The reliability of an environmental epidemiology meta-analysis, a case study

Human exposure risk assessment for infectious diseases due to temperature and air pollution: an overview of reviews

Air pollution and global temperature change are expected to affect infectious diseases. Air pollution usually causes inflammatory response and disrupts immune defense system, while temperature mainly exacerbates the effect of vectors on humans. Yet to date overview of systematic reviews assessing the exposure risk of air pollutants and temperature on infectious diseases is unavailable. This article aims to fill this research gap. PubMed, Embase, the Cochrane Library, Web of Science, and the Cumulative Index to Nursing and Allied Health Literature were searched. Systematic reviews and meta-analyses investigated the exposure risk of pollutants or temperature on infectious diseases were included. Two investigators screened literature, extracted data and performed the risk of bias assessments independently. A total of 23 articles met the inclusion criteria, which 3 (13%) were "low" quality and 20 (87%) were "critically low" quality. COVID-19 morbidity was associated with long-term exposure PM2.5 (RR = 1.056 per 1 [Formula: see text], 95% CI: 1.039-1.072) and NO2 (RR = 1.042 per 1 [Formula: see text], 95% CI: 1.017-1.068). In addition, for each 1 °C increase in temperature, the morbidity risk of dengue increased 13% (RR = 1.130 per 1 °C, 95% CI: 1.120-1.150), infectious diarrhea increased 8% (RR = 1.080 per 1 °C, 95% CI: 1.050-1.200), and hand, foot and mouth disease (HFMD) increased 5% (RR = 1.050 per 1 °C, 95% CI: 1.020-1.080). In conclusion, PM2.5 and NO2 increased the risk of COVID-19 and temperatures were associated with dengue, infectious diarrhoea and HFMD morbidity. Moreover, the exposure risk of temperature on COVID-19 was recommended to be further explored.

The P value plot does not provide evidence against air pollution hazards

A number of papers by Young and collaborators have criticized epidemiological studies and meta-analyses of air pollution hazards using a graphical method that the authors call a P value plot, claiming to find zero effects, heterogeneity, and P hacking. However, the P value plot method has not been validated in a peer-reviewed publication. The aim of this study was to investigate the statistical and evidentiary properties of this method.

Evaluation of a Meta-Analysis of Ambient Air Quality as a Risk Factor for Asthma Exacerbation

Background: An irreproducibility crisis currently afflicts a wide range of scientific disciplines, including public health and biomedical science. A study was undertaken to assess the reliability of a meta-analysis examining whether air quality components (carbon monoxide, particulate matter 10 µm and 2.5 µm (PM10 and PM2.5), sulfur dioxide, nitrogen dioxide and ozone) are risk factors for asthma exacerbation. Methods: The number of statistical tests and models were counted in 17 randomly selected base papers from 87 used in the meta-analysis. Confidence intervals from all 87 base papers were converted to p-values. p-value plots for each air component were constructed to evaluate the effect heterogeneity of the p-values. Results: The number of statistical tests possible in the 17 selected base papers was large, median = 15,360 (interquartile range = 1536–40,960), in comparison to results presented. Each p-value plot showed a two-component mixture with small p-values < 0.001 while other p-values appeared random (p-values > 0.05). Given potentially large numbers of statistical tests conducted in the 17 selected base papers, p-hacking cannot be ruled out as explanations for small p-values. Conclusions: Our interpretation of the meta-analysis is that random p-values indicating null associations are more plausible and the meta-analysis is unlikely to replicate in the absence of bias.

Public health impact of daily life triggers of sudden cardiac death: A systematic review and comparative risk assessment

BACKGROUND

Sudden cardiac death (SCD) may be triggered by daily circumstances and activities such as stressful psycho-emotional events, physical exertion or substance misuse. We calculated population attributable fractions (PAFs) to estimate the public health relevance of daily life triggers of SCD and to compare their population impacts.

METHODS

We searched PubMed, Scopus and the Web of Science citation databases to retrieve studies of triggers of SCD and cardiac arrest that would enable a computation of PAFs. When more studies investigated the same trigger, a meta-analytical pooled risk random-effect estimate was used.

RESULTS

Of the retrieved studies, eight provided data enabling computation of PAFs. The prevalence of exposure within population for SCD triggers in the control periods ranged from 1.06% for influenza infection to 8.73% for recent use of cannabis. Triggers ordered from the highest to the lowest risk increase were: physical exertion, recent cocaine use, episodic alcohol consumption, recent amphetamine use, episodic coffee consumption, psycho-emotional stress within the previous month, influenza infection, and recent cannabis use. The relative risk increase ranged from 1.10 to 4.98. By accounting for both the magnitude of the risk increase and the prevalence in the population, the present estimates of PAF assign 14.5% (95% confidence interval [CI] 4.9-28.5) of all SCDs to episodic alcohol consumption, 9.4% (95% CI 1.2-29.3) to physical exertion, 6.9% (95% CI 0.3-25.0) to cocaine, 6% (95% CI 1.2-14.6) to episodic coffee consumption, 3% (95% CI 0.4-6.8) to psycho-emotional stress in the previous month, 1.7% (95% CI -0.9 to 12.9) to amphetamines, 0.9% (95% CI -4.9 to 12.5) to cannabis, and 0.3% (95% CI 0.2-0.4) to influenza infections.

CONCLUSIONS

In addition to episodic alcohol consumption, a trigger with the greatest public health importance for SCD, episodic physical exertion, cocaine use and coffee consumption also show a considerable population impact.

Evaluation of a meta-analysis of air quality and heart attacks, a case study

It is generally acknowledged that claims from observational studies often fail to replicate. An exploratory study was undertaken to assess the reliability of base studies used in meta-analysis of short-term air quality-myocardial infarction risk and to judge the reliability of statistical evidence from meta-analysis that uses data from observational studies. A highly cited meta-analysis paper examining whether short-term air quality exposure triggers myocardial infarction was evaluated as a case study. The paper considered six air quality components - carbon monoxide, nitrogen dioxide, sulphur dioxide, particulate matter 10 μm and 2.5 μm in diameter (PM10 and PM2.5), and ozone. The number of possible questions and statistical models at issue in each of 34 base papers used were estimated and p-value plots for each of the air components were constructed to evaluate the effect heterogeneity of p-values used from the base papers. Analysis search spaces (number of statistical tests possible) in the base papers were large, median = 12,288 (interquartile range = 2496 - 58,368), in comparison to actual statistical test results presented. Statistical test results taken from the base papers may not provide unbiased measures of effect for meta-analysis. Shapes of p-value plots for the six air components were consistent with the possibility of analysis manipulation to obtain small p-values in several base papers. Results suggest the appearance of heterogeneous, researcher-generated p-values used in the meta-analysis rather than unbiased evidence of real effects for air quality. We conclude that this meta-analysis does not provide reliable evidence for an association of air quality components with myocardial risk.