The growing rift between epidemiologists and their data

The Association Between Smoking Behavior and the Risk of Hypertension: Review of the Observational and Genetic Evidence

Background

Cigarette smoking is one of the world's largest avoidable risk factors for morbidity and mortality. Numerous studies have investigated the association between smoking and hypertension (HTN). Although observational data and cross-sectional research often exhibit a link between smoking and HTN, establishing causation remains challenging owing to potential confounding variables. Mendelian randomization (MR), a genetic epidemiological technique that employs genetic variants as instrumental variables, offers a more robust approach for evaluating causal links. This review aimed to explore both the observational and causal relationships between smoking and the risk of HTN.

Methodology

A comprehensive literature search across major electronic databases was conducted to identify relevant observational and Mendelian randomization (MR) studies on smoking and HTN risk. Various characteristics were included during study selection, such as study design, exposure assessment, and age range. Standardized processes were used for data retrieval and quality evaluation.

Results

Analysis of observational data revealed a paradoxical association between smoking and the risk of HTN, where a lower risk was observed among current smokers when compared to non-smokers. However, observational analysis also presented a dose-response effect with greater smoking intensity showed a modest linear increase in HTN risk, and older smoking initiation was associated with a slight increase in HTN risk (compared with younger). In contrast, MR-based causal estimates provide inconsistent evidence regarding the causal relationship between smoking behavior and HTN. Some MR analyses indicated a potential causal link between smoking and HTN; but this was not consistent.

Conclusion

Observational studies suggest a paradoxical association between smoking and HTN. However, MR studies do not provide sufficient evidence to establish a causal relationship. Regardless, lifestyle variables remain crucial for overall health. Healthcare professionals should regularly assess smoking status and provide counseling for quitting. Further research is needed to clarify the underlying processes, identify mediators, and evaluate the interventions.

Child maltreatment by non-accidental burns: interest of an algorithm of detection based on hospital discharge database

OBJECTIVES

To build a detection algorithm of non-accidental pediatric burns (NAB) using hospital resumes from the French Hospital Discharge Database (HDD) and to describe cases with no judicial or administrative report.

MATERIALS AND METHODS

Children aged 0-16 years old hospitalized at the University Hospital of Tours from 2012 to 2017 with a coded burn were included. "Probable" or "possible" HDD cases of NAB were defined based on the International Classification of Diseases 10th version codes during the inclusion stay or the previous year. A chart review was performed on all the HDD cases and HDD non cases matched on sex and age with a 1:2 ratio. Performance parameters were estimated for three clinical definitions of child maltreatment: excluding neglect, including neglect in a restrictive definition, and in a broad definition. For clinical cases, report to the judicial or administrative authorities was searched.

RESULTS

Among the 253 included children, 83 "probable" cases and 153 non-cases were analyzed. Sensitivity varied from 48 (_95%CI [36-60], excluding neglect) to 90% [55-100] and specificity from 70 [63;77] to 68% [61;74]. The proportion of clinical cases with no report without justification varied from 0 (excluding neglect) to > 85% (with the broadest definition); all corresponded to possible isolated neglect.

CONCLUSION

The performances of the algorithm varied tremendously according to the clinical definition of child maltreatment. Neglect is obviously complex and tough to clinically detect. Training for healthcare professionals and qualitative studies on obstacles to report should be added to this work.

Epidemiologists of the Future: Data Collectors or Scientists?

Epidemiology is the study of epidemics. It is a biological science that includes expertise in many disciplines in social and behavioral sciences. Epidemiology is also a key component of preventive medicine and public health. Unfortunately, over recent years, academic epidemiology has lost its relationship with preventive medicine, as well as much of its focus on epidemics. The new "-omics" technologies to measure risk factors and phenotypes, and advances in genomics (e.g., host susceptibility) consistent with good epidemiology methods will likely enhance epidemiology research. There is a need based on these new technologies to modify training, especially for the first-level doctorate epidemiologist.

The Diesel Exhaust in Miners Study provides no evidence for an increase in risk for lung cancer in miners exposed to diesel engine emissions

The Diesel Exhaust in Miners Study is unquestionably the most suitable data material to date to examine a possible link between diesel engine emissions and lung cancer risk. But the results do not appear to be consistent in themselves. The crucial methodological problem in this study, however, has yet to be discovered, to which the lack of any description of age related information (year of birth, year of hire, year of first exposure, year of death) for the cohort as well as for the cases might have contributed. This information is important to understand the flaws in the analysis. It turns out that the year of birth is associated with the exposure, i.e. with the chance to be exposed over a certain period of time as well as with the chance to be an ever-smoker. A further important issue for the interpretation of the results is the validity of the data on smoking, which are mainly obtained from next of kin for decedents up to 50 years after death. Taking all these aspects into account, it can be concluded that only the SMR-analysis can be considered from all published results.

Epidemiology in wonderland: Big Data and precision medicine

Big Data and precision medicine, two major contemporary challenges for epidemiology, are critically examined from two different angles. In Part 1 Big Data collected for research purposes (Big research Data) and Big Data used for research although collected for other primary purposes (Big secondary Data) are discussed in the light of the fundamental common requirement of data validity, prevailing over "bigness". Precision medicine is treated developing the key point that high relative risks are as a rule required to make a variable or combination of variables suitable for prediction of disease occurrence, outcome or response to treatment; the commercial proliferation of allegedly predictive tests of unknown or poor validity is commented. Part 2 proposes a "wise epidemiology" approach to: (a) choosing in a context imprinted by Big Data and precision medicine-epidemiological research projects actually relevant to population health, (b) training epidemiologists,