from Statistics Sweden for the years 2011-2019. Daily average concentrations of PM2.5 were obtained from the main urban background monitoring station in Stockholm. A case-crossover study design was applied to estimate the association between short-term PM2.5 and onset of sick leave episodes. Conditional logistic regression was used to estimate the relative increase in odds of onset per 10 μg/m3 of PM2.5, adjusting for temperature, season, and pollen. A human capital method was applied to estimate the PM2.5 attributable productivity loss. In total, 1.5 million (M) individual sick leave occurrences were studied. The measured daily mean PM2.5 concentration was 4.2 μg/m3 (IQR 3.7 μg/m3). The odds of a sick leave episode was estimated to increase by 8.5% (95% CI: 7.8-9.3) per 10 μg/m3 average exposure 2-4 days before. Sub-group analysis showed that private sector and individuals 15-24 years old had a lower increase in odds of sick leave episodes in relation to PM2.5 exposure. In Stockholm, 4% of the sick leave episodes were attributable to PM2.5 exposure, corresponding to €17 M per year in productivity loss. Our study suggests a positive association between PM2.5 and sick leave episodes in a low exposure area.

Impact of ultrafine particles and total particle number concentration on five cause-specific hospital admission endpoints in three German cities

INTRODUCTION

Numerous studies have shown associations between daily concentrations of fine particles (e.g., particulate matter with an aerodynamic diameter ≤2.5 µm; PM2.5) and morbidity. However, evidence for ultrafine particles (UFP; particles with an aerodynamic diameter of 10-100 nm) remains conflicting. Therefore, we aimed to examine the short-term associations of UFP with five cause-specific hospital admission endpoints for Leipzig, Dresden, and Augsburg, Germany.

MATERIAL AND METHODS

We obtained daily counts of (cause-specific) cardiorespiratory hospital admissions between 2010 and 2017. Daily average concentrations of UFP, total particle number (PNC; 10-800 nm), and black carbon (BC) were measured at six sites; PM2.5 and nitrogen dioxide (NO2) were obtained from monitoring networks. We assessed immediate (lag 0-1), delayed (lag 2-4, lag 5-7), and cumulative (lag 0-7) effects by applying station-specific confounder-adjusted Poisson regression models. We then used a novel multi-level meta-analytical method to obtain pooled risk estimates. Finally, we performed two-pollutant models to investigate interdependencies between pollutants and examined possible effect modification by age, sex, and season.

RESULTS

UFP showed a delayed (lag 2-4) increase in respiratory hospital admissions of 0.69% [95% confidence interval (CI): -0.28%; 1.67%]. For other hospital admission endpoints, we found only suggestive results. Larger particle size fractions, such as accumulation mode particles (particles with an aerodynamic diameter of 100-800 nm), generally showed stronger effects (respiratory hospital admissions & lag 2-4: 1.55% [95% CI: 0.86%; 2.25%]). PM2.5 showed the most consistent associations for (cardio-)respiratory hospital admissions, whereas NO2 did not show any associations. Two-pollutant models showed independent effects of PM2.5 and BC. Moreover, higher risks have been observed for children.

CONCLUSIONS

We observed clear associations with PM2.5 but UFP or PNC did not show a clear association across different exposure windows and cause-specific hospital admissions. Further multi-center studies are needed using harmonized UFP measurements to draw definite conclusions on the health effects of UFP.

The effect of ambient ozone exposure on three types of diabetes: a meta-analysis

BACKGROUND

Ozone as an air pollutant is gradually becoming a threat to people's health. However, the effect of ozone exposure on risk of developing diabetes, a fast-growing global metabolic disease, remains controversial.

OBJECTIVE

To evaluate the impact of ambient ozone exposure on the incidence rate of type 1, type 2 and gestational diabetes mellitus.

METHOD

We systematically searched PubMed, Web of Science, and Cochrane Library databases before July 9, 2022, to determine relevant literature. Data were extracted after quality evaluation according to the Newcastle Ottawa Scale (NOS) and the agency for healthcare research and quality (AHRQ) standards, and a meta-analysis was used to evaluate the correlation between ozone exposure and type 1 diabetes mellitus (T1D), type 2 diabetes mellitus (T2D), and gestational diabetes mellitus (GDM). The heterogeneity test, sensitivity analysis, and publication bias were performed using Stata 16.0.

RESULTS

Our search identified 667 studies from three databases, 19 of which were included in our analysis after removing duplicate and ineligible studies. Among the remaining studies, three were on T1D, five were on T2D, and eleven were on GDM. The result showed that ozone exposure was positively correlated with T2D [effect size (ES) = 1.06, 95% CI: 1.02, 1.11] and GDM [pooled odds ratio (OR) = 1.01, 95% CI: 1.00, 1.03]. Subgroup analysis demonstrated that ozone exposure in the first trimester of pregnancy might raise the risk of GDM. However, no significant association was observed between ozone exposure and T1D.

