Global Epidemiology最新文献

Toews et al. [1] and the World Health Organization (WHO) [2] reviewed observational epidemiology studies of non-sugar sweeteners (NSSs) and various health effects. The former used the Risk of Bias in Non-randomised Studies – of Interventions (ROBINS-I) tool and the latter used both the ROBINS-I tool and the Newcastle-Ottawa Scale to evaluate study quality. Both reviews concluded that there were no associations between NSS or aspartame consumption and cancer (except possibly between saccharin and bladder cancer) but indicated that the certainty of the evidence for all cancer types was “very low.” While we agree with this conclusion, the support for the confidence in the evidence generally was not transparently documented, as the results of the study quality assessment were only provided in scores or ratings. An examination of illustrative case studies shows that some important aspects of study quality domains specific for NSSs generally or aspartame specifically (i.e., issues with the exposure and outcome assessments, the consideration of confounding/covariates, and selection bias) may have been overlooked or not given appropriate consideration, while other aspects that were less likely to have a large impact on overall study quality dominated the results in the two assessments. Our review of other studies published after the WHO [2] review further demonstrates this point. While this may not seem important given the overall lack of associations, it impacts the degree to which evidence supports a lack of effects as opposed to not being adequate to evaluate associations. In the future, aspartame and cancer outcome reviews should focus on those study quality domains that are most likely to impact the interpretation of results and discuss them in a transparent, systematic manner. If there is very low certainty in the evidence as a result of low study quality, reviewers should conclude the evidence is inadequate for making a causal determination.

Mathematical modelling plays a key role in understanding and predicting the epidemiological dynamics of infectious diseases. We construct a flexible discrete-time model that incorporates multiple viral strains with different transmissibilities to estimate the changing patterns of human contact that generates new infections. Using a Bayesian approach, we fit the model to longitudinal data on hospitalisation with COVID-19 from the Republic of Ireland and Northern Ireland during the first year of the pandemic. We describe the estimated change in human contact in the context of government-mandated non-pharmaceutical interventions in the two jurisdictions on the island of Ireland. We take advantage of the fitted model to conduct counterfactual analyses exploring the impact of lockdown timing and introducing a novel, more transmissible variant. We found substantial differences in human contact between the two jurisdictions during periods of varied restriction easing and December holidays. Our counterfactual analyses reveal that implementing lockdowns earlier would have decreased subsequent hospitalisation substantially in most, but not all cases, and that an introduction of a more transmissible variant - without necessarily being more severe - can cause a large impact on the health care burden.

Background

Understanding discrepancies in mental health and substance use treatment utilization can help identify inequities in access to health services. We investigate mental health and substance use treatment utilization as function of demographic and social determinants, as well as pre-existing mental health and substance use disorders.

Methods

In this repeated cross-sectional study, we used the 2017–2019 National Survey on Drug Use and Health data on US adults above age 18. Two logistic regression models were conducted, using predictors of age, gender, race/Hispanicity, sexual identity, education, insurance, family income, and past year mental health and substance use disorders, with outcomes of mental health or substance use treatment utilization. Weighted estimates of substance use disorders and insurance types and Pearson's correlation tests of vulnerability among age, gender, and treatment type were reported.

Findings

Racial minorities, uninsured populations, sexual minorities, and females had lower odds of receiving mental health treatment, while older populations, lower income groups, and dual eligible enrollees had higher odds. Individuals with substance use disorders but no mental illness had higher odds of receiving mental health treatment. Those utilizing mental health treatment were mostly of high income, privately insured, and using cannabis, cocaine, and opioids. Older populations, men, and Medicaid only enrollees had higher odds of receiving substance use disorder treatment, whereas racial minorities had lower odds. Distribution of income, insurance type, and substance use were more widespread than mental health treatment.

Interpretation

Mental health treatment can be used as an avenue for substance use treatment, particularly opioid use disorders. It is important to target vulnerable populations, like racial minorities and uninsured populations to improve access to mental health and substance use treatment.

Background

The present study aimed to investigate the progression of the SARS-CoV-2 pandemic in Ireland over the first three waves of infection.

Method

A selection of blood donor serum samples collected between February 2020 and December 2021 were analysed by various commercially available serological assays for antibodies to SARS-CoV-2 (n = 15,066).

Results

An increase in seropositivity was observed between wave 1 (February to September 2020) and wave 2 (November and December 2020) of 2.20% to 3.55%. A large increase in estimated seroprevalence to 11.89% was observed in samples collected in February and March 2021 (wave 3 of infection).The rate of seropositivity varied by age group, with the highest rate observed in the youngest donors (18–29 years) peaking at 18.79% in wave 3. The results of spike antibody (anti-S) testing indicated that 44/1009 (4.36%) of seroreactive donors in wave 3 had a serological profile consistent with vaccination. By November 2021, we detected an overall seropositivity of 97.04%.

Conclusions

The present study provides a comprehensive estimation of the level of circulating SARS-CoV-2 antibodies in Irish blood donors, enabling differentiation between vaccination and natural infection, as well as real-time monitoring of the progression of the COVID-19 pandemic in Ireland. Seroepidemiology has a role in determining reliable estimates of transmission, infection fatality rates and vaccine uptake. The continued screening of blood donors for this purpose has the potential to generate important data to assist with the management of future waves of SARS-CoV-2.

The most recent meta-analysis of gas cooking and respiratory outcomes in children was conducted by Lin et al. [93] in 2013. Since then, a number of epidemiology studies have been published on this topic. We conducted the first systematic review of this epidemiology literature that includes an in-depth evaluation of study heterogeneity and study quality, neither of which was systematically evaluated in earlier reviews. We reviewed a total of 66 relevant studies, including those in the Lin et al. [93] meta-analysis. Most of the studies are cross-sectional by design, precluding causal inference. Only a few are cohort studies that could establish temporality and they have largely reported null results. There is large variability across studies in terms of study region, age of children, gas cooking exposure definition, and asthma or wheeze outcome definition, precluding clear interpretations of meta-analysis estimates such as those reported in Lin et al. [93]. Further, our systematic study quality evaluation reveals that a large proportion of the studies to date are subject to multiple sources of bias and inaccuracy, primarily due to self-reported gas cooking exposure or respiratory outcomes, insufficient adjustment for key confounders (e.g., environmental tobacco smoke, family history of asthma or allergies, socioeconomic status or home environment), and unestablished temporality. We conclude that the epidemiology literature is limited by high heterogeneity and low study quality and, therefore, it does not provide sufficient evidence regarding causal relationships between gas cooking or indoor NO₂ and asthma or wheeze. We caution against over-interpreting the quantitative evidence synthesis estimates from meta-analyses of these studies.

This paper critically evaluates the Suppression Threshold Strategy (STS) for controlling Covid-19 (C-19). STS asserts a “fundamental distinction” between suppression and mitigation strategies, reflected in very different outcomes in eventual mortality depending on whether reproductive number R is caused to fall below 1. We show that there is no real distinction based on any value of R which falls in any case from early on in an epidemic wave. We show that actual mortality outcomes lay on a continuum, correlating with suppression levels, but not exhibiting any step changes or threshold effects. We argue that an excessive focus on achieving suppression at all costs, driven by the erroneous notion that suppression is a threshold, led to a lack of information on how to trade off the effects of different specific interventions. This led many countries to continue with inappropriate intervention-packages even after it became clear that their initial goal was not going to be attained. Future pandemic planning must support the design of “Plan B", which may be quite different from “Plan A".

Several recent news stories have alarmed many politicians and members of the public by reporting that indoor air pollution from gas stoves causes about 13% of childhood asthma in the United States. Research on the reproducibility and trustworthiness of epidemiological risk assessments has identified a number of common questionable research practices (QRPs) that should be avoided to draw sound causal conclusions from epidemiological data. Examples of such QRPs include claiming causation without using study designs or data analyses that allow valid causal inferences; generalizing or transporting risk estimates based on data for specific populations, time periods, and locations to different ones without accounting for differences in the study and target populations; claiming causation without discussing or quantitatively correcting for confounding, external validity bias, or other biases; and not mentioning or resolving contradictory evidence. We examine the recently estimated gas stove-childhood asthma associations from the perspective of these QRPs and conclude that it exemplifies all of them. The quantitative claim that about 13% of childhood asthma in the United States could be prevented by reducing exposure to gas stove pollution is not supported by the data collected or by the measures of association (Population Attributable Fractions) used to analyze the data. The qualitative finding that reducing exposure to gas stove pollution would reduce the burden of childhood asthma in the United States has no demonstrated validity. Systematically checking how and whether QRPs have been addressed before reporting or responding to claims that everyday exposures cause substantial harm to health might reduce social amplification of perceived risks based on QRPs and help to improve the credibility and trustworthiness of published epidemiological risk assessments.

Contact tracing is commonly recommended to control outbreaks of COVID-19, but its effectiveness is unclear. Following PRISMA guidelines, we searched four databases using a range of terms related to contact tracing effectiveness for COVID-19. We found 343 papers; 32 were included. All were observational or modelling studies. Observational studies (n = 14) provided consistent, very-low certainty evidence that contact tracing (alone or in combination with other interventions) was associated with better control of COVID-19 (e.g. in Hong Kong, only 1084 cases and four deaths were recorded in the first 4.5 months of the pandemic). Modelling studies (n = 18) provided consistent, high-certainty evidence that under assumptions of prompt and thorough tracing with effective quarantines, contact tracing could stop the spread of COVID-19 (e.g. by reducing the reproduction number from 2.2 to 0.57). A cautious interpretation indicates that to stop the spread of COVID-19, public health practitioners have 2–3 days from the time a new case develops symptoms to isolate the case and quarantine at least 80% of its contacts.

We present a Socratic dialogue with ChatGPT, a large language model (LLM), on the causal interpretation of epidemiological associations between fine particulate matter (PM2.5) and human mortality risks. ChatGPT, reflecting probable patterns of human reasoning and argumentation in the sources on which it has been trained, initially holds that “It is well-established that exposure to ambient levels of PM2.5 does increase mortality risk” and adds the unsolicited remark that “Reducing exposure to PM2.5 is an important public health priority.” After patient questioning, however, it concludes that “It is not known with certainty that current ambient levels of PM2.5 increase mortality risk. While there is strong evidence of an association between PM2.5 and mortality risk, the causal nature of this association remains uncertain due to the possibility of omitted confounders.” This revised evaluation of the evidence suggests the potential value of sustained questioning in refining and improving both the types of human reasoning and argumentation imitated by current LLMs and the reliability of the initial conclusions expressed by current LLMs.