Global Epidemiology最新文献

The SARS-CoV-2 virus was first detected in December 2019, which prompted many researchers to investigate how the virus spreads. SARS-CoV-2 is mainly transmitted through respiratory droplets. Symptoms of the SARS-CoV-2 virus appear after an incubation period. Moreover, the asymptomatic infected individuals unknowingly spread the virus. Detecting infected people requires daily tests and contact tracing, which are expensive. The early detection of infectious diseases, including COVID-19, can be achieved with wastewater-based epidemiology, which is timely and cost-effective. In this study, we collected wastewater samples from wastewater treatment plants in several cities in North Dakota and then extracted viral RNA copies. We used log-RNA copies in the model to predict the number of infected cases using Quantile Regression (QR) and K-Nearest Neighbor (KNN) Regression. The model's performance was evaluated by comparing the Mean Absolute Percentage Error (MAPE). The QR model performs well in cities where the population is >$10000$. In addition, the model predictions were compared with the basic Susceptible-Infected-Recovered (SIR) model which is the golden standard model for infectious diseases.

Background

Incidence rates of thyroid cancer have increased. Recent studies findings suggest that women who underwent a hysterectomy have an elevated relative risk of thyroid cancer. The aim of our meta-analysis is to summarize the evidence about the association between hysterectomy and thyroid cancer risk.

Methods

PubMed, Web of Science, and Scopus database were searched for studies published up to 5 September 2023. The PRISMA statement was followed. Heterogeneity was explored with Q statistic and the I2 statistic. Publication bias was assessed with Begg's and Egger's tests.

Results

Sixteen studies met the criteria. The pooled analysis showed a significantly 64% increment of thyroid cancer risk in association with any hysterectomy (OR 1.64, 95% CI 1.48–1.81; I2 = 28.68%, p = 0.156). Hysterectomy without oophorectomy was a stronger predictor of risk than hysterectomy with oophorectomy. The pooled analysis of data regarding hysterectomy without oophorectomy showed a statistically significant increment of thyroid cancer risk by 59%. Hysterectomy with oophorectomy was associated with an increase of thyroid cancer risk of 39% (OR 1.39, 95% CI 1.16–1.67; I2 = 42.10%, p = 0.049). Significant publication bias was not detected.

Conclusions

Our findings help with decision making around these surgeries.

Pesticides are highly tested and regulated chemicals. There is currently great interest in the role that pesticides may play in childhood neurodevelopment. The objective was to identify and describe the body of evidence and to assess the ability to synthesize effect estimates. The epidemiologic literature from 2011 to 2022 was searched for publications on the association between pesticide exposure and neurodevelopment, behavior, and/or cognition in children. We identified 114 publications, representing 67 unique studies. While organochlorine and other insecticides were the most common classes of pesticides studied, up to 159 different metabolites or active ingredients were reported. Nine pesticides or their metabolites were reported in >10 publications. Similarly, multiple assessment methods were administered across studies to evaluate outcomes in neurodevelopment at ages which ranged from birth to 18 years of age. This scoping review reveals the heterogeneity among published studies with respect to exposures and health outcomes, in the methods used to assess and classify them, and in combinations of the two. This limits the adequacy of the evidence to evaluate specific risk estimates for a particular exposure-outcome pair. Intentional coordination among researchers to increase consistency in methodologies would facilitate the synthesis of results across studies. Research opportunities also exist to validate assumptions in exposure and outcome assessment which are implicit in many of the studies reviewed. In conclusion, there are many ongoing epidemiologic studies with a focus on pesticides and neurodevelopment. The variety of exposures, exposure assessment methods and tests for each outcome can be overwhelming. Interdisciplinary collaboration is recommended to harmonize data collection and to enable meaningful interpretation of the study results across populations.

Background

Causal directed acyclic graphs (cDAGs) are frequently used to identify confounding and collider bias. We demonstrate how to use causal directed acyclic graphs to adjust for collider bias in the hospitalized Covid-19 setting.