CONCLUSION

Long-term exposure to ozone may increase the risk of T2D, and daily ozone exposure during pregnancy was a hazard factor for developing GDM. Decreasing ambient ozone pollution may reduce the burden of both diseases.

Methylated polycyclic aromatic hydrocarbons from household coal use across the life course and risk of lung cancer in a large cohort of 42,420 subjects in Xuanwei, China

BACKGROUND

We previously showed that exposure to 5-methylchrysene (5MC) and other methylated polycyclic aromatic hydrocarbons (PAHs) best explains lung cancer risks in a case-control study among non-smoking women using smoky coal in China. Time-related factors (e.g., age at exposure) and non-linear relations were not explored.

OBJECTIVE

We investigated the relation between coal-derived air pollutants and lung cancer mortality using data from a large retrospective cohort.

METHODS

Participants were smoky (bituminous) or smokeless (anthracite) coal users from a cohort of 42,420 subjects from four communes in XuanWei. Follow-up was from 1976 to 2011, during which 4,827 deaths from lung-cancer occurred. Exposures were predicted for 43 different pollutants. Exposure clusters were identified using hierarchical clustering. Cox regression was used to estimate exposure-response relations for 5MC, while effect modification by age at exposure was investigated for cluster prototypes. A Bayesian penalized multi-pollutant model was fitted on a nested case-control sample, with more restricted models fitted to investigate non-linear exposure-response relations.

RESULTS

We confirmed the strong exposure-response relation for 5MC (Hazard Ratio [95% Confidence Interval] = 2.5 [2.4, 2.6] per standard-deviation (SD)). We identified four pollutant clusters, with all but two PAHs in a single cluster. Exposure to PAHs in the large cluster was associated with a higher lung cancer mortality rate (HR [95%CI] = 2.4 [2.2, 2.6] per SD), while exposure accrued before 18 years of age appeared more important than adulthood exposures. Results from the multi-pollutant model identified anthanthrene (ANT) and benzo(a)chrysene (BaC) as risk factors. 5MC remained strongly associated with lung cancer in models that included ANT and BaC and also benzo(a)pyrene (BaP).

CONCLUSION

We confirmed the link between PAH exposures and lung cancer in smoky coal users and found exposures before age 18 to be especially important. We found some evidence for the carcinogen 5MC and non-carcinogens ANT and BaC.

Measurement error correction for ambient PM2.5 exposure using stratified regression calibration: Effects on all-cause mortality

BACKGROUND

Previous studies on the impact of measurement error for PM2.5 were mostly simulation studies, did not control for other pollutants, or used a single regression calibration model to correct for measurement error. However, the relationship between actual and error-prone PM2.5 concentration may vary by time and region. We aim to correct the measurement error of PM2.5 predictions using stratified regression calibration and investigate how the measurement error biases the association between PM2.5 and mortality in the Medicare Cohort.

METHODS

The "gold-standard" measurements of PM2.5 were defined as daily monitoring data. We regressed daily monitoring PM2.5 on modeled PM2.5 using the simple linear regression by strata of season, elevation, census division and time period. Calibrated PM2.5 was calculated with stratum-specific calibration parameters β0 (intercept) and β1 (slope) for each strata and aggregated to annual level. Associations between calibrated and error-prone annual PM2.5 and all-cause mortality among Medicare beneficiaries were estimated with Quasi-Poisson regression models.

RESULTS

Across 208 strata, the median of β0 and β1 were 0.62 (25% 0.0.20, 75% 1.06) and 0.93 (25% 0.87, 75% 0.99). From calibrated and error-prone PM2.5 data, we estimated that each 10 μg/m3 increase in PM2.5 was respectively associated with 4.9% (95%CI 4.6-5.2) and 4.6% (95%CI 4.4-4.9) increases in the mortality rate among Medicare beneficiaries, conditional on confounders.

CONCLUSIONS

Regression calibration parameters of PM2.5 varied by time and region. Using error-prone measures of PM2.5 underestimated the association between PM2.5 and all-cause mortality. Modern exposure models produce relatively small bias.

Short-term effects of ultrafine particles on heart rate variability: A systematic review and meta-analysis

