Comment on “Mortality and Cause of Death in Hearing Loss Participants: A Longitudinal Follow-Up Study Using a National Sample Cohort”
Bibliographic record
Abstract
To the Editor: We recently reviewed the article published by Kim et al. (1) in the human communication epidemiology research group. The study aims to estimate the risk of mortality in subjects with severe and profound hearing loss according to the cause of death. The authors assume that changes in the auditory system lead to a greater risk of serious falls that lead to death (1). In studies in which the causal chain of diseases and illnesses are analyzed, depending on the basic epistemological and epidemiological framework, it is recommended that the links between the exposures and the outcome studied, as well as the variables related to exposure and outcome even if they are not the objective of the study, must be considered in the analysis models (2). Furthermore, considering that the planning of a study requires theoretical models defined a priori and based on the existing literature in addition to the definition of the statistical analyzes to be performed, including the covariables that will be inserted in the analysis models that will be performed reduces the probability that relevant variables are left out of the necessary adjustments for proper testing of the hypotheses on screen (3,4). In this case we refer mainly to confounding factors, since not considering these can, among others, lead researchers to find spurious associations (5). Literature research on the relationship between severe and profound hearing loss from the age of 40 and over and mortality rates opens a relevant range of possibilities already explored before (6–13) and not explored by the authors in the discussion or even in the fragilities of the study. Analyzing Table 1 of the manuscript, in which the authors present the characteristics of the studied population, there is a high proportion of subjects from the rural area in both groups of hearing impaired, severe and profound (59.7 and 65.1%, respectively) (1). The two studies previously carried out and cited by the authors (Genther et al., 2015) (14) and Karpa et al. (15) have lower hazard ratio and do not present information on the residence of their population (if rural or from the cities). We think that an important causal factor related to mortality and hearing loss in the researched population, exposure to pesticides and agrochemicals (16–18), was not considered by the authors in their analysis and in the discussion of the data. This is an important and current element of causation of health problems in death, as highlighted in the literature (19). Despite the fact that modern epidemiology demonstrates that health transposes the individual level of understanding of the health-disease process, it is also necessary to consider individual aspects such as the period of acquisition of hearing loss, whether acquired or congenital, labor aspects and exposure to noise and health-related aspects (5), such as self-perceived health (20), access to health services (21), and other associated psychological disorders (22,23), as these are related to both exposure and outcome under study and, although this information was not available for the study, it should be considered in the discussion of the findings. The discussion and conclusions are not fully supported by the presented methodology and results. Hearing is a complex system and the relationship between hearing loss and death is possibly not a direct cause. Thus, we highlight the importance of a critical and cautious reading of the data presented, especially in relation to its application in health policies and clinic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.034 | 0.035 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".