Materials and methods

According to the cDAGs, three types of modeling have been performed. In model 1, only vaccination is entered as an independent variable. In model 2, in addition to vaccination, age is entered the model to adjust for collider bias due to the conditioning of hospitalization. In model 3, comorbidities are also included for adjustment of collider bias due to the conditioning of hospitalization in different biasing paths intercepting age and comorbidities.

Results

There was no evidence of the effect of vaccination on preventing death due to Covid-19 in model 1. In the second model, where age was included as a covariate, a protective role for vaccination became evident. In model 3, after including chronic diseases as other covariates, the protective effect was slightly strengthened.

Conclusion

Studying hospitalized patients is subject to collider-stratification bias. Like confounding, this type of selection bias can be adjusted for by inclusion of the risk factors of the outcome which also affect hospitalization in the regression model.

Schools of public health often serve both as public health advocacy organizations and as academic units within a university. These two roles, however, can sometimes come into conflict. I experienced this conflict directly at the Harvard T. H. Chan School of Public Health in holding and expressing unpopular minority viewpoints on certain moral controversies. In this essay I describe my experiences and their relation to questions of academic freedom, population health promotion, and efforts at working together across differing moral systems.

Objectives

Identifying the trend of diseases and its changes over time can be highly important in evaluating the extent and method of achieving strategies for controlling them, developing health indicators, and health planning. This study aimed to investigate the incidence of tuberculosis.

Methods

As a repeated cross-sectional study in which the population under study was a census, this study involved all tuberculosis cases registered in 21 cities of Southwest of Iran, from 2010 to 2019. Data were obtained from the National System of Notification of Tuberculosis and included variables related to age, sex and Disease consequence. Segmented regression models were used to analyze the trend of tuberculosis changes. Also, data analysis software- Join Point Regression version 5.0.2 was used for data analysis.

Results

The results of evaluating the trend of tuberculosis from 2010 to 2019 showed no change in the general trend of tuberculosis and an annual 0.84% (95% CI: ‐5.17 to 6.82) increase in incidence rate is observed in the trend. Also, the findings of join point regression analysis show that between 2010 and 2013, an annual 18.10% (95% CI: 8.78 to 34.89) increase in the incidence of tuberculosis, and between 2013 and 2019, annual −5.42% (95% CI: −10.04 to −2.22) decrease in the incidence of tuberculosis was observed. From 2010 to 2012, a 33.10% (95% CI: 15.77 to 48.06) annual increase in the incidence of tuberculosis in males and − 9.47% (95%CI: −14.02 to −6.33) annual decrease in the incidence of tuberculosis in females was observed.

Conclusions

The results of this study showed that the incidence of tuberculosis had an upward trend from 2010 to 2013 and a downward trend from 2013 onwards.

Aspartame is a dipeptide non-sugar sweetener that was first marketed in the US in carbonated beverages in 1983, before gaining prominence globally. The Joint Food and Agriculture Organization of the United Nations (FAO)/World Health Organization (WHO) Expert Committee on Food Additives (JECFA) and the WHO International Agency for Research on Cancer (IARC) completed evaluations of aspartame and cancer in July 2023. JECFA reaffirmed the safety of aspartame, stating that epidemiology evidence is “not convincing,” and that there are no consistent associations between aspartame and cancer (JECFA/IARC, 2023; JECFA, 2023). JECFA also noted “reverse causality, chance, bias and confounding by socioeconomic or lifestyle factors, or consumption of other dietary components, could not be completely ruled out” in relevant epidemiology studies (JECFA/IARC, 2023). In contrast, IARC stated that there are three “high quality” studies on liver cancer (Riboli, 2023), but that the evidence is limited because “chance, bias or confounding could not be ruled out as an explanation for the positive findings” (JECFA/IARC, 2023). IARC does not provide an explanation as to how these studies can be both high quality and have these weaknesses, most notably potential exposure misclassification, or how inconsistent associations from studies with these weaknesses constitute limited evidence. Further, when IARC concludes an agent has limited or inadequate human evidence (and no sufficient animal or strong mechanistic evidence), it classifies that agent as either Group 2B, a possible human carcinogen, or Group 3, not classifiable as to its carcinogenicity. Ultimately, the interpretations of Group 2B and Group 3 classifications are intended to be similar. However, a Group 2B designation may make it appear to scientists and non-scientists alike that the evidence is pointing in the direction of causality. This can lead to unnecessary confusion with respect to the evidence, as well as a perception of a disagreement within WHO regarding aspartame. This apparent contradiction could have been avoided by assigning the IARC classification most consistent with the conclusion that the human evidence for cancer is inadequate: Group 3.