An increasing number of epidemiological studies have examined the association between ultrafine particles (UFP) and imbalanced autonomic control of the heart, a potential mechanism linking particulate matter air pollution to cardiovascular disease. This study systematically reviews and meta-analyzes studies on short-term effects of UFP on autonomic function, as assessed by heart rate variability (HRV). We searched PubMed and Web of Science for articles published until June 30, 2022. We extracted quantitative measures of UFP effects on HRV with a maximum lag of 15 days from single-pollutant models. We assessed the risk of bias in the included studies regarding confounding, selection bias, exposure assessment, outcome measurement, missing data, and selective reporting. Random-effects models were applied to synthesize effect estimates on HRV of various time courses. Twelve studies with altogether 1,337 subjects were included in the meta-analysis. For an increase of 10,000 particles/cm³ in UFP assessed by central outdoor measurements, our meta-analysis showed immediate decreases in the standard deviation of the normal-to-normal intervals (SDNN) by 4.0% [95% confidence interval (CI): 7.1%, -0.9%] and root mean square of successive R-R interval differences (RMSSD) by 4.7% (95% CI: 9.1%, 0.0%) within 6 h after exposure. The immediate decreases in SDNN and RMSSD associated with UFP assessed by personal measurements were smaller and borderline significant. Elevated UFP were also associated with decreases in SDNN, low-frequency power, and the ratio of low-frequency to high-frequency power when pooling estimates of lags across hours to days. We did not find associations between HRV and concurrent-day UFP exposure (daily average of at least 18 h) or exposure at lags ≥ one day. Our study indicates that short-term exposure to ambient UFP is associated with decreased HRV, predominantly as an immediate response within hours. This finding highlights that UFP may contribute to the onset of cardiovascular events through autonomic dysregulation.

Short term exposure to air pollution and mortality in the US: a double negative control analysis

RATIONALE

Studies examining the association of short-term air pollution exposure and daily deaths have typically been limited to cities and used citywide average exposures, with few using causal models.

OBJECTIVES

To estimate the associations between short-term exposures to fine particulate matter (PM_2.5), ozone (O₃), and nitrogen dioxide (NO₂) and all-cause and cause-specific mortality in multiple US states using census tract or address exposure and including rural areas, using a double negative control analysis.

METHODS

We conducted a time-stratified case-crossover study examining the entire population of seven US states from 2000-2015, with over 3 million non-accidental deaths. Daily predictions of PM_2.5, O₃, and NO₂ at 1x1 km grid cells were linked to mortality based on census track or residential address. For each pollutant, we used conditional logistic regression to quantify the association between exposure and the relative risk of mortality conditioning on meteorological variables, other pollutants, and using double negative controls.

RESULTS

A 10 μg/m³ increase in PM_2.5 exposure at the moving average of lag 0-2 day was significantly associated with a 0.67% (95%CI: 0.34-1.01%) increase in all-cause mortality. 10 ppb increases in NO₂ or O₃ exposure at lag 0-2 day were marginally associated with and 0.19% (95%CI: -0.01-0.38%) and 0.20 (95% CI-0.01, 0.40), respectively. The adverse effects of PM_2.5 persisted when pollution levels were restricted to below the current global air pollution standards. Negative control models indicated little likelihood of omitted confounders for PM_2.5, and mixed results for the gases. PM_2.5 was also significantly associated with respiratory mortality and cardiovascular mortality.

CONCLUSIONS

Short-term exposure to PM_2.5 and possibly O₃ and NO₂ are associated with increased risks for all-cause mortality. Our findings delivered evidence that risks of death persisted at levels below currently permissible.

The Impact of Exposure Measurement Error on the Estimated Concentration-Response Relationship between Long-Term Exposure to PM2.5 and Mortality

BACKGROUND

Exposure measurement error is a central concern in air pollution epidemiology. Given that studies have been using ambient air pollution predictions as proxy exposure measures, the potential impact of exposure error on health effect estimates needs to be comprehensively assessed.

OBJECTIVES

We aimed to generate wide-ranging scenarios to assess direction and magnitude of bias caused by exposure errors under plausible concentration-response relationships between annual exposure to fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] and all-cause mortality.

METHODS

In this simulation study, we use daily PM2.5 predictions at 1-km2 spatial resolution to estimate annual PM2.5 exposures and their uncertainties for ZIP Codes of residence across the contiguous United States between 2000 and 2016. We consider scenarios in which we vary the error type (classical or Berkson) and the true concentration-response relationship between PM2.5 exposure and mortality (linear, quadratic, or soft-threshold-i.e., a smooth approximation to the hard-threshold model). In each scenario, we generate numbers of deaths using error-free exposures and confounders of concurrent air pollutants and neighborhood-level covariates and perform epidemiological analyses using error-prone exposures under correct specification or misspecification of the concentration-response relationship between PM2.5 exposure and mortality, adjusting for the confounders.

RESULTS

We simulate 1,000 replicates of each of 162 scenarios investigated. In general, both classical and Berkson errors can bias the concentration-response curve toward the null. The biases remain small even when using three times the predicted uncertainty to generate errors and are relatively larger at higher exposure levels.

DISCUSSION

Our findings suggest that the causal determination for long-term PM2.5 exposure and mortality is unlikely to be undermined when using high-resolution ambient predictions given that the estimated effect is generally smaller than the truth. The small magnitude of bias suggests that epidemiological findings are relatively robust against the exposure error. In practice, the use of ambient predictions with a finer spatial resolution will result in smaller bias. https://doi.org/10.1289/EHP10389.