Background

COVID-19 is associated with severe pneumonia lung damage, acute respiratory distress syndrome (ARDS), and mortality. In this study, we aimed to compare corticosteroids' effect on the mortality risk in patients hospitalized with COVID-19.

Methods

PubMed, Web of Science, Scopus, Cochrane Library, and Embase, were searched using a predesigned search strategy. Randomized controlled trials (RCTs) that had compared the corticosteroid drugs were included. The hazard ratio (HR) with a 95% confidence interval (CI) was used to summarize the effect size from the network meta-analysis (NMA).

Results

Out of 329 retrieved references, 12 RCTs with 11,455 participants met the eligibility criteria in this review. The included RCTs formed one network with six treatments. In addition, five treatments in two RCTs were not connected to the network. Methylprednisolone + usual care (UC) versus UC decreased the risk of death by 0.65 (95% CI: 0.47, 0.90). Among treatments in the network the highest P-score (0.89) was related to Methylprednisolone + UC.

Conclusion

Based on the results of this NMA it seems Methylprednisolone + UC to be the best treatment option in patients with COVID-ARDS and COVID pneumonia.

Exposure-response curves are among the most widely used tools of quantitative health risk assessment. However, we propose that exactly what they mean is usually left ambiguous, making it impossible to answer such fundamental questions as whether and by how much reducing exposure by a stated amount would change average population risks and distributions of individual risks. Recent concepts and computational methods from causal artificial intelligence (CAI) and machine learning (ML) can be applied to clarify what an exposure-response curve means; what other variables are held fixed (and at what levels) in estimating it; and how much inter-individual variability there is around population average exposure-response curves. These advances in conceptual clarity and practical computational methods not only enable epidemiologists and risk analysis practitioners to better quantify population and individual exposure-response curves but also challenge them to specify exactly what exposure-response relationships they seek to quantify and communicate to risk managers and how to use the resulting information to improve risk management decisions.

Background

This study aimed to assess medication adherence, glycemic control, and their influencing factors among outpatients at an Indonesian clinic with type 2 diabetes.

Methods

A cross-sectional study was conducted among patients with type 2 diabetes at a hospital-based clinic in Surabaya, Indonesia, from September to December 2018. A purposive sampling was used; patients aged 18 years and older, had diabetes and any comorbidity, received hypoglycemic agents, and provided written informed consent were included. The previously validated Brief Medication Questionnaire was used to measure medication adherence, while glycosylated hemoglobin (A1C) levels were used to evaluate glycemic control. Binary logistic regression was used to identify factors associated with medication adherence and glycemic control.

Results

Of 321 patients enrolled in the study, 268 (83.5%) patients were medication nonadherent. Patients who did not engage regularly in physical activity (aOR: 0.49, 95% CI: 0.26–0.93) was more likely to be medication adherent. Poor glycemic control (A1C: >7%) was observed in 106 (33.0%) of the patients. Patients who used a combination of oral hypoglycemic agents and insulin (aOR: 2.74, 95% CI: 1.09–6.86), did not take biguanide (aOR: 2.73, 95% CI: 1.16–6.43), reported hyperglycemia (aOR: 4.24, 95% CI: 1.53–11.81), and had comorbid diseases (aOR: 4.33, 95% CI: 1.08–17.34) increased the risk of having poor glycemic control. Patients who were more likely to achieve good glycemic control were male (aOR: 0.39, 95% CI: 0.20–0.74) and aged older (aOR: 0.95, 95% CI: 0.92–0.99).

Conclusions

The proportion of patients who were medication nonadherent was much higher than those with poor glycemic control. Whereas regular exercise was a predictor of nonadherence, age, sex, diabetes medication, not taking biguanide, acute complications, and comorbidity were predictors of poor glycemic control. Therefore, strategies are needed to improve medication adherence and glycemic